samples/zeppelin-notebooks/2BCHR4T1Q/note.json

{
  "paragraphs": [
    {
      "title": "Let us first read the 311 complaint data for new york city from hDFS",
      "text": "val nyc_sdff \u003d \nsqlContext.read.format(\"com.databricks.spark.csv\").option(\"header\",\"true\").load(\"hdfs://localhost:9000/datasets/nyc-311-service/311_Service_Requests_from_2010_to_Present.csv\").cache()",
      "dateUpdated": "Feb 11, 2016 7:46:52 PM",
      "config": {
        "colWidth": 12.0,
        "editorMode": "ace/mode/scala",
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        },
        "title": true
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1454426714926_618059279",
      "id": "20160202-152514_1906486717",
      "result": {
        "code": "SUCCESS",
        "type": "TEXT",
        "msg": "nyc_sdff: org.apache.spark.sql.DataFrame \u003d [Unique Key: string, Created Date: string, Closed Date: string, Agency: string, Agency Name: string, Complaint Type: string, Descriptor: string, Location Type: string, Incident Zip: string, Incident Address: string, Street Name: string, Cross Street 1: string, Cross Street 2: string, Intersection Street 1: string, Intersection Street 2: string, Address Type: string, City: string, Landmark: string, Facility Type: string, Status: string, Due Date: string, Resolution Description: string, Resolution Action Updated Date: string, Community Board: string, Borough: string, X Coordinate (State Plane): string, Y Coordinate (State Plane): string, Park Facility Name: string, Park Borough: string, School Name: string, School Number: string, School Region: s..."
      },
      "dateCreated": "Feb 2, 2016 3:25:14 PM",
      "dateStarted": "Feb 11, 2016 7:46:56 PM",
      "dateFinished": "Feb 11, 2016 7:47:39 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Create a Temporary Spark Table out of the Spark DataFrame",
      "text": "nyc_sdff.registerTempTable(\"nyc311f\")",
      "dateUpdated": "Feb 11, 2016 7:51:07 PM",
      "config": {
        "colWidth": 12.0,
        "editorMode": "ace/mode/scala",
        "title": true,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        }
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1445353951173_-79877865",
      "id": "20151020-151231_921365907",
      "result": {
        "code": "SUCCESS",
        "type": "TEXT",
        "msg": ""
      },
      "dateCreated": "Oct 20, 2015 3:12:31 PM",
      "dateStarted": "Feb 11, 2016 7:51:11 PM",
      "dateFinished": "Feb 11, 2016 7:51:12 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Total 311 Complaints in New York City In last 1 year",
      "text": "%sql select count(*) as TotalComplaintCountFrom2015 from nyc311f",
      "dateUpdated": "Feb 11, 2016 7:51:19 PM",
      "config": {
        "colWidth": 12.0,
        "graph": {
          "mode": "table",
          "height": 95.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        },
        "editorMode": "ace/mode/sql",
        "title": true
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1454428061459_102213864",
      "id": "20160202-154741_356355521",
      "result": {
        "code": "SUCCESS",
        "type": "TABLE",
        "msg": "TotalComplaintCountFrom2015\n1652212\n"
      },
      "dateCreated": "Feb 2, 2016 3:47:41 PM",
      "dateStarted": "Feb 11, 2016 7:51:21 PM",
      "dateFinished": "Feb 11, 2016 7:51:40 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Let us investigate some relevant columns in 311 data",
      "text": "%sql select `Unique Key` as Unique_key, `Created Date` as Created_Date, `Complaint Type` as Complaint_Type, Borough, `Incident Address` as Incident_Address, `Incident Zip` as Incident_Zip from nyc311f",
      "dateUpdated": "Feb 11, 2016 3:30:45 PM",
      "config": {
        "colWidth": 12.0,
        "graph": {
          "mode": "table",
          "height": 706.0,
          "optionOpen": false,
          "keys": [
            {
              "name": "Unique_key",
              "index": 0.0,
              "aggr": "sum"
            }
          ],
          "values": [
            {
              "name": "Created_Date",
              "index": 1.0,
              "aggr": "sum"
            }
          ],
          "groups": [],
          "scatter": {
            "xAxis": {
              "name": "Unique_key",
              "index": 0.0,
              "aggr": "sum"
            },
            "yAxis": {
              "name": "Created_Date",
              "index": 1.0,
              "aggr": "sum"
            }
          }
        },
        "editorMode": "ace/mode/sql",
        "title": true
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1454428593942_-298330523",
      "id": "20160202-155633_173828066",
      "result": {
        "code": "SUCCESS",
        "type": "TABLE",
        "msg": "Unique_key\tCreated_Date\tComplaint_Type\tBorough\tIncident_Address\tIncident_Zip\n32391115\t01/12/2016 02:13:47 AM\tBenefit Card Replacement\tUnspecified\t\t\n32390536\t01/12/2016 02:13:44 AM\tDPR Internal\tMANHATTAN\t\t10007\n32393425\t01/12/2016 02:01:54 AM\tBlocked Driveway\tQUEENS\t45-15 ROBINSON STREET\t11355\n32391698\t01/12/2016 02:00:13 AM\tRodent\tMANHATTAN\t232 EAST 88 STREET\t10128\n32390163\t01/12/2016 01:52:10 AM\tNoise - Street/Sidewalk\tBRONX\t\t10465\n32388785\t01/12/2016 01:47:28 AM\tBlocked Driveway\tQUEENS\t110-49 107 STREET\t11417\n32394554\t01/12/2016 01:40:32 AM\tBlocked Driveway\tQUEENS\t76 STREET\t11416\n32389895\t01/12/2016 01:39:06 AM\tBlocked Driveway\tBRONX\t1317 WHEELER AVENUE\t10472\n32386676\t01/12/2016 01:34:04 AM\tOpinion for the Mayor\tUnspecified\t\t\n32394539\t01/12/2016 01:29:47 AM\tBlocked Driveway\tBRONX\t4344 GUNTHER AVENUE\t10466\n32392200\t01/12/2016 01:27:58 AM\tBenefit Card Replacement\tUnspecified\t\t\n32387271\t01/12/2016 01:20:32 AM\tStreet Sign - Damaged\tQUEENS\t\t11420\n32394248\t01/12/2016 01:18:30 AM\tNoise - Street/Sidewalk\tBROOKLYN\t5514 18 AVENUE\t11204\n32391293\t01/12/2016 01:13:29 AM\tOpinion for the Mayor\tUnspecified\t\t\n32389391\t01/12/2016 01:12:23 AM\tIllegal Parking\tQUEENS\t29-08 147 STREET\t11354\n32388639\t01/12/2016 01:08:12 AM\tNoise - Vehicle\tBROOKLYN\t163 LAFAYETTE AVENUE\t11238\n32390508\t01/12/2016 01:08:08 AM\tBlocked Driveway\tBROOKLYN\t353 WINTHROP STREET\t11225\n32389928\t01/12/2016 01:02:42 AM\tBlocked Driveway\tBROOKLYN\t562 EAST 9 STREET\t11218\n32392869\t01/12/2016 01:02:23 AM\tIllegal Parking\tMANHATTAN\t417 EAST 72 STREET\t10021\n32394219\t01/12/2016 01:00:46 AM\tDamaged Tree\tBROOKLYN\t2271 78 STREET\t11214\n32391741\t01/12/2016 01:00:17 AM\tHomeless Person Assistance\tMANHATTAN\t712 5 AVENUE\t10019\n32387046\t01/12/2016 01:00:11 AM\tIllegal Parking\tBRONX\t1621 UNDERCLIFF AVENUE\t10453\n32386077\t01/12/2016 12:58:56 AM\tFor Hire Vehicle Complaint\tMANHATTAN\t228 WEST 47 STREET\t10036\n32393390\t01/12/2016 12:56:04 AM\tBlocked Driveway\tQUEENS\t35-11 105 STREET\t11368\n32388446\t01/12/2016 12:56:00 AM\tStreet Light Condition\tBRONX\t1230 MORRISON AVENUE\t10472\n32390525\t01/12/2016 12:55:39 AM\tIllegal Parking\tBROOKLYN\t175 SPENCER STREET\t11205\n32394572\t01/12/2016 12:53:05 AM\tBlocked Driveway\tQUEENS\t84-16 133 AVENUE\t11417\n32387536\t01/12/2016 12:50:40 AM\tBlocked Driveway\tBROOKLYN\t793 MONTGOMERY STREET\t11213\n32391751\t01/12/2016 12:49:18 AM\tNoise - Vehicle\tBROOKLYN\tEAST 59 STREET\t11234\n32389331\t01/12/2016 12:46:57 AM\tIllegal Parking\tQUEENS\t132-15 SANFORD AVENUE\t11355\n32388230\t01/12/2016 12:44:56 AM\tIllegal Parking\tQUEENS\t133-45 41 ROAD\t11355\n32394208\t01/12/2016 12:42:47 AM\tNoise - Commercial\tQUEENS\t90-11 31 AVENUE\t11369\n32386687\t01/12/2016 12:41:37 AM\tOpinion for the Mayor\tUnspecified\t\t\n32390201\t01/12/2016 12:39:00 AM\tStreet Light Condition\tBRONX\t\t10472\n32386963\t01/12/2016 12:37:46 AM\tBlocked Driveway\tBRONX\t1831 BILLINGSLEY TERRACE\t10453\n32392925\t01/12/2016 12:35:04 AM\tBlocked Driveway\tBROOKLYN\t739 54 STREET\t11220\n32388171\t01/12/2016 12:32:15 AM\tIllegal Parking\tBROOKLYN\t1961 74 STREET\t11204\n32391760\t01/12/2016 12:31:38 AM\tIllegal Parking\tBROOKLYN\tROCKAWAY PARKWAY\t11236\n32394307\t01/12/2016 12:31:37 AM\tTaxi Report\tMANHATTAN\t\t10036\n32387571\t01/12/2016 12:30:42 AM\tBlocked Driveway\tQUEENS\t26-25 12 STREET\t11102\n32394921\t01/12/2016 12:29:06 AM\tGraffiti\tBROOKLYN\t2128 ROCKAWAY PARKWAY\t11236\n32386398\t01/12/2016 12:29:00 AM\tTraffic Signal Condition\tBROOKLYN\t\t\n32394547\t01/12/2016 12:28:00 AM\tTraffic Signal Condition\tBROOKLYN\t\t\n32387619\t01/12/2016 12:23:59 AM\tBlocked Driveway\tQUEENS\t45-15 ROBINSON STREET\t11355\n32387768\t01/12/2016 12:23:27 AM\tCity Vehicle Placard Complaint\tBROOKLYN\t1961 74 STREET\t11204\n32389400\t01/12/2016 12:23:07 AM\tNoise - Commercial\tMANHATTAN\t302 2 AVENUE\t10003\n32389399\t01/12/2016 12:20:31 AM\tNoise - Commercial\tMANHATTAN\t247 DYCKMAN STREET\t10034\n32390535\t01/12/2016 12:20:18 AM\tNoise - Commercial\tQUEENS\t76-19 ROOSEVELT AVENUE\t11372\n32386475\t01/12/2016 12:13:50 AM\tBlocked Driveway\tQUEENS\t141-17 181 STREET\t11413\n32387148\t01/12/2016 12:12:16 AM\tHomeless Encampment\tMANHATTAN\t121 EAST 3 STREET\t10009\n32389540\t01/12/2016 12:11:49 AM\tNoise - Commercial\tQUEENS\t90-11 31 AVENUE\t11369\n32390528\t01/12/2016 12:06:06 AM\tIllegal Parking\tSTATEN ISLAND\t\t10309\n32386319\t01/12/2016 12:06:01 AM\tFor Hire Vehicle Complaint\tMANHATTAN\t601 LEXINGTON AVENUE\t10022\n32387129\t01/12/2016 12:05:00 AM\tIndoor Air Quality\tMANHATTAN\t22 EAST 105 STREET\t10029\n32387055\t01/12/2016 12:05:00 AM\tTraffic Signal Condition\tBROOKLYN\t\t\n32394813\t01/12/2016 12:04:11 AM\tHEAT/HOT WATER\tBRONX\t2505 UNIVERSITY AVENUE\t10468\n32392856\t01/12/2016 12:03:37 AM\tBlocked Driveway\tBROOKLYN\t3475 ATLANTIC AVENUE\t11208\n32387112\t01/12/2016 12:03:36 AM\tNoise - Commercial\tMANHATTAN\t859 9 AVENUE\t10019\n32386130\t01/12/2016 12:03:14 AM\tNoise - Commercial\tBROOKLYN\t\t11235\n32391204\t01/12/2016 12:01:57 AM\tFLOORING/STAIRS\tMANHATTAN\t654 WEST  161 STREET\t10032\n32392420\t01/12/2016 12:01:57 AM\tGENERAL\tMANHATTAN\t654 WEST  161 STREET\t10032\n32387328\t01/12/2016 12:01:57 AM\tWATER LEAK\tMANHATTAN\t654 WEST  161 STREET\t10032\n32395089\t01/12/2016 12:01:57 AM\tPAINT/PLASTER\tMANHATTAN\t654 WEST  161 STREET\t10032\n32391818\t01/12/2016 12:01:57 AM\tAPPLIANCE\tMANHATTAN\t654 WEST  161 STREET\t10032\n32391978\t01/12/2016 12:01:57 AM\tUNSANITARY CONDITION\tMANHATTAN\t654 WEST  161 STREET\t10032\n32391041\t01/12/2016 12:01:57 AM\tAPPLIANCE\tMANHATTAN\t654 WEST  161 STREET\t10032\n32391116\t01/12/2016 12:01:57 AM\tELECTRIC\tMANHATTAN\t654 WEST  161 STREET\t10032\n32391143\t01/12/2016 12:01:57 AM\tDOOR/WINDOW\tMANHATTAN\t654 WEST  161 STREET\t10032\n32394958\t01/12/2016 12:01:47 AM\tHEAT/HOT WATER\tMANHATTAN\t330 WEST   55 STREET\t10019\n32390128\t01/12/2016 12:01:45 AM\tNoise - Vehicle\tBROOKLYN\t239 26 STREET\t11232\n32390213\t01/12/2016 12:00:44 AM\tHEAT/HOT WATER\tMANHATTAN\t51 EAST  129 STREET\t10035\n32394090\t01/11/2016 11:58:43 PM\tIllegal Parking\tQUEENS\t\t11374\n32393053\t01/11/2016 11:58:00 PM\tNoise\tQUEENS\t\t11694\n32394853\t01/11/2016 11:57:58 PM\tHEAT/HOT WATER\tBROOKLYN\t289 DIVISION AVENUE\t11211\n32392827\t01/11/2016 11:57:39 PM\tPLUMBING\tBROOKLYN\t310 EAST   25 STREET\t11226\n32389638\t01/11/2016 11:57:39 PM\tUNSANITARY CONDITION\tBROOKLYN\t310 EAST   25 STREET\t11226\n32395097\t01/11/2016 11:57:38 PM\tPLUMBING\tBROOKLYN\t310 EAST   25 STREET\t11226\n32388139\t01/11/2016 11:57:38 PM\tPAINT/PLASTER\tBROOKLYN\t310 EAST   25 STREET\t11226\n32390070\t01/11/2016 11:57:38 PM\tFLOORING/STAIRS\tBROOKLYN\t310 EAST   25 STREET\t11226\n32390410\t01/11/2016 11:57:38 PM\tPAINT/PLASTER\tBROOKLYN\t310 EAST   25 STREET\t11226\n32390071\t01/11/2016 11:57:38 PM\tFLOORING/STAIRS\tBROOKLYN\t310 EAST   25 STREET\t11226\n32389936\t01/11/2016 11:57:38 PM\tDOOR/WINDOW\tBROOKLYN\t310 EAST   25 STREET\t11226\n32389280\t01/11/2016 11:57:38 PM\tPLUMBING\tBROOKLYN\t310 EAST   25 STREET\t11226\n32391626\t01/11/2016 11:57:38 PM\tPLUMBING\tBROOKLYN\t310 EAST   25 STREET\t11226\n32395018\t01/11/2016 11:57:37 PM\tHEAT/HOT WATER\tMANHATTAN\t60 SEAMAN AVENUE\t10034\n32386714\t01/11/2016 11:57:33 PM\tHEAT/HOT WATER\tBROOKLYN\t118 COOPER STREET\t11207\n32392681\t01/11/2016 11:57:31 PM\tHEAT/HOT WATER\tMANHATTAN\t307 WEST   39 STREET\t10018\n32393699\t01/11/2016 11:57:25 PM\tHEAT/HOT WATER\tBRONX\t814 EAST  168 STREET\t10459\n32386097\t01/11/2016 11:57:00 PM\tStreet Light Condition\tQUEENS\t113-23 209 STREET\t11429\n32388397\t01/11/2016 11:57:00 PM\tNoise\tMANHATTAN\t419 EAST   66 STREET\t10065\n32386658\t01/11/2016 11:56:59 PM\tNoise - Vehicle\tMANHATTAN\t79 RIVINGTON STREET\t10002\n32389384\t01/11/2016 11:56:55 PM\tIllegal Parking\tMANHATTAN\t334 EAST 73 STREET\t10021\n32390042\t01/11/2016 11:56:25 PM\tConsumer Complaint\tSTATEN ISLAND\tRICHMOND ROAD\t\n32387846\t01/11/2016 11:55:49 PM\tNoise - Vehicle\tBROOKLYN\t619 WASHINGTON AVENUE\t11238\n32394820\t01/11/2016 11:55:09 PM\tHEAT/HOT WATER\tBROOKLYN\t5429 KINGS HIGHWAY\t11203\n32387310\t01/11/2016 11:55:00 PM\tWater System\tBROOKLYN\t80 DEKALB AVENUE\t11201\n32389161\t01/11/2016 11:54:52 PM\tHEAT/HOT WATER\tBROOKLYN\t381 FRANKLIN AVENUE\t11238\n32394924\t01/11/2016 11:54:41 PM\tHEAT/HOT WATER\tBRONX\t2074 WALLACE AVENUE\t10462\n32391661\t01/11/2016 11:54:30 PM\tIllegal Parking\tBROOKLYN\t2035 BROWN STREET\t11229\n32393637\t01/11/2016 11:54:19 PM\tStreet Condition\tMANHATTAN\t79 RIVINGTON STREET\t10002\n32393096\t01/11/2016 11:54:00 PM\tNoise\tMANHATTAN\t79 RIVINGTON STREET\t10002\n32389389\t01/11/2016 11:53:59 PM\tIllegal Parking\tQUEENS\t\t11378\n32388322\t01/11/2016 11:53:53 PM\tNoise - Commercial\tMANHATTAN\t102 NORFOLK STREET\t10002\n32394789\t01/11/2016 11:53:51 PM\tNoise - Commercial\tMANHATTAN\t249 ELDRIDGE STREET\t10002\n32394050\t01/11/2016 11:53:09 PM\tIllegal Parking\tMANHATTAN\t334 EAST 73 STREET\t10021\n32387245\t01/11/2016 11:53:00 PM\tNoise\tMANHATTAN\tEAST   31 STREET\t10016\n32392644\t01/11/2016 11:52:11 PM\tHEAT/HOT WATER\tBROOKLYN\t490 NEW YORK AVENUE\t11225\n32394969\t01/11/2016 11:52:07 PM\tHEAT/HOT WATER\tBROOKLYN\t1230 AVENUE X\t11235\n32386578\t01/11/2016 11:52:06 PM\tTaxi Complaint\tMANHATTAN\t100 FIFTH AVENUE\t10011\n32388331\t01/11/2016 11:51:40 PM\tRodent\tBROOKLYN\t879 DEKALB AVENUE\t11206\n32392811\t01/11/2016 11:51:01 PM\tRodent\tBROOKLYN\t1485 SHORE PARKWAY\t11214\n32393752\t01/11/2016 11:51:00 PM\tStreet Light Condition\tMANHATTAN\t\t\n32391347\t01/11/2016 11:50:50 PM\tHEAT/HOT WATER\tBRONX\t1318 MANOR AVENUE\t10472\n32392753\t01/11/2016 11:49:52 PM\tPAINT/PLASTER\tBROOKLYN\t1005 LINCOLN PLACE\t11213\n32389617\t01/11/2016 11:49:51 PM\tUNSANITARY CONDITION\tBROOKLYN\t1005 LINCOLN PLACE\t11213\n32387721\t01/11/2016 11:49:51 PM\tELECTRIC\tBROOKLYN\t1005 LINCOLN PLACE\t11213\n32395081\t01/11/2016 11:49:51 PM\tPAINT/PLASTER\tBROOKLYN\t1005 LINCOLN PLACE\t11213\n32386480\t01/11/2016 11:49:51 PM\tELECTRIC\tBROOKLYN\t1005 LINCOLN PLACE\t11213\n32392296\t01/11/2016 11:49:51 PM\tELECTRIC\tBROOKLYN\t1005 LINCOLN PLACE\t11213\n32395064\t01/11/2016 11:49:51 PM\tPAINT/PLASTER\tBROOKLYN\t1005 LINCOLN PLACE\t11213\n32389262\t01/11/2016 11:49:51 PM\tPAINT/PLASTER\tBROOKLYN\t1005 LINCOLN PLACE\t11213\n32390059\t01/11/2016 11:49:51 PM\tFLOORING/STAIRS\tBROOKLYN\t1005 LINCOLN PLACE\t11213\n32388119\t01/11/2016 11:49:51 PM\tPAINT/PLASTER\tBROOKLYN\t1005 LINCOLN PLACE\t11213\n32393918\t01/11/2016 11:49:49 PM\tHEAT/HOT WATER\tBROOKLYN\t11 MAPLE STREET\t11225\n32394232\t01/11/2016 11:49:00 PM\tNoise\tMANHATTAN\t86 9 AVENUE\t10019\n32392054\t01/11/2016 11:49:00 PM\tDirty Conditions\tBROOKLYN\t8753 25 AVENUE\t11214\n32393700\t01/11/2016 11:48:13 PM\tHEAT/HOT WATER\tQUEENS\t31-35 CRESCENT STREET\t11106\n32391025\t01/11/2016 11:47:54 PM\tBenefit Card Replacement\tUnspecified\t\t\n32392524\t01/11/2016 11:47:45 PM\tHEAT/HOT WATER\tMANHATTAN\t1423 AMSTERDAM AVENUE\t10027\n32389080\t01/11/2016 11:46:37 PM\tHEAT/HOT WATER\tMANHATTAN\t775 RIVERSIDE DRIVE\t10032\n32393094\t01/11/2016 11:46:00 PM\tNoise\tMANHATTAN\t210 EAST   31 STREET\t10016\n32387105\t01/11/2016 11:44:39 PM\tNoise - Commercial\tBROOKLYN\t228 MANHATTAN AVENUE\t11206\n32395025\t01/11/2016 11:44:39 PM\tHEAT/HOT WATER\tMANHATTAN\t60 SEAMAN AVENUE\t10034\n32388030\t01/11/2016 11:44:30 PM\tHEAT/HOT WATER\tBROOKLYN\t8851 20 AVENUE\t11214\n32395005\t01/11/2016 11:44:24 PM\tHEAT/HOT WATER\tBRONX\t2090 EAST TREMONT AVENUE\t10462\n32390527\t01/11/2016 11:44:19 PM\tNoise - Commercial\tQUEENS\t32-10 37 AVENUE\t11101\n32389078\t01/11/2016 11:44:00 PM\tStreet Light Condition\tBROOKLYN\t\t11216\n32387122\t01/11/2016 11:43:26 PM\tFor Hire Vehicle Complaint\tUnspecified\tJOHN F KENNEDY AIRPORT\t11430\n32386824\t01/11/2016 11:43:24 PM\tHEAT/HOT WATER\tQUEENS\t80-25 PARSONS BOULEVARD\t11432\n32387036\t01/11/2016 11:42:46 PM\tIllegal Parking\tMANHATTAN\t417 EAST 72 STREET\t10021\n32386705\t01/11/2016 11:42:39 PM\tHEAT/HOT WATER\tBRONX\t2028 VALENTINE AVENUE\t10457\n32390788\t01/11/2016 11:42:39 PM\tUNSANITARY CONDITION\tBRONX\t2028 VALENTINE AVENUE\t10457\n32393515\t01/11/2016 11:42:39 PM\tELECTRIC\tBRONX\t2028 VALENTINE AVENUE\t10457\n32391251\t01/11/2016 11:42:39 PM\tGENERAL\tBRONX\t2028 VALENTINE AVENUE\t10457\n32393537\t01/11/2016 11:42:39 PM\tELECTRIC\tBRONX\t2028 VALENTINE AVENUE\t10457\n32391445\t01/11/2016 11:42:35 PM\tHEAT/HOT WATER\tBROOKLYN\t8851 20 AVENUE\t11214\n32386393\t01/11/2016 11:42:21 PM\tBlocked Driveway\tQUEENS\t90-10 69 AVENUE\t11375\n32387217\t01/11/2016 11:40:01 PM\tNoise - Street/Sidewalk\tMANHATTAN\t\t10011\n32391872\t01/11/2016 11:40:00 PM\tNoise\tMANHATTAN\t350 9 AVENUE\t10001\n32390026\t01/11/2016 11:38:54 PM\tFLOORING/STAIRS\tBROOKLYN\t354 EAST   53 STREET\t11203\n32392345\t01/11/2016 11:38:54 PM\tFLOORING/STAIRS\tBROOKLYN\t354 EAST   53 STREET\t11203\n32389670\t01/11/2016 11:38:54 PM\tUNSANITARY CONDITION\tBROOKLYN\t354 EAST   53 STREET\t11203\n32393583\t01/11/2016 11:38:54 PM\tFLOORING/STAIRS\tBROOKLYN\t354 EAST   53 STREET\t11203\n32391979\t01/11/2016 11:38:54 PM\tUNSANITARY CONDITION\tBROOKLYN\t354 EAST   53 STREET\t11203\n32388138\t01/11/2016 11:38:53 PM\tPLUMBING\tBROOKLYN\t354 EAST   53 STREET\t11203\n32389032\t01/11/2016 11:38:39 PM\tHEAT/HOT WATER\tBRONX\t2180 WALLACE AVENUE\t10462\n32389159\t01/11/2016 11:38:29 PM\tHEAT/HOT WATER\tMANHATTAN\t1627 AMSTERDAM AVENUE\t10031\n32392768\t01/11/2016 11:38:29 PM\tHEAT/HOT WATER\tMANHATTAN\t1627 AMSTERDAM AVENUE\t10031\n32386906\t01/11/2016 11:38:29 PM\tHEAT/HOT WATER\tMANHATTAN\t1627 AMSTERDAM AVENUE\t10031\n32389022\t01/11/2016 11:38:23 PM\tHEAT/HOT WATER\tBRONX\t1755 CLAY AVENUE\t10457\n32391493\t01/11/2016 11:38:19 PM\tHEAT/HOT WATER\tBROOKLYN\t437 KINGSTON AVENUE\t11225\n32387398\t01/11/2016 11:37:38 PM\tNoise - Commercial\tMANHATTAN\t2271 ADAM CLAYTON POWELL BOULEVARD\t10030\n32390532\t01/11/2016 11:37:27 PM\tIllegal Parking\tBROOKLYN\t\t11235\n32389588\t01/11/2016 11:37:05 PM\tPLUMBING\tBROOKLYN\t1013 AVENUE J\t11230\n32386787\t01/11/2016 11:36:17 PM\tHEAT/HOT WATER\tBRONX\t1326 COMMONWEALTH AVENUE\t10472\n32389777\t01/11/2016 11:36:00 PM\tDirty Conditions\tQUEENS\t90-13 69 AVENUE\t11375\n32386089\t01/11/2016 11:36:00 PM\tNoise\tMANHATTAN\t\t10002\n32388338\t01/11/2016 11:35:00 PM\tNoise\tMANHATTAN\t609 WEST  176 STREET\t10033\n32393861\t01/11/2016 11:34:57 PM\tHEAT/HOT WATER\tBROOKLYN\t30 3 AVENUE\t11217\n32391933\t01/11/2016 11:34:00 PM\tNoise\tMANHATTAN\t134 ORCHARD STREET\t10002\n32386864\t01/11/2016 11:33:54 PM\tHEAT/HOT WATER\tMANHATTAN\t51 EAST  129 STREET\t10035\n32387887\t01/11/2016 11:33:48 PM\tHEAT/HOT WATER\tBRONX\t4010 LOWERRE PLACE\t10466\n32391330\t01/11/2016 11:32:57 PM\tHEAT/HOT WATER\tBRONX\t75 WEST MOSHOLU PARKWAY NORTH\t10467\n32393825\t01/11/2016 11:32:56 PM\tHEAT/HOT WATER\tBRONX\t2319 BELMONT AVENUE\t10458\n32393920\t01/11/2016 11:32:53 PM\tHEAT/HOT WATER\tMANHATTAN\t51 EAST  129 STREET\t10035\n32387302\t01/11/2016 11:32:52 PM\tUNSANITARY CONDITION\tBROOKLYN\t2391 BEDFORD AVENUE\t11226\n32387299\t01/11/2016 11:32:52 PM\tUNSANITARY CONDITION\tBROOKLYN\t2391 BEDFORD AVENUE\t11226\n32388532\t01/11/2016 11:32:52 PM\tWATER LEAK\tBROOKLYN\t2391 BEDFORD AVENUE\t11226\n32389672\t01/11/2016 11:32:52 PM\tWATER LEAK\tBROOKLYN\t2391 BEDFORD AVENUE\t11226\n32391602\t01/11/2016 11:32:52 PM\tPAINT/PLASTER\tBROOKLYN\t2391 BEDFORD AVENUE\t11226\n32388430\t01/11/2016 11:32:52 PM\tUNSANITARY CONDITION\tBROOKLYN\t2391 BEDFORD AVENUE\t11226\n32392718\t01/11/2016 11:32:47 PM\tHEAT/HOT WATER\tMANHATTAN\t1627 AMSTERDAM AVENUE\t10031\n32389207\t01/11/2016 11:32:47 PM\tHEAT/HOT WATER\tMANHATTAN\t1627 AMSTERDAM AVENUE\t10031\n32386850\t01/11/2016 11:32:47 PM\tHEAT/HOT WATER\tMANHATTAN\t1627 AMSTERDAM AVENUE\t10031\n32389115\t01/11/2016 11:32:42 PM\tHEAT/HOT WATER\tQUEENS\t103-26 68 AVENUE\t11375\n32394926\t01/11/2016 11:32:39 PM\tHEAT/HOT WATER\tBRONX\t1898 HARRISON AVENUE\t10453\n32394110\t01/11/2016 11:31:17 PM\tIllegal Parking\tBROOKLYN\t473 VANDERVOORT AVENUE\t11222\n32394062\t01/11/2016 11:31:13 PM\tIllegal Parking\tBRONX\t1217 FAIRFAX AVENUE\t10465\n32391690\t01/11/2016 11:30:48 PM\tDerelict Vehicle\tQUEENS\t90-10 69 AVENUE\t11375\n32391956\t01/11/2016 11:30:00 PM\tWater System\tQUEENS\t57-15 155 STREET\t11355\n32393701\t01/11/2016 11:29:39 PM\tHEAT/HOT WATER\tBROOKLYN\t368 97 STREET\t11209\n32394233\t01/11/2016 11:29:00 PM\tNoise\tMANHATTAN\t\t10002\n32387953\t01/11/2016 11:28:29 PM\tHEAT/HOT WATER\tMANHATTAN\t859 9 AVENUE\t10019\n32392642\t01/11/2016 11:28:25 PM\tHEAT/HOT WATER\tBRONX\t2090 EAST TREMONT AVENUE\t10462\n32392884\t01/11/2016 11:26:27 PM\tDOOR/WINDOW\tBRONX\t2361 WESTCHESTER AVENUE\t10462\n32392784\t01/11/2016 11:26:27 PM\tPLUMBING\tBRONX\t2361 WESTCHESTER AVENUE\t10462\n32388465\t01/11/2016 11:26:27 PM\tPLUMBING\tBRONX\t2361 WESTCHESTER AVENUE\t10462\n32390018\t01/11/2016 11:26:26 PM\tDOOR/WINDOW\tBRONX\t2361 WESTCHESTER AVENUE\t10462\n32394961\t01/11/2016 11:26:25 PM\tHEAT/HOT WATER\tMANHATTAN\t284 EAST   10 STREET\t10009\n32393048\t01/11/2016 11:26:00 PM\tNoise\tMANHATTAN\tWEST   57 STREET\t10019\n32389569\t01/11/2016 11:26:00 PM\tNoise\tMANHATTAN\t\t10016\n32385906\t01/11/2016 11:26:00 PM\tIllegal Parking\tQUEENS\t1740 DECATUR STREET\t11385\n32394245\t01/11/2016 11:26:00 PM\tNoise\tMANHATTAN\t\t10002\n32385879\t01/11/2016 11:25:56 PM\tIllegal Parking\tBROOKLYN\t418 RIDGEWOOD AVENUE\t11208\n32390598\t01/11/2016 11:24:37 PM\tDerelict Vehicle\tBRONX\t390 PENNYFIELD AVENUE\t10465\n32391567\t01/11/2016 11:24:16 PM\tHEAT/HOT WATER\tMANHATTAN\t1619 AMSTERDAM AVENUE\t10031\n32391512\t01/11/2016 11:24:15 PM\tHEAT/HOT WATER\tMANHATTAN\t311 EAST   90 STREET\t10128\n32388055\t01/11/2016 11:24:15 PM\tHEAT/HOT WATER\tMANHATTAN\t1619 AMSTERDAM AVENUE\t10031\n32386876\t01/11/2016 11:24:15 PM\tHEAT/HOT WATER\tMANHATTAN\t1619 AMSTERDAM AVENUE\t10031\n32387450\t01/11/2016 11:24:00 PM\tDirty Conditions\tQUEENS\t90-13 69 AVENUE\t11375\n32387136\t01/11/2016 11:24:00 PM\tNoise\tBROOKLYN\t3086 EMMONS AVENUE\t11235\n32392198\t01/11/2016 11:23:40 PM\tNon-Residential Heat\tMANHATTAN\t643 PARK AVENUE\t10065\n32393750\t01/11/2016 11:23:00 PM\tStreet Light Condition\tBROOKLYN\t\t11230\n32393706\t01/11/2016 11:22:36 PM\tHEAT/HOT WATER\tMANHATTAN\t261 SEAMAN AVENUE\t10034\n32392616\t01/11/2016 11:22:20 PM\tHEAT/HOT WATER\tBROOKLYN\t664 46 STREET\t11220\n32393737\t01/11/2016 11:22:17 PM\tHEAT/HOT WATER\tMANHATTAN\t572 WEST  141 STREET\t10031\n32389052\t01/11/2016 11:22:14 PM\tHEAT/HOT WATER\tMANHATTAN\t405 EDGECOMBE AVENUE\t10031\n32394234\t01/11/2016 11:22:00 PM\tNoise\tMANHATTAN\t\t10002\n32386872\t01/11/2016 11:21:02 PM\tHEAT/HOT WATER\tMANHATTAN\t1619 AMSTERDAM AVENUE\t10031\n32392655\t01/11/2016 11:21:01 PM\tHEAT/HOT WATER\tMANHATTAN\t1619 AMSTERDAM AVENUE\t10031\n32390378\t01/11/2016 11:21:01 PM\tHEAT/HOT WATER\tMANHATTAN\t1619 AMSTERDAM AVENUE\t10031\n32394741\t01/11/2016 11:20:29 PM\tGENERAL\tBRONX\t1127 FTELEY AVENUE\t10472\n32387933\t01/11/2016 11:20:29 PM\tHEAT/HOT WATER\tBRONX\t1127 FTELEY AVENUE\t10472\n32387700\t01/11/2016 11:19:01 PM\tELECTRIC\tQUEENS\t137-65 HOLLY AVENUE\t11355\n32392364\t01/11/2016 11:19:01 PM\tFLOORING/STAIRS\tQUEENS\t137-65 HOLLY AVENUE\t11355\n32386180\t01/11/2016 11:19:01 PM\tWATER LEAK\tQUEENS\t137-65 HOLLY AVENUE\t11355\n32391616\t01/11/2016 11:19:01 PM\tPAINT/PLASTER\tQUEENS\t137-65 HOLLY AVENUE\t11355\n32389180\t01/11/2016 11:18:59 PM\tHEAT/HOT WATER\tMANHATTAN\t121 FT GEORGE AVENUE\t10040\n32390235\t01/11/2016 11:18:51 PM\tHEAT/HOT WATER\tBROOKLYN\t450 ATKINS AVENUE\t11208\n32391634\t01/11/2016 11:18:38 PM\tIllegal Parking\tBROOKLYN\t179 SPENCER STREET\t11205\n32392687\t01/11/2016 11:18:26 PM\tHEAT/HOT WATER\tMANHATTAN\t553 WEST  144 STREET\t10031\n32389143\t01/11/2016 11:18:18 PM\tHEAT/HOT WATER\tMANHATTAN\t572 WEST  141 STREET\t10031\n32390718\t01/11/2016 11:18:00 PM\tNoise\tMANHATTAN\t\t10019\n32389570\t01/11/2016 11:18:00 PM\tNoise\tMANHATTAN\t53 WEST   23 STREET\t10010\n32388572\t01/11/2016 11:17:59 PM\tWATER LEAK\tMANHATTAN\t3851 BROADWAY\t10032\n32386865\t01/11/2016 11:17:48 PM\tHEAT/HOT WATER\tMANHATTAN\t1619 AMSTERDAM AVENUE\t10031\n32394953\t01/11/2016 11:17:48 PM\tHEAT/HOT WATER\tMANHATTAN\t1619 AMSTERDAM AVENUE\t10031\n32390374\t01/11/2016 11:17:48 PM\tHEAT/HOT WATER\tMANHATTAN\t1619 AMSTERDAM AVENUE\t10031\n32391465\t01/11/2016 11:17:14 PM\tHEAT/HOT WATER\tBROOKLYN\t335 LEFFERTS AVENUE\t11225\n32393050\t01/11/2016 11:17:00 PM\tNoise\tMANHATTAN\tRIVINGTON STREET\t10002\n32388024\t01/11/2016 11:17:00 PM\tHEAT/HOT WATER\tMANHATTAN\t4740 BROADWAY\t10040\n32392552\t01/11/2016 11:16:56 PM\tHEAT/HOT WATER\tMANHATTAN\t248 SHERMAN AVENUE\t10034\n32394991\t01/11/2016 11:16:43 PM\tHEAT/HOT WATER\tBRONX\t3132 PERRY AVENUE\t10467\n32391536\t01/11/2016 11:16:38 PM\tHEAT/HOT WATER\tMANHATTAN\t1627 AMSTERDAM AVENUE\t10031\n32389204\t01/11/2016 11:16:38 PM\tHEAT/HOT WATER\tMANHATTAN\t1627 AMSTERDAM AVENUE\t10031\n32392628\t01/11/2016 11:16:38 PM\tHEAT/HOT WATER\tMANHATTAN\t1627 AMSTERDAM AVENUE\t10031\n32386387\t01/11/2016 11:15:44 PM\tBlocked Driveway\tBRONX\t834 ADEE AVENUE\t10467\n32394353\t01/11/2016 11:15:00 PM\tWater System\tQUEENS\t89-02 247 STREET\t11426\n32393820\t01/11/2016 11:14:58 PM\tHEAT/HOT WATER\tBRONX\t66 EAST TREMONT AVENUE\t10453\n32392391\t01/11/2016 11:14:37 PM\tFerry Inquiry\tUnspecified\t\t\n32389350\t01/11/2016 11:14:23 PM\tDamaged Tree\tBROOKLYN\t1926 EAST 33RD STREET\t11234\n32388312\t01/11/2016 11:14:19 PM\tNoise - Commercial\tQUEENS\tMENAHAN STREET\t11385\n32392253\t01/11/2016 11:14:12 PM\tBlocked Driveway\tBROOKLYN\t12 BAY 14 STREET\t11214\n32386895\t01/11/2016 11:14:05 PM\tHEAT/HOT WATER\tBRONX\t701 CROTONA PARK NORTH\t10457\n32389318\t01/11/2016 11:14:02 PM\tIllegal Parking\tBROOKLYN\t1509 83 STREET\t11228\n32387058\t01/11/2016 11:13:46 PM\tBlocked Driveway\tSTATEN ISLAND\t36 JOHN STREET\t10302\n32386844\t01/11/2016 11:12:35 PM\tHEAT/HOT WATER\tBRONX\t4464 PARK AVENUE\t10457\n32394997\t01/11/2016 11:12:27 PM\tHEAT/HOT WATER\tBRONX\t1701 UNIONPORT ROAD\t10462\n32386816\t01/11/2016 11:12:15 PM\tHEAT/HOT WATER\tBRONX\t1781 CLINTON AVENUE\t10457\n32392265\t01/11/2016 11:12:06 PM\tBlocked Driveway\tBRONX\t834 ADEE AVENUE\t10467\n32389149\t01/11/2016 11:12:05 PM\tHEAT/HOT WATER\tMANHATTAN\t553 WEST  144 STREET\t10031\n32392731\t01/11/2016 11:11:58 PM\tHEAT/HOT WATER\tMANHATTAN\t1627 AMSTERDAM AVENUE\t10031\n32393907\t01/11/2016 11:11:58 PM\tHEAT/HOT WATER\tMANHATTAN\t1627 AMSTERDAM AVENUE\t10031\n32390332\t01/11/2016 11:11:58 PM\tHEAT/HOT WATER\tMANHATTAN\t1627 AMSTERDAM AVENUE\t10031\n32389076\t01/11/2016 11:11:52 PM\tHEAT/HOT WATER\tBRONX\t1127 FTELEY AVENUE\t10472\n32386852\t01/11/2016 11:11:49 PM\tHEAT/HOT WATER\tBRONX\t2050 VALENTINE AVENUE\t10457\n32391443\t01/11/2016 11:11:35 PM\tHEAT/HOT WATER\tMANHATTAN\t2 ELLWOOD STREET\t10040\n32386976\t01/11/2016 11:10:58 PM\tIllegal Parking\tBROOKLYN\t\t11220\n32393995\t01/11/2016 11:10:50 PM\tPLUMBING\tSTATEN ISLAND\t2044 RICHMOND TERRACE\t10302\n32392815\t01/11/2016 11:10:50 PM\tDOOR/WINDOW\tSTATEN ISLAND\t2044 RICHMOND TERRACE\t10302\n32389288\t01/11/2016 11:10:50 PM\tPLUMBING\tSTATEN ISLAND\t2044 RICHMOND TERRACE\t10302\n32395087\t01/11/2016 11:10:50 PM\tPAINT/PLASTER\tSTATEN ISLAND\t2044 RICHMOND TERRACE\t10302\n32389123\t01/11/2016 11:10:48 PM\tHEAT/HOT WATER\tBRONX\t1781 CLINTON AVENUE\t10457\n32391716\t01/11/2016 11:09:59 PM\tBlocked Driveway\tBRONX\t2188 TIEBOUT AVENUE\t10457\n32389160\t01/11/2016 11:09:57 PM\tHEAT/HOT WATER\tBROOKLYN\t1352 NEW YORK AVENUE\t11210\n32387160\t01/11/2016 11:09:00 PM\tDerelict Vehicles\tBRONX\t1790 PROSPECT AVENUE\t10457\n32393601\t01/11/2016 11:08:55 PM\tGENERAL\tMANHATTAN\t3624 BROADWAY\t10031\n32388449\t01/11/2016 11:08:55 PM\tPLUMBING\tMANHATTAN\t3624 BROADWAY\t10031\n32391707\t01/11/2016 11:08:49 PM\tBlocked Driveway\tBROOKLYN\t265 CLARKSON AVENUE\t11226\n32386033\t01/11/2016 11:08:00 PM\tDerelict Vehicles\tBRONX\t1785 PROSPECT AVENUE\t10457\n32394937\t01/11/2016 11:07:34 PM\tHEAT/HOT WATER\tMANHATTAN\t1627 AMSTERDAM AVENUE\t10031\n32390362\t01/11/2016 11:07:34 PM\tHEAT/HOT WATER\tMANHATTAN\t1627 AMSTERDAM AVENUE\t10031\n32393971\t01/11/2016 11:07:34 PM\tHEAT/HOT WATER\tMANHATTAN\t1627 AMSTERDAM AVENUE\t10031\n32393863\t01/11/2016 11:07:17 PM\tHEAT/HOT WATER\tMANHATTAN\t2 ELLWOOD STREET\t10040\n32390736\t01/11/2016 11:07:00 PM\tStreet Light Condition\tMANHATTAN\t1016 ST NICHOLAS AVENUE\t10032\n32393858\t01/11/2016 11:06:35 PM\tHEAT/HOT WATER\tMANHATTAN\t752 WEST END AVENUE\t10025\n32388003\t01/11/2016 11:06:31 PM\tHEAT/HOT WATER\tQUEENS\t36-06 30 STREET\t11106\n32386825\t01/11/2016 11:06:21 PM\tHEAT/HOT WATER\tBROOKLYN\t662 REMSEN AVENUE\t11236\n32386294\t01/11/2016 11:06:21 PM\tFor Hire Vehicle Complaint\tMANHATTAN\t\t10002\n32390652\t01/11/2016 11:06:00 PM\tNoise\tMANHATTAN\t134 ORCHARD STREET\t10002\n32387885\t01/11/2016 11:05:18 PM\tHEAT/HOT WATER\tQUEENS\t134-02 87 AVENUE\t11418\n32386828\t01/11/2016 11:05:16 PM\tHEAT/HOT WATER\tBROOKLYN\t2215 NEWKIRK AVENUE\t11226\n32394561\t01/11/2016 11:05:00 PM\tNoise\tMANHATTAN\t1204 BROADWAY\t10001\n32395027\t01/11/2016 11:04:57 PM\tHEAT/HOT WATER\tMANHATTAN\t1627 AMSTERDAM AVENUE\t10031\n32392740\t01/11/2016 11:04:57 PM\tHEAT/HOT WATER\tMANHATTAN\t1627 AMSTERDAM AVENUE\t10031\n32390314\t01/11/2016 11:04:56 PM\tHEAT/HOT WATER\tMANHATTAN\t1627 AMSTERDAM AVENUE\t10031\n32389235\t01/11/2016 11:04:48 PM\tHEAT/HOT WATER\tBRONX\t1264 CLAY AVENUE\t10456\n32387032\t01/11/2016 11:03:47 PM\tIllegal Parking\tBRONX\t4309 RICHARDSON AVENUE\t10466\n32386796\t01/11/2016 11:03:22 PM\tHEAT/HOT WATER\tQUEENS\t94-25 57 AVENUE\t11373\n32392828\t01/11/2016 11:03:08 PM\tDOOR/WINDOW\tBROOKLYN\t395 BAINBRIDGE STREET\t11233\n32386149\t01/11/2016 11:03:08 PM\tUNSANITARY CONDITION\tBROOKLYN\t395 BAINBRIDGE STREET\t11233\n32393573\t01/11/2016 11:03:08 PM\tFLOORING/STAIRS\tBROOKLYN\t395 BAINBRIDGE STREET\t11233\n32392292\t01/11/2016 11:03:08 PM\tAPPLIANCE\tBROOKLYN\t395 BAINBRIDGE STREET\t11233\n32387333\t01/11/2016 11:03:06 PM\tWATER LEAK\tMANHATTAN\t654 WEST  161 STREET\t10032\n32390069\t01/11/2016 11:03:06 PM\tFLOORING/STAIRS\tMANHATTAN\t654 WEST  161 STREET\t10032\n32391994\t01/11/2016 11:03:06 PM\tUNSANITARY CONDITION\tMANHATTAN\t654 WEST  161 STREET\t10032\n32395093\t01/11/2016 11:03:06 PM\tPAINT/PLASTER\tMANHATTAN\t654 WEST  161 STREET\t10032\n32388474\t01/11/2016 11:03:06 PM\tUNSANITARY CONDITION\tMANHATTAN\t654 WEST  161 STREET\t10032\n32391234\t01/11/2016 11:03:06 PM\tGENERAL\tMANHATTAN\t654 WEST  161 STREET\t10032\n32387612\t01/11/2016 11:03:06 PM\tAPPLIANCE\tMANHATTAN\t654 WEST  161 STREET\t10032\n32392305\t01/11/2016 11:03:06 PM\tELECTRIC\tMANHATTAN\t654 WEST  161 STREET\t10032\n32392787\t01/11/2016 11:03:06 PM\tPLUMBING\tMANHATTAN\t654 WEST  161 STREET\t10032\n32390171\t01/11/2016 11:03:02 PM\tHEAT/HOT WATER\tBROOKLYN\t829 HALSEY STREET\t11233\n32394126\t01/11/2016 11:02:57 PM\tHomeless Encampment\tMANHATTAN\t121 EAST 3 STREET\t10009\n32386783\t01/11/2016 11:02:04 PM\tHEAT/HOT WATER\tBRONX\t2180 WALLACE AVENUE\t10462\n32388454\t01/11/2016 11:02:00 PM\tNoise\tQUEENS\t28-13 14 STREET\t11102\n32386000\t01/11/2016 11:01:17 PM\tNoise - Commercial\tMANHATTAN\t251 WEST 48 STREET\t10036\n32393875\t01/11/2016 11:00:49 PM\tHEAT/HOT WATER\tMANHATTAN\t20 SHERMAN AVENUE\t10040\n32393734\t01/11/2016 11:00:00 PM\tStreet Light Condition\tQUEENS\t\t\n32394676\t01/11/2016 11:00:00 PM\tTraffic Signal Condition\tSTATEN ISLAND\t\t10314\n32387353\t01/11/2016 11:00:00 PM\tWater System\tMANHATTAN\t3 AVENUE\t10003\n32394314\t01/11/2016 11:00:00 PM\tStreet Light Condition\tBRONX\t655 228 ST E\t\n32393443\t01/11/2016 10:59:33 PM\tDOOR/WINDOW\tBROOKLYN\t395 BAINBRIDGE STREET\t11233\n32393578\t01/11/2016 10:59:33 PM\tGENERAL\tBROOKLYN\t395 BAINBRIDGE STREET\t11233\n32386469\t01/11/2016 10:59:33 PM\tDOOR/WINDOW\tBROOKLYN\t395 BAINBRIDGE STREET\t11233\n32393159\t01/11/2016 10:59:33 PM\tUNSANITARY CONDITION\tBROOKLYN\t395 BAINBRIDGE STREET\t11233\n32386570\t01/11/2016 10:59:33 PM\tFLOORING/STAIRS\tBROOKLYN\t395 BAINBRIDGE STREET\t11233\n32389264\t01/11/2016 10:59:33 PM\tPAINT/PLASTER\tBROOKLYN\t395 BAINBRIDGE STREET\t11233\n32391213\t01/11/2016 10:59:33 PM\tFLOORING/STAIRS\tBROOKLYN\t395 BAINBRIDGE STREET\t11233\n32394372\t01/11/2016 10:59:33 PM\tWATER LEAK\tBROOKLYN\t395 BAINBRIDGE STREET\t11233\n32393989\t01/11/2016 10:59:33 PM\tPLUMBING\tBROOKLYN\t395 BAINBRIDGE STREET\t11233\n32391996\t01/11/2016 10:59:33 PM\tUNSANITARY CONDITION\tBROOKLYN\t395 BAINBRIDGE STREET\t11233\n32394267\t01/11/2016 10:59:11 PM\tStreet Condition\tBROOKLYN\t1839 82 STREET\t11214\n32394933\t01/11/2016 10:58:29 PM\tHEAT/HOT WATER\tQUEENS\t43-23 42 STREET\t11104\n32391803\t01/11/2016 10:58:00 PM\tNoise\tMANHATTAN\t79 RIVINGTON STREET\t10002\n32393928\t01/11/2016 10:56:49 PM\tHEAT/HOT WATER\tBROOKLYN\t442 DECATUR STREET\t11233\n32388801\t01/11/2016 10:56:32 PM\tBlocked Driveway\tBRONX\t643 EAST 228 STREET\t10466\n32392668\t01/11/2016 10:56:29 PM\tHEAT/HOT WATER\tBROOKLYN\t160 96 STREET\t11209\n32395017\t01/11/2016 10:56:28 PM\tHEAT/HOT WATER\tBROOKLYN\t1365 ST JOHNS PLACE\t11213\n32389124\t01/11/2016 10:56:24 PM\tHEAT/HOT WATER\tMANHATTAN\t241 EAST   24 STREET\t10010\n32390692\t01/11/2016 10:56:00 PM\tNoise\tMANHATTAN\t\t10011\n32390123\t01/11/2016 10:55:58 PM\tStreet Sign - Damaged\tBROOKLYN\t\t11237\n32386877\t01/11/2016 10:55:38 PM\tHEAT/HOT WATER\tMANHATTAN\t640 10 AVENUE\t10036\n32392738\t01/11/2016 10:55:37 PM\tHEAT/HOT WATER\tMANHATTAN\t640 10 AVENUE\t10036\n32393141\t01/11/2016 10:55:00 PM\tSewer\tQUEENS\t\t11413\n32388216\t01/11/2016 10:54:50 PM\tBlocked Driveway\tBROOKLYN\t1913 EAST 19 STREET\t11229\n32388348\t01/11/2016 10:54:49 PM\tTaxi Report\tMANHATTAN\t\t10002\n32386166\t01/11/2016 10:53:57 PM\tUNSANITARY CONDITION\tMANHATTAN\t4 SOUTH PINEHURST AVENUE\t10033\n32389130\t01/11/2016 10:53:52 PM\tHEAT/HOT WATER\tBROOKLYN\t1365 ST JOHNS PLACE\t11213\n32391975\t01/11/2016 10:53:40 PM\tUNSANITARY CONDITION\tBROOKLYN\t1213 AVENUE Z\t11235\n32391507\t01/11/2016 10:53:35 PM\tHEAT/HOT WATER\tBRONX\t1291 SPOFFORD AVENUE\t10474\n32386698\t01/11/2016 10:53:25 PM\tHEAT/HOT WATER\tBROOKLYN\t395 BAINBRIDGE STREET\t11233\n32393144\t01/11/2016 10:52:00 PM\tWater System\tMANHATTAN\t43 EAST   67 STREET\t10065\n32387987\t01/11/2016 10:51:39 PM\tHEAT/HOT WATER\tBRONX\t27 WEST  181 STREET\t10453\n32389492\t01/11/2016 10:51:16 PM\tNoise - Street/Sidewalk\tBROOKLYN\t\t11209\n32389530\t01/11/2016 10:51:00 PM\tDerelict Vehicles\tQUEENS\t218-65 99 AVENUE\t11429\n32388089\t01/11/2016 10:50:59 PM\tHEAT/HOT WATER\tBROOKLYN\t269 OCEAN PARKWAY\t11218\n32385976\t01/11/2016 10:50:42 PM\tNoise - House of Worship\tMANHATTAN\tWEST 57 STREET\t10019\n32392648\t01/11/2016 10:50:31 PM\tHEAT/HOT WATER\tBRONX\t2090 EAST TREMONT AVENUE\t10462\n32387870\t01/11/2016 10:50:29 PM\tHEAT/HOT WATER\tBRONX\t1390 PROSPECT AVENUE\t10459\n32391951\t01/11/2016 10:50:00 PM\tSewer\tBRONX\t\t\n32393132\t01/11/2016 10:49:51 PM\tUNSANITARY CONDITION\tBROOKLYN\t143 HULL STREET\t11233\n32390172\t01/11/2016 10:49:48 PM\tHEAT/HOT WATER\tBROOKLYN\t400 HERKIMER STREET\t11213\n32391069\t01/11/2016 10:49:45 PM\tTaxi Complaint\tMANHATTAN\t\t10026\n32390084\t01/11/2016 10:49:44 PM\tTaxi Complaint\tQUEENS\t\t11365\n32391560\t01/11/2016 10:49:42 PM\tHEAT/HOT WATER\tBROOKLYN\t269 OCEAN PARKWAY\t11218\n32386840\t01/11/2016 10:49:39 PM\tHEAT/HOT WATER\tQUEENS\t33-15 28 STREET\t11106\n32387719\t01/11/2016 10:49:36 PM\tGENERAL\tBRONX\t449 HOWE AVENUE\t10473\n32388120\t01/11/2016 10:49:36 PM\tPAINT/PLASTER\tBRONX\t449 HOWE AVENUE\t10473\n32392764\t01/11/2016 10:49:36 PM\tPAINT/PLASTER\tBRONX\t449 HOWE AVENUE\t10473\n32386737\t01/11/2016 10:49:36 PM\tHEAT/HOT WATER\tBRONX\t449 HOWE AVENUE\t10473\n32388935\t01/11/2016 10:49:36 PM\tGENERAL\tBRONX\t449 HOWE AVENUE\t10473\n32391684\t01/11/2016 10:49:36 PM\tDOOR/WINDOW\tBRONX\t449 HOWE AVENUE\t10473\n32386912\t01/11/2016 10:49:35 PM\tPAINT/PLASTER\tBRONX\t449 HOWE AVENUE\t10473\n32391494\t01/11/2016 10:49:32 PM\tHEAT/HOT WATER\tMANHATTAN\t393 WEST END AVENUE\t10024\n32388975\t01/11/2016 10:49:20 PM\tStreet Condition\tMANHATTAN\t3 AVENUE\t10003\n32394235\t01/11/2016 10:49:00 PM\tNoise\tMANHATTAN\t\t10002\n32386801\t01/11/2016 10:48:24 PM\tHEAT/HOT WATER\tBRONX\t364 EAST  209 STREET\t10467\n32386841\t01/11/2016 10:48:10 PM\tHEAT/HOT WATER\tQUEENS\t33-15 28 STREET\t11106\n32387140\t01/11/2016 10:48:00 PM\tNoise\tMANHATTAN\t\t10002\n32386099\t01/11/2016 10:48:00 PM\tStreet Light Condition\tMANHATTAN\t452454 44 ST W\t\n32391632\t01/11/2016 10:47:55 PM\tIllegal Parking\tBRONX\t1643 UNDERCLIFF AVENUE\t10453\n32390524\t01/11/2016 10:47:49 PM\tIllegal Parking\tBROOKLYN\t2725 BROWN STREET\t11235\n32394653\t01/11/2016 10:47:39 PM\tConsumer Complaint\tQUEENS\t\t11692\n32392805\t01/11/2016 10:46:58 PM\tBenefit Card Replacement\tUnspecified\t\t\n32385856\t01/11/2016 10:46:00 PM\tTraffic Signal Condition\tQUEENS\t\t11355\n32394059\t01/11/2016 10:45:48 PM\tIllegal Parking\tSTATEN ISLAND\t233 WELLINGTON COURT\t10314\n32388248\t01/11/2016 10:45:06 PM\tIllegal Parking\tBROOKLYN\t1461 OCEAN PARKWAY\t11230\n32386781\t01/11/2016 10:45:00 PM\tHEAT/HOT WATER\tBRONX\t617 EAST  178 STREET\t10457\n32393061\t01/11/2016 10:45:00 PM\tNoise\tMANHATTAN\t\t10002\n32389627\t01/11/2016 10:44:19 PM\tUNSANITARY CONDITION\tMANHATTAN\t4 SOUTH PINEHURST AVENUE\t10033\n32386945\t01/11/2016 10:44:17 PM\tAnimal Abuse\tBROOKLYN\t166 THROOP AVENUE\t11206\n32389337\t01/11/2016 10:44:00 PM\tTraffic Signal Condition\tQUEENS\t\t11355\n32387750\t01/11/2016 10:43:57 PM\tFLOORING/STAIRS\tQUEENS\t138-29 91 AVENUE\t11435\n32390612\t01/11/2016 10:43:43 PM\tDerelict Vehicle\tBRONX\t2711 YATES AVENUE\t10469\n32389061\t01/11/2016 10:43:43 PM\tHEAT/HOT WATER\tMANHATTAN\t132 SHERMAN AVENUE\t10034\n32391564\t01/11/2016 10:43:32 PM\tHEAT/HOT WATER\tBRONX\t3821 BRONXWOOD AVENUE\t10469\n32393708\t01/11/2016 10:43:06 PM\tHEAT/HOT WATER\tBRONX\t883 EAST  180 STREET\t10460\n32393872\t01/11/2016 10:43:00 PM\tHEAT/HOT WATER\tBROOKLYN\t719 HENRY STREET\t11231\n32387338\t01/11/2016 10:43:00 PM\tWater System\tMANHATTAN\t656 WEST  162 STREET\t10032\n32389669\t01/11/2016 10:43:00 PM\tWater System\tQUEENS\t20-10A 125 STREET\t11356\n32391457\t01/11/2016 10:42:57 PM\tHEAT/HOT WATER\tBRONX\t214 EAST  203 STREET\t10458\n32388011\t01/11/2016 10:42:28 PM\tHEAT/HOT WATER\tBRONX\t2090 EAST TREMONT AVENUE\t10462\n32389338\t01/11/2016 10:42:00 PM\tTraffic Signal Condition\tQUEENS\t\t11355\n32393051\t01/11/2016 10:42:00 PM\tNoise\tMANHATTAN\t\t10002\n32385975\t01/11/2016 10:41:30 PM\tNoise - Commercial\tBROOKLYN\t\t11215\n32390870\t01/11/2016 10:41:00 PM\tOther Enforcement\tBROOKLYN\t708 HINSDALE STREET\t11207\n32387274\t01/11/2016 10:41:00 PM\tWater System\tQUEENS\t14-50 132 STREET\t11356\n32391578\t01/11/2016 10:40:33 PM\tPAINT/PLASTER\tBRONX\t449 HOWE AVENUE\t10473\n32393010\t01/11/2016 10:40:33 PM\tPLUMBING\tBRONX\t449 HOWE AVENUE\t10473\n32389286\t01/11/2016 10:40:33 PM\tPLUMBING\tBRONX\t449 HOWE AVENUE\t10473\n32391636\t01/11/2016 10:40:33 PM\tPLUMBING\tBRONX\t449 HOWE AVENUE\t10473\n32386917\t01/11/2016 10:40:33 PM\tPAINT/PLASTER\tBRONX\t449 HOWE AVENUE\t10473\n32386127\t01/11/2016 10:40:33 PM\tPLUMBING\tBRONX\t449 HOWE AVENUE\t10473\n32395100\t01/11/2016 10:40:33 PM\tPLUMBING\tBRONX\t449 HOWE AVENUE\t10473\n32386550\t01/11/2016 10:40:33 PM\tELECTRIC\tBRONX\t449 HOWE AVENUE\t10473\n32386960\t01/11/2016 10:40:33 PM\tPLUMBING\tBRONX\t449 HOWE AVENUE\t10473\n32393487\t01/11/2016 10:40:33 PM\tAPPLIANCE\tBRONX\t449 HOWE AVENUE\t10473\n32389325\t01/11/2016 10:40:20 PM\tDrinking\tMANHATTAN\t196 2 AVENUE\t10003\n32392077\t01/11/2016 10:39:34 PM\tSidewalk Condition\tMANHATTAN\t123 WEST 79 STREET\t10024\n32395020\t01/11/2016 10:39:32 PM\tHEAT/HOT WATER\tMANHATTAN\t11 AVENUE D\t10009\n32389433\t01/11/2016 10:39:13 PM\tIllegal Parking\tQUEENS\t60-44 59 ROAD\t11378\n32394253\t01/11/2016 10:39:00 PM\tNoise\tMANHATTAN\t78 RIVINGTON STREET\t10002\n32386994\t01/11/2016 10:38:23 PM\tPLUMBING\tQUEENS\t138-29 91 AVENUE\t11435\n32391033\t01/11/2016 10:38:15 PM\tBlocked Driveway\tBRONX\t147 EAST 236 STREET\t10470\n32386084\t01/11/2016 10:38:00 PM\tNoise\tMANHATTAN\t\t10002\n32389661\t01/11/2016 10:37:19 PM\tWATER LEAK\tBROOKLYN\t41 HAVEMEYER STREET\t11211\n32394339\t01/11/2016 10:37:19 PM\tUNSANITARY CONDITION\tBROOKLYN\t41 HAVEMEYER STREET\t11211\n32391964\t01/11/2016 10:37:19 PM\tUNSANITARY CONDITION\tBROOKLYN\t41 HAVEMEYER STREET\t11211\n32389671\t01/11/2016 10:37:19 PM\tWATER LEAK\tBROOKLYN\t41 HAVEMEYER STREET\t11211\n32394847\t01/11/2016 10:37:05 PM\tHEAT/HOT WATER\tBROOKLYN\t1352 NEW YORK AVENUE\t11210\n32391846\t01/11/2016 10:37:00 PM\tDerelict Vehicles\tBRONX\t2747 YATES AVENUE\t10469\n32386091\t01/11/2016 10:37:00 PM\tStreet Light Condition\tBROOKLYN\t1868 50 STREET\t11204\n32394828\t01/11/2016 10:36:50 PM\tHEAT/HOT WATER\tBRONX\t1691 FULTON AVENUE\t10457\n32394823\t01/11/2016 10:36:40 PM\tHEAT/HOT WATER\tQUEENS\t91-13 183 STREET\t11423\n32391789\t01/11/2016 10:36:40 PM\tDerelict Vehicle\tQUEENS\t86-19 97 AVENUE\t11416\n32390263\t01/11/2016 10:36:39 PM\tHEAT/HOT WATER\tMANHATTAN\t2 ELLWOOD STREET\t10040\n32388038\t01/11/2016 10:36:24 PM\tHEAT/HOT WATER\tBRONX\t2533 AQUEDUCT AVENUE WEST\t10468\n32388232\t01/11/2016 10:36:17 PM\tIllegal Parking\tQUEENS\t1808 DECATUR STREET\t11385\n32389918\t01/11/2016 10:36:02 PM\tBlocked Driveway\tBROOKLYN\t46 CHESTNUT STREET\t11208\n32392306\t01/11/2016 10:35:51 PM\tDOOR/WINDOW\tBROOKLYN\t1144 BLAKE AVENUE\t11208\n32391508\t01/11/2016 10:35:37 PM\tHEAT/HOT WATER\tQUEENS\t72-10 41 AVENUE\t11377\n32389657\t01/11/2016 10:35:00 PM\tWater System\tMANHATTAN\t37 EAST   67 STREET\t10065\n32387188\t01/11/2016 10:35:00 PM\tStreet Light Condition\tSTATEN ISLAND\t290 TRAVIS AVENUE\t10314\n32394198\t01/11/2016 10:34:46 PM\tNoise - Commercial\tQUEENS\t144-08A LIBERTY AVENUE\t11435\n32387249\t01/11/2016 10:34:13 PM\tPLUMBING\tQUEENS\t98-40 57 AVENUE\t11368\n32389230\t01/11/2016 10:34:13 PM\tHEAT/HOT WATER\tQUEENS\t98-40 57 AVENUE\t11368\n32391950\t01/11/2016 10:34:13 PM\tUNSANITARY CONDITION\tQUEENS\t98-40 57 AVENUE\t11368\n32386695\t01/11/2016 10:34:00 PM\tSnow\tSTATEN ISLAND\t\t10314\n32387125\t01/11/2016 10:33:33 PM\tRodent\tMANHATTAN\t16 WEST 82 STREET\t10024\n32393913\t01/11/2016 10:33:07 PM\tHEAT/HOT WATER\tQUEENS\t98-40 57 AVENUE\t11368\n32394431\t01/11/2016 10:33:03 PM\tStreet Condition\tBROOKLYN\t4901 14 AVENUE\t11219\n32389571\t01/11/2016 10:33:00 PM\tNoise\tBROOKLYN\t487 4 STREET\t11215\n32391135\t01/11/2016 10:31:16 PM\tELECTRIC\tBRONX\t1115 VIRGINIA AVENUE\t10472\n32390443\t01/11/2016 10:31:16 PM\tPLUMBING\tBRONX\t1115 VIRGINIA AVENUE\t10472\n32386117\t01/11/2016 10:31:15 PM\tPLUMBING\tBRONX\t1115 VIRGINIA AVENUE\t10472\n32391134\t01/11/2016 10:31:15 PM\tELECTRIC\tBRONX\t1115 VIRGINIA AVENUE\t10472\n32386831\t01/11/2016 10:31:11 PM\tHEAT/HOT WATER\tMANHATTAN\t10 WEST   90 STREET\t10024\n32392676\t01/11/2016 10:31:02 PM\tHEAT/HOT WATER\tBRONX\t2090 EAST TREMONT AVENUE\t10462\n32394368\t01/11/2016 10:31:00 PM\tWater System\tMANHATTAN\t659 WEST  162 STREET\t10032\n32391351\t01/11/2016 10:30:06 PM\tHEAT/HOT WATER\tBROOKLYN\t397 ROGERS AVENUE\t11225\n32392737\t01/11/2016 10:30:05 PM\tHEAT/HOT WATER\tQUEENS\t98-40 57 AVENUE\t11368\n32393852\t01/11/2016 10:30:02 PM\tHEAT/HOT WATER\tQUEENS\t79-25 150 STREET\t11367\n32394772\t01/11/2016 10:29:53 PM\tNoise - Vehicle\tMANHATTAN\t307 WEST 115 STREET\t10026\n32385886\t01/11/2016 10:29:15 PM\tIllegal Parking\tBROOKLYN\t722 EAST 4 STREET\t11218\n32388097\t01/11/2016 10:28:58 PM\tHEAT/HOT WATER\tBROOKLYN\t1594 EASTERN PARKWAY EXTENSION\t11233\n32388736\t01/11/2016 10:28:50 PM\tAsbestos\tBROOKLYN\t738 EAST 45 STREET\t11203\n32388272\t01/11/2016 10:27:52 PM\tFLOORING/STAIRS\tBRONX\t767 EAST  223 STREET\t10466\n32392226\t01/11/2016 10:27:47 PM\tBlocked Driveway\tQUEENS\t32-08 107 STREET\t11369\n32388466\t01/11/2016 10:27:38 PM\tUNSANITARY CONDITION\tBROOKLYN\t1288 ST JOHNS PLACE\t11213\n32389667\t01/11/2016 10:27:38 PM\tUNSANITARY CONDITION\tBROOKLYN\t1288 ST JOHNS PLACE\t11213\n32394349\t01/11/2016 10:27:38 PM\tUNSANITARY CONDITION\tBROOKLYN\t1288 ST JOHNS PLACE\t11213\n32394930\t01/11/2016 10:27:31 PM\tHEAT/HOT WATER\tBRONX\t2090 EAST TREMONT AVENUE\t10462\n32389453\t01/11/2016 10:27:24 PM\tPosting Advertisement\tSTATEN ISLAND\t177 LAREDO AVENUE\t10312\n32391834\t01/11/2016 10:27:00 PM\tDerelict Vehicles\tQUEENS\t49-70 31 STREET\t11101\n32394836\t01/11/2016 10:26:44 PM\tHEAT/HOT WATER\tBROOKLYN\t151 EAST   96 STREET\t11212\n32393097\t01/11/2016 10:26:00 PM\tNoise\tMANHATTAN\t243 SULLIVAN STREET\t10012\n32389903\t01/11/2016 10:25:41 PM\tDOOR/WINDOW\tMANHATTAN\t146 10 AVENUE\t10011\n32390428\t01/11/2016 10:25:41 PM\tPAINT/PLASTER\tMANHATTAN\t146 10 AVENUE\t10011\n32391487\t01/11/2016 10:25:36 PM\tHEAT/HOT WATER\tBROOKLYN\t738 EAST   45 STREET\t11203\n32387899\t01/11/2016 10:25:32 PM\tHEAT/HOT WATER\tBROOKLYN\t76 NORTH    4 STREET\t11249\n32387555\t01/11/2016 10:25:16 PM\tDamaged Tree\tSTATEN ISLAND\t15 LOCUST AVENUE\t10306\n32386065\t01/11/2016 10:25:00 PM\tNoise\tMANHATTAN\t336 EAST   86 STREET\t10028\n32388450\t01/11/2016 10:25:00 PM\tNoise\tMANHATTAN\t\t10011\n32388249\t01/11/2016 10:24:53 PM\tIllegal Parking\tQUEENS\t87-22 90 STREET\t11421\n32386892\t01/11/2016 10:24:48 PM\tHEAT/HOT WATER\tBROOKLYN\t2017 CHURCH AVENUE\t11226\n32393817\t01/11/2016 10:24:42 PM\tHEAT/HOT WATER\tBROOKLYN\t2158 DEAN STREET\t11233\n32389393\t01/11/2016 10:24:39 PM\tIllegal Parking\tBROOKLYN\t1507 83 STREET\t11228\n32394306\t01/11/2016 10:24:11 PM\tStreet Condition\tMANHATTAN\tEAST   12 STREET\t10003\n32387184\t01/11/2016 10:24:00 PM\tSanitation Condition\tBRONX\tLAYTON AVENUE\t10465\n32388274\t01/11/2016 10:23:57 PM\tHomeless Person Assistance\tMANHATTAN\t25 UNIVERSITY PLACE\t10003\n32387278\t01/11/2016 10:23:22 PM\tUNSANITARY CONDITION\tBRONX\t1440 EAST AVENUE\t10462\n32392777\t01/11/2016 10:23:22 PM\tPAINT/PLASTER\tBRONX\t1440 EAST AVENUE\t10462\n32386784\t01/11/2016 10:23:15 PM\tHEAT/HOT WATER\tBRONX\t2090 EAST TREMONT AVENUE\t10462\n32394954\t01/11/2016 10:22:23 PM\tHEAT/HOT WATER\tMANHATTAN\t148 WEST   14 STREET\t10011\n32390271\t01/11/2016 10:22:23 PM\tHEAT/HOT WATER\tBRONX\t1773 VYSE AVENUE\t10460\n32390346\t01/11/2016 10:22:20 PM\tHEAT/HOT WATER\tBRONX\t2238 MORRIS AVENUE\t10453\n32394810\t01/11/2016 10:22:17 PM\tHEAT/HOT WATER\tBROOKLYN\t902 CLARKSON AVENUE\t11203\n32391915\t01/11/2016 10:22:13 PM\tNoise - Commercial\tQUEENS\t\t11432\n32386503\t01/11/2016 10:21:38 PM\tELECTRIC\tBRONX\t507 EAST  183 STREET\t10458\n32387757\t01/11/2016 10:21:38 PM\tGENERAL\tBRONX\t507 EAST  183 STREET\t10458\n32391003\t01/11/2016 10:20:43 PM\tBlocked Driveway\tQUEENS\t84-18 89 STREET\t11421\n32386211\t01/11/2016 10:20:13 PM\tWATER LEAK\tMANHATTAN\t31 WEST  129 STREET\t10027\n32393062\t01/11/2016 10:20:00 PM\tNoise\tMANHATTAN\t\t10021\n32392855\t01/11/2016 10:20:00 PM\tIllegal Parking\tBROOKLYN\t1950 54 STREET\t11204\n32387150\t01/11/2016 10:20:00 PM\tNoise\tQUEENS\t84-62 AUSTIN STREET\t11415\n32391378\t01/11/2016 10:19:37 PM\tHEAT/HOT WATER\tMANHATTAN\t210 STANTON STREET\t10002\n32386699\t01/11/2016 10:19:34 PM\tHEAT/HOT WATER\tBRONX\t2025 SEWARD AVENUE\t10473\n32388217\t01/11/2016 10:19:32 PM\tIllegal Parking\tBROOKLYN\t26 CASS PLACE\t11235\n32385992\t01/11/2016 10:19:25 PM\tNoise - Commercial\tQUEENS\t64-35 102 STREET\t11374\n32394833\t01/11/2016 10:19:16 PM\tHEAT/HOT WATER\tBRONX\t112 EAST  198 STREET\t10468\n32390324\t01/11/2016 10:19:13 PM\tHEAT/HOT WATER\tBRONX\t2090 EAST TREMONT AVENUE\t10462\n32390226\t01/11/2016 10:19:05 PM\tHEAT/HOT WATER\tMANHATTAN\t3851 BROADWAY\t10032\n32390726\t01/11/2016 10:19:00 PM\tStreet Light Condition\tBRONX\t909 KELLY STREET\t10459\n32386546\t01/11/2016 10:18:53 PM\tELECTRIC\tBRONX\t767 EAST  223 STREET\t10466\n32390454\t01/11/2016 10:18:53 PM\tPLUMBING\tBRONX\t767 EAST  223 STREET\t10466\n32386159\t01/11/2016 10:18:53 PM\tUNSANITARY CONDITION\tBRONX\t767 EAST  223 STREET\t10466\n32391957\t01/11/2016 10:18:53 PM\tUNSANITARY CONDITION\tBRONX\t767 EAST  223 STREET\t10466\n32386465\t01/11/2016 10:18:53 PM\tELECTRIC\tBRONX\t767 EAST  223 STREET\t10466\n32387272\t01/11/2016 10:18:53 PM\tUNSANITARY CONDITION\tBRONX\t767 EAST  223 STREET\t10466\n32388871\t01/11/2016 10:18:53 PM\tDOOR/WINDOW\tBRONX\t767 EAST  223 STREET\t10466\n32391553\t01/11/2016 10:18:53 PM\tPAINT/PLASTER\tBRONX\t767 EAST  223 STREET\t10466\n32393517\t01/11/2016 10:18:53 PM\tDOOR/WINDOW\tBRONX\t767 EAST  223 STREET\t10466\n32391725\t01/11/2016 10:18:05 PM\tIllegal Parking\tBRONX\t1217 FAIRFAX AVENUE\t10465\n32393057\t01/11/2016 10:17:51 PM\tSAFETY\tMANHATTAN\t605 WEST  137 STREET\t10031\n32394927\t01/11/2016 10:17:43 PM\tHEAT/HOT WATER\tMANHATTAN\t317 EAST   74 STREET\t10021\n32391148\t01/11/2016 10:17:37 PM\tELECTRIC\tQUEENS\t101-39 126 STREET\t11419\n32387907\t01/11/2016 10:17:37 PM\tHEAT/HOT WATER\tQUEENS\t101-39 126 STREET\t11419\n32387164\t01/11/2016 10:17:29 PM\tNoise - Street/Sidewalk\tMANHATTAN\t656 WEST 162 STREET\t10032\n32386713\t01/11/2016 10:17:20 PM\tHEAT/HOT WATER\tBRONX\t900 HOE AVENUE\t10459\n32390653\t01/11/2016 10:17:00 PM\tNoise\tQUEENS\t64-34 102 STREET\t11374\n32392518\t01/11/2016 10:16:38 PM\tHEAT/HOT WATER\tBRONX\t2075 HAVILAND AVENUE\t10472\n32386811\t01/11/2016 10:16:22 PM\tHEAT/HOT WATER\tBRONX\t2090 EAST TREMONT AVENUE\t10462\n32394285\t01/11/2016 10:16:00 PM\tStreet Light Condition\tSTATEN ISLAND\t403 ADAMS AVENUE\t10306\n32393385\t01/11/2016 10:16:00 PM\tAir Quality\tMANHATTAN\t323 EAST   50 STREET\t10022\n32392857\t01/11/2016 10:15:46 PM\tRodent\tQUEENS\t103-27 135 STREET\t11419\n32391804\t01/11/2016 10:15:00 PM\tNoise\tBROOKLYN\t485 4 STREET\t11215\n32393422\t01/11/2016 10:14:33 PM\tDOOR/WINDOW\tBROOKLYN\t88 STARR STREET\t11237\n32388845\t01/11/2016 10:14:33 PM\tDOOR/WINDOW\tBROOKLYN\t88 STARR STREET\t11237\n32394558\t01/11/2016 10:14:29 PM\tBenefit Card Replacement\tUnspecified\t\t\n32391080\t01/11/2016 10:14:20 PM\tBenefit Card Replacement\tUnspecified\t\t\n32390523\t01/11/2016 10:14:14 PM\tIllegal Parking\tMANHATTAN\t60 WEST 142 STREET\t10037\n32385908\t01/11/2016 10:14:07 PM\tIllegal Parking\tBROOKLYN\t29 DEBEVOISE AVENUE\t11211\n32390140\t01/11/2016 10:14:00 PM\tStreet Light Condition\tQUEENS\t13308 LAURELTON PKWY\t\n32393000\t01/11/2016 10:13:48 PM\tNoise - Street/Sidewalk\tMANHATTAN\t671 WEST 162 STREET\t10032\n32389184\t01/11/2016 10:13:30 PM\tHEAT/HOT WATER\tBRONX\t2968 PERRY AVENUE\t10458\n32387016\t01/11/2016 10:13:10 PM\tIllegal Parking\tMANHATTAN\t659 WEST 162 STREET\t10032\n32389469\t01/11/2016 10:12:55 PM\tNoise - Commercial\tMANHATTAN\t2271 ADAM CLAYTON POWELL BOULEVARD\t10030\n32387550\t01/11/2016 10:12:51 PM\tBlocked Driveway\tBROOKLYN\t35 WYONA STREET\t11207\n32390464\t01/11/2016 10:12:36 PM\tIllegal Parking\tMANHATTAN\t333 EAST 69 STREET\t10021\n32386732\t01/11/2016 10:12:26 PM\tHEAT/HOT WATER\tMANHATTAN\t89 CHRISTOPHER STREET\t10014\n32392334\t01/11/2016 10:11:23 PM\tDOOR/WINDOW\tMANHATTAN\t605 WEST  137 STREET\t10031\n32386782\t01/11/2016 10:11:21 PM\tHEAT/HOT WATER\tMANHATTAN\t3105 BROADWAY\t10027\n32389136\t01/11/2016 10:11:16 PM\tHEAT/HOT WATER\tMANHATTAN\t119 VERMILYEA AVENUE\t10034\n32386904\t01/11/2016 10:11:16 PM\tHEAT/HOT WATER\tBROOKLYN\t351 WASHINGTON AVENUE\t11238\n32389091\t01/11/2016 10:10:43 PM\tHEAT/HOT WATER\tMANHATTAN\t2816 FREDERICK DOUGLASS BOULEVARD\t10039\n32389176\t01/11/2016 10:10:29 PM\tHEAT/HOT WATER\tMANHATTAN\t114 EAST    1 STREET\t10009\n32389398\t01/11/2016 10:10:18 PM\tNoise - Commercial\tMANHATTAN\t2210 FREDERICK DOUGLASS BOULEVARD\t10026\n32394294\t01/11/2016 10:10:00 PM\tNoise\tMANHATTAN\t\t10011\n32385916\t01/11/2016 10:09:48 PM\tIllegal Parking\tQUEENS\t62-10 62 AVENUE\t11379\n32390835\t01/11/2016 10:09:18 PM\tWATER LEAK\tBROOKLYN\t326 LINCOLN PLACE\t11238\n32390347\t01/11/2016 10:09:08 PM\tHEAT/HOT WATER\tBROOKLYN\t1594 EASTERN PARKWAY EXTENSION\t11233\n32392708\t01/11/2016 10:09:05 PM\tHEAT/HOT WATER\tBRONX\t2238 MORRIS AVENUE\t10453\n32389265\t01/11/2016 10:09:02 PM\tPAINT/PLASTER\tMANHATTAN\t503 WEST  158 STREET\t10032\n32391276\t01/11/2016 10:09:02 PM\tGENERAL\tMANHATTAN\t503 WEST  158 STREET\t10032\n32391730\t01/11/2016 10:09:00 PM\tIllegal Parking\tBROOKLYN\t29 DEBEVOISE AVENUE\t11211\n32386133\t01/11/2016 10:08:59 PM\tSAFETY\tBRONX\t65 EAST  193 STREET\t10468\n32388469\t01/11/2016 10:08:59 PM\tSAFETY\tBRONX\t65 EAST  193 STREET\t10468\n32390693\t01/11/2016 10:07:00 PM\tNoise\tMANHATTAN\t\t10011\n32394254\t01/11/2016 10:07:00 PM\tNoise\tBROOKLYN\t15 SOUTH   11 STREET\t11249\n32392257\t01/11/2016 10:06:55 PM\tBlocked Driveway\tBRONX\t145 EAST 236 STREET\t10470\n32391150\t01/11/2016 10:06:47 PM\tDOOR/WINDOW\tBRONX\t1285 SHAKESPEARE AVENUE\t10452\n32394329\t01/11/2016 10:06:47 PM\tUNSANITARY CONDITION\tBRONX\t1285 SHAKESPEARE AVENUE\t10452\n32394347\t01/11/2016 10:06:47 PM\tUNSANITARY CONDITION\tBRONX\t1285 SHAKESPEARE AVENUE\t10452\n32386438\t01/11/2016 10:06:25 PM\tBenefit Card Replacement\tUnspecified\t\t\n32389131\t01/11/2016 10:06:15 PM\tHEAT/HOT WATER\tBROOKLYN\t2069 85 STREET\t11214\n32392713\t01/11/2016 10:05:55 PM\tHEAT/HOT WATER\tBRONX\t2351 GRAND CONCOURSE\t10468\n32391375\t01/11/2016 10:05:49 PM\tHEAT/HOT WATER\tMANHATTAN\t208 WEST   30 STREET\t10001\n32391552\t01/11/2016 10:05:43 PM\tHEAT/HOT WATER\tBROOKLYN\t314 TROUTMAN STREET\t11237\n32388207\t01/11/2016 10:05:09 PM\tIllegal Parking\tBROOKLYN\t644 44 STREET\t11220\n32388034\t01/11/2016 10:04:44 PM\tHEAT/HOT WATER\tMANHATTAN\t725 WEST  172 STREET\t10032\n32392899\t01/11/2016 10:04:39 PM\tFor Hire Vehicle Complaint\tQUEENS\t\t11433\n32393880\t01/11/2016 10:04:32 PM\tHEAT/HOT WATER\tQUEENS\t25-83 31 STREET\t11102\n32388173\t01/11/2016 10:04:27 PM\tIllegal Parking\tBROOKLYN\t445 58 STREET\t11220\n32393063\t01/11/2016 10:04:00 PM\tNoise\tMANHATTAN\t\t10012\n32388339\t01/11/2016 10:04:00 PM\tNoise\tBROOKLYN\t198 COURT STREET\t11201\n32386863\t01/11/2016 10:03:37 PM\tHEAT/HOT WATER\tMANHATTAN\t351 WADSWORTH AVENUE\t10040\n32392719\t01/11/2016 10:03:32 PM\tHEAT/HOT WATER\tBROOKLYN\t314 TROUTMAN STREET\t11237\n32393718\t01/11/2016 10:02:31 PM\tHEAT/HOT WATER\tBROOKLYN\t1455 55 STREET\t11219\n32391242\t01/11/2016 10:02:26 PM\tGENERAL\tBRONX\t767 EAST  223 STREET\t10466\n32389884\t01/11/2016 10:02:26 PM\tAPPLIANCE\tBRONX\t767 EAST  223 STREET\t10466\n32388503\t01/11/2016 10:02:26 PM\tWATER LEAK\tBRONX\t767 EAST  223 STREET\t10466\n32391654\t01/11/2016 10:02:26 PM\tPLUMBING\tBRONX\t767 EAST  223 STREET\t10466\n32386579\t01/11/2016 10:02:26 PM\tFLOORING/STAIRS\tBRONX\t767 EAST  223 STREET\t10466\n32386078\t01/11/2016 10:02:26 PM\tAPPLIANCE\tBRONX\t767 EAST  223 STREET\t10466\n32386913\t01/11/2016 10:02:26 PM\tPAINT/PLASTER\tBRONX\t767 EAST  223 STREET\t10466\n32390422\t01/11/2016 10:02:26 PM\tPLUMBING\tBRONX\t767 EAST  223 STREET\t10466\n32391643\t01/11/2016 10:02:26 PM\tPLUMBING\tBRONX\t767 EAST  223 STREET\t10466\n32394874\t01/11/2016 10:02:26 PM\tHEAT/HOT WATER\tBROOKLYN\t350 KEAP STREET\t11211\n32389054\t01/11/2016 10:02:26 PM\tHEAT/HOT WATER\tBRONX\t767 EAST  223 STREET\t10466\n32390620\t01/11/2016 10:02:25 PM\tNoise - Commercial\tBRONX\t1207 BEACH AVENUE\t10472\n32391559\t01/11/2016 10:02:21 PM\tHEAT/HOT WATER\tMANHATTAN\t25 WEST   87 STREET\t10024\n32387075\t01/11/2016 10:02:04 PM\tIllegal Parking\tBROOKLYN\t\t11249\n32387095\t01/11/2016 10:02:00 PM\tSanitation Condition\tQUEENS\t26-48 93 STREET\t11369\n32393833\t01/11/2016 10:01:21 PM\tHEAT/HOT WATER\tMANHATTAN\t338 WEST   46 STREET\t10036\n32388394\t01/11/2016 10:00:09 PM\tNoise - Vehicle\tMANHATTAN\t353 EAST 53 STREET\t10022\n32389798\t01/11/2016 10:00:00 PM\tMissed Collection (All Materials)\tQUEENS\t97-12 67 AVENUE\t11374\n32389881\t01/11/2016 10:00:00 PM\tNoise\tBROOKLYN\t4211 14 AVENUE\t11219\n32389459\t01/11/2016 10:00:00 PM\tSanitation Condition\tSTATEN ISLAND\t573 WATCHOGUE ROAD\t10314\n32385940\t01/11/2016 10:00:00 PM\tSanitation Condition\tBROOKLYN\t135 COLUMBIA STREET\t11231\n32393587\t01/11/2016 09:59:43 PM\tGENERAL\tMANHATTAN\t222 WEST  141 STREET\t10030\n32392023\t01/11/2016 09:59:43 PM\tWATER LEAK\tMANHATTAN\t222 WEST  141 STREET\t10030\n32386592\t01/11/2016 09:59:37 PM\tGENERAL\tMANHATTAN\t104 2 AVENUE\t10003\n32389355\t01/11/2016 09:59:32 PM\tFor Hire Vehicle Complaint\tQUEENS\t\t11433\n32387003\t01/11/2016 09:59:27 PM\tRodent\tQUEENS\t8101-8109 34TH AVE\t11372\n32386514\t01/11/2016 09:59:24 PM\tDOOR/WINDOW\tBRONX\t995 EAST  181 STREET\t10460\n32392314\t01/11/2016 09:59:24 PM\tDOOR/WINDOW\tBRONX\t995 EAST  181 STREET\t10460\n32391099\t01/11/2016 09:59:24 PM\tDOOR/WINDOW\tBRONX\t995 EAST  181 STREET\t10460\n32392534\t01/11/2016 09:59:24 PM\tHEAT/HOT WATER\tBRONX\t995 EAST  181 STREET\t10460\n32390317\t01/11/2016 09:59:18 PM\tHEAT/HOT WATER\tBROOKLYN\t1350 NEW YORK AVENUE\t11210\n32394757\t01/11/2016 09:59:00 PM\tSewer\tBRONX\t1453 WALTON AVENUE\t10452\n32394556\t01/11/2016 09:58:13 PM\tBlocked Driveway\tBROOKLYN\t359 49 STREET\t11220\n32387029\t01/11/2016 09:58:11 PM\tIllegal Parking\tSTATEN ISLAND\t\t10309\n32394984\t01/11/2016 09:57:54 PM\tHEAT/HOT WATER\tBRONX\t27 WEST  181 STREET\t10453\n32386793\t01/11/2016 09:57:52 PM\tHEAT/HOT WATER\tQUEENS\t41-14 58 STREET\t11377\n32394565\t01/11/2016 09:57:14 PM\tBlocked Driveway\tBROOKLYN\t2719 CORTELYOU ROAD\t11226\n32390779\t01/11/2016 09:57:00 PM\tWater System\tQUEENS\t\t11356\n32388114\t01/11/2016 09:56:44 PM\tHEAT/HOT WATER\tQUEENS\t119-66 80 ROAD\t11415\n32393285\t01/11/2016 09:56:29 PM\tNoise - Vehicle\tBROOKLYN\t\t11218\n32387028\t01/11/2016 09:56:22 PM\tIllegal Parking\tQUEENS\t1736 DECATUR STREET\t11385\n32388431\t01/11/2016 09:56:00 PM\tSewer\tQUEENS\t85-10 159 STREET\t11432\n32390118\t01/11/2016 09:55:21 PM\tStreet Condition\tBROOKLYN\tSOUTH 11 STREET\t11249\n32389409\t01/11/2016 09:55:08 PM\tNoise - Commercial\tMANHATTAN\t2273 7 AVENUE\t10030\n32389195\t01/11/2016 09:55:07 PM\tHEAT/HOT WATER\tBROOKLYN\t617 HEGEMAN AVENUE\t11207\n32394236\t01/11/2016 09:55:00 PM\tNoise\tMANHATTAN\t353 EAST   53 STREET\t10022\n32391605\t01/11/2016 09:55:00 PM\tOther Enforcement\tQUEENS\t\t11385\n32392769\t01/11/2016 09:54:22 PM\tHEAT/HOT WATER\tQUEENS\t119-66 80 ROAD\t11415\n32387872\t01/11/2016 09:54:19 PM\tHEAT/HOT WATER\tBRONX\t2559 GRAND CONCOURSE\t10468\n32390552\t01/11/2016 09:54:13 PM\tIllegal Parking\tMANHATTAN\t415 EAST 52 STREET\t10022\n32392862\t01/11/2016 09:54:07 PM\tIllegal Parking\tBROOKLYN\t1950 WEST 3 STREET\t11223\n32386148\t01/11/2016 09:54:05 PM\tUNSANITARY CONDITION\tBROOKLYN\t1535 EAST   14 STREET\t11230\n32388149\t01/11/2016 09:54:05 PM\tPAINT/PLASTER\tBROOKLYN\t1535 EAST   14 STREET\t11230\n32392672\t01/11/2016 09:54:02 PM\tHEAT/HOT WATER\tBRONX\t1326 COMMONWEALTH AVENUE\t10472\n32393896\t01/11/2016 09:53:58 PM\tHEAT/HOT WATER\tQUEENS\t37-32 104 STREET\t11368\n32391463\t01/11/2016 09:53:56 PM\tHEAT/HOT WATER\tMANHATTAN\t139 EAST   27 STREET\t10016\n32392645\t01/11/2016 09:53:32 PM\tHEAT/HOT WATER\tBRONX\t1520 SEDGWICK AVENUE\t10453\n32392423\t01/11/2016 09:52:24 PM\tStreet Condition\tBROOKLYN\t\t11211\n32391233\t01/11/2016 09:52:24 PM\tTaxi Complaint\tBROOKLYN\t5 EAST 93 STREET\t11212\n32392511\t01/11/2016 09:51:01 PM\tHEAT/HOT WATER\tQUEENS\t96-02 57 AVENUE\t11368\n32393686\t01/11/2016 09:50:46 PM\tHEAT/HOT WATER\tMANHATTAN\t2 ELLWOOD STREET\t10040\n32392494\t01/11/2016 09:50:44 PM\tHEAT/HOT WATER\tBRONX\t941 LEGGETT AVENUE\t10455\n32392813\t01/11/2016 09:50:40 PM\tDOOR/WINDOW\tSTATEN ISLAND\t133 ARLINGTON PLACE\t10303\n32391935\t01/11/2016 09:50:40 PM\tUNSANITARY CONDITION\tSTATEN ISLAND\t133 ARLINGTON PLACE\t10303\n32391341\t01/11/2016 09:50:40 PM\tHEAT/HOT WATER\tSTATEN ISLAND\t133 ARLINGTON PLACE\t10303\n32386837\t01/11/2016 09:49:43 PM\tHEAT/HOT WATER\tBROOKLYN\t57 NEWEL STREET\t11222\n32388054\t01/11/2016 09:49:35 PM\tHEAT/HOT WATER\tQUEENS\t41-50 48 STREET\t11104\n32391747\t01/11/2016 09:49:21 PM\tIllegal Parking\tBROOKLYN\t\t11234\n32388404\t01/11/2016 09:49:00 PM\tNoise\tMANHATTAN\t\t10028\n32394946\t01/11/2016 09:48:43 PM\tHEAT/HOT WATER\tMANHATTAN\t725 WEST  172 STREET\t10032\n32391363\t01/11/2016 09:48:38 PM\tHEAT/HOT WATER\tMANHATTAN\t166 WEST  123 STREET\t10027\n32388018\t01/11/2016 09:48:35 PM\tHEAT/HOT WATER\tQUEENS\t42-60 MAIN STREET\t11355\n32386072\t01/11/2016 09:48:32 PM\tAPPLIANCE\tBRONX\t3021 HOLLAND AVENUE\t10467\n32390080\t01/11/2016 09:48:32 PM\tGENERAL\tBRONX\t3021 HOLLAND AVENUE\t10467\n32387589\t01/11/2016 09:48:32 PM\tAPPLIANCE\tBRONX\t3021 HOLLAND AVENUE\t10467\n32391961\t01/11/2016 09:48:32 PM\tUNSANITARY CONDITION\tBRONX\t3021 HOLLAND AVENUE\t10467\n32392528\t01/11/2016 09:48:32 PM\tHEAT/HOT WATER\tBRONX\t3021 HOLLAND AVENUE\t10467\n32391543\t01/11/2016 09:48:31 PM\tHEAT/HOT WATER\tBRONX\t1279 REV JAMES POLITE AVENUE\t10459\n32386723\t01/11/2016 09:48:27 PM\tHEAT/HOT WATER\tBRONX\t3605 SEDGWICK AVENUE\t10463\n32386981\t01/11/2016 09:48:10 PM\tIllegal Parking\tQUEENS\t\t11106\n32387912\t01/11/2016 09:48:01 PM\tHEAT/HOT WATER\tQUEENS\t72-10 41 AVENUE\t11377\n32388365\t01/11/2016 09:48:00 PM\tNoise\tMANHATTAN\t118 WEST   17 STREET\t10011\n32390131\t01/11/2016 09:47:49 PM\tStreet Condition\tMANHATTAN\t2 COLUMBUS AVENUE\t10019\n32393407\t01/11/2016 09:47:09 PM\tBlocked Driveway\tQUEENS\t62-44 99 STREET\t11374\n32388791\t01/11/2016 09:47:07 PM\tBlocked Driveway\tBRONX\t2676 HEATH AVENUE\t10463\n32389043\t01/11/2016 09:47:00 PM\tStreet Light Condition\tQUEENS\t\t11004\n32390316\t01/11/2016 09:45:29 PM\tHEAT/HOT WATER\tMANHATTAN\t725 WEST  172 STREET\t10032\n32387869\t01/11/2016 09:45:05 PM\tHEAT/HOT WATER\tMANHATTAN\t2376 AMSTERDAM AVENUE\t10033\n32386250\t01/11/2016 09:45:00 PM\tWater System\tQUEENS\t20-17 37 STREET\t11105\n32386675\t01/11/2016 09:44:42 PM\tOpinion for the Mayor\tUnspecified\t\t\n32389005\t01/11/2016 09:44:14 PM\tHEAT/HOT WATER\tMANHATTAN\t170 LENOX AVENUE\t10026\n32389565\t01/11/2016 09:44:00 PM\tNoise\tMANHATTAN\t20 EAST   43 STREET\t10017\n32394373\t01/11/2016 09:44:00 PM\tWater System\tQUEENS\t74-02 64 LANE\t11385\n32385900\t01/11/2016 09:43:46 PM\tIllegal Parking\tBROOKLYN\t112 SULLIVAN STREET\t11231\n32389144\t01/11/2016 09:43:25 PM\tHEAT/HOT WATER\tBRONX\t1500 GRAND CONCOURSE\t10457\n32392749\t01/11/2016 09:43:23 PM\tHEAT/HOT WATER\tMANHATTAN\t530 WEST  163 STREET\t10032\n32389923\t01/11/2016 09:43:19 PM\tBenefit Card Replacement\tUnspecified\t\t\n32394819\t01/11/2016 09:43:04 PM\tHEAT/HOT WATER\tMANHATTAN\t4791 BROADWAY\t10034\n32388405\t01/11/2016 09:43:00 PM\tNoise\tMANHATTAN\t\t10022\n32391165\t01/11/2016 09:42:05 PM\tIllegal Animal Kept as Pet\tBRONX\t2074 ARTHUR AVENUE\t10457\n32390878\t01/11/2016 09:41:54 PM\tOvergrown Tree/Branches\tBROOKLYN\t\t11215\n32389147\t01/11/2016 09:41:03 PM\tHEAT/HOT WATER\tMANHATTAN\t109 SHERMAN AVENUE\t10034\n32390391\t01/11/2016 09:40:59 PM\tHEAT/HOT WATER\tBROOKLYN\t437 DEWITT AVENUE\t11207\n32389376\t01/11/2016 09:40:38 PM\tIllegal Parking\tQUEENS\t63-05 BORDEN AVENUE\t11378\n32395045\t01/11/2016 09:40:13 PM\tHEAT/HOT WATER\tBROOKLYN\t706 LEFFERTS AVENUE\t11203\n32389395\t01/11/2016 09:39:56 PM\tIllegal Parking\tBROOKLYN\t\t11214\n32393196\t01/11/2016 09:39:04 PM\tBenefit Card Replacement\tUnspecified\t\t\n32389048\t01/11/2016 09:39:00 PM\tStreet Light Condition\tMANHATTAN\t\t\n32388008\t01/11/2016 09:38:30 PM\tHEAT/HOT WATER\tMANHATTAN\t4791 BROADWAY\t10034\n32389148\t01/11/2016 09:38:26 PM\tHEAT/HOT WATER\tBROOKLYN\t972A ST JOHNS PLACE\t11213\n32395040\t01/11/2016 09:38:16 PM\tHEAT/HOT WATER\tBRONX\t3021 HOLLAND AVENUE\t10467\n32391798\t01/11/2016 09:38:15 PM\tRodent\tBRONX\t950 WOODYCREST AVENUE\t10452\n32392227\t01/11/2016 09:38:15 PM\tBlocked Driveway\tBROOKLYN\t2849 BRIGHTON 6 STREET\t11235\n32389145\t01/11/2016 09:38:00 PM\tHEAT/HOT WATER\tBRONX\t1255 STRATFORD AVENUE\t10472\n32394355\t01/11/2016 09:38:00 PM\tWater System\tQUEENS\t135-05 20 AVENUE\t11356\n32394942\t01/11/2016 09:37:41 PM\tHEAT/HOT WATER\tMANHATTAN\t441 CONVENT AVENUE\t10031\n32387588\t01/11/2016 09:37:00 PM\tNoise\tMANHATTAN\t1204 BROADWAY\t10001\n32393450\t01/11/2016 09:36:51 PM\tBlocked Driveway\tQUEENS\t55-35 97 STREET\t11368\n32389888\t01/11/2016 09:36:51 PM\tBlocked Driveway\tBROOKLYN\t1240 EAST 34 STREET\t11210\n32386717\t01/11/2016 09:36:31 PM\tHEAT/HOT WATER\tBRONX\t261 EAST KINGSBRIDGE ROAD\t10458\n32392716\t01/11/2016 09:36:13 PM\tHEAT/HOT WATER\tBRONX\t647 MANIDA STREET\t10474\n32390568\t01/11/2016 09:35:58 PM\tIllegal Parking\tBROOKLYN\t67 MANHATTAN COURT\t11235\n32391370\t01/11/2016 09:35:32 PM\tHEAT/HOT WATER\tBRONX\t1314 LAFAYETTE AVENUE\t10474\n32390723\t01/11/2016 09:35:29 PM\tTaxi Report\tQUEENS\t122 82 ROAD\t11415\n32392503\t01/11/2016 09:35:18 PM\tHEAT/HOT WATER\tMANHATTAN\t71 ST NICHOLAS AVENUE\t10026\n32394272\t01/11/2016 09:35:00 PM\tStreet Light Condition\tQUEENS\t35-21 81 STREET\t11372\n32390694\t01/11/2016 09:35:00 PM\tNoise\tMANHATTAN\t\t10022\n32387624\t01/11/2016 09:34:26 PM\tELECTRIC\tBROOKLYN\t87 EUCLID AVENUE\t11208\n32386479\t01/11/2016 09:33:21 PM\tBlocked Driveway\tBROOKLYN\t114 FORREST STREET\t11206\n32393840\t01/11/2016 09:33:11 PM\tHEAT/HOT WATER\tBRONX\t1134 WEST FARMS ROAD\t10459\n32386887\t01/11/2016 09:33:04 PM\tHEAT/HOT WATER\tBRONX\t1650 HUTCH RIVER PARKWAY EAST\t10461\n32394237\t01/11/2016 09:33:00 PM\tNoise\tMANHATTAN\t350 EAST   53 STREET\t10022\n32391053\t01/11/2016 09:32:55 PM\tBlocked Driveway\tQUEENS\t61-16 134 STREET\t11367\n32390685\t01/11/2016 09:32:44 PM\tPosting Advertisement\tSTATEN ISLAND\t181 LAREDO AVENUE\t10312\n32391325\t01/11/2016 09:32:05 PM\tHEAT/HOT WATER\tQUEENS\t23-42 100 STREET\t11369\n32393892\t01/11/2016 09:32:04 PM\tHEAT/HOT WATER\tBRONX\t2968 PERRY AVENUE\t10458\n32386063\t01/11/2016 09:31:59 PM\tNoise - Vehicle\tMANHATTAN\t\t10026\n32394145\t01/11/2016 09:31:31 PM\tRodent\tBROOKLYN\t147 GEORGE STREET\t11237\n32387859\t01/11/2016 09:31:00 PM\tSnow\tBRONX\t\t10468\n32391453\t01/11/2016 09:30:52 PM\tHEAT/HOT WATER\tBROOKLYN\t186 RUSSELL STREET\t11222\n32388297\t01/11/2016 09:30:43 PM\tPosting Advertisement\tSTATEN ISLAND\t181 LAREDO AVENUE\t10312\n32388810\t01/11/2016 09:30:00 PM\tNoise\tMANHATTAN\t93 AVENUE B\t10009\n32386558\t01/11/2016 09:29:52 PM\tELECTRIC\tQUEENS\t107-51 156 STREET\t11433\n32387988\t01/11/2016 09:29:51 PM\tHEAT/HOT WATER\tBRONX\t1134 WEST FARMS ROAD\t10459\n32395075\t01/11/2016 09:29:46 PM\tPAINT/PLASTER\tQUEENS\t36-14 28 AVENUE\t11103\n32388102\t01/11/2016 09:29:46 PM\tPAINT/PLASTER\tQUEENS\t36-14 28 AVENUE\t11103\n32393649\t01/11/2016 09:29:46 PM\tFLOORING/STAIRS\tQUEENS\t36-14 28 AVENUE\t11103\n32388963\t01/11/2016 09:29:46 PM\tFLOORING/STAIRS\tQUEENS\t36-14 28 AVENUE\t11103\n32388571\t01/11/2016 09:29:45 PM\tWATER LEAK\tBROOKLYN\t964 EASTERN PARKWAY\t11213\n32388167\t01/11/2016 09:29:42 PM\tSmoking\tBRONX\t3035 WHITE PLAINS ROAD\t10467\n32386082\t01/11/2016 09:29:12 PM\tStreet Condition\tBROOKLYN\t809 NEPTUNE AVENUE\t11224\n32389586\t01/11/2016 09:29:00 PM\tStreet Light Condition\tMANHATTAN\t360 RIVERSIDE DRIVE\t10025\n32391907\t01/11/2016 09:29:00 PM\tNoise\tMANHATTAN\t971 1 AVENUE\t10022\n32391854\t01/11/2016 09:29:00 PM\tNoise\tBROOKLYN\t\t11201\n32389516\t01/11/2016 09:28:55 PM\tNoise - Street/Sidewalk\tBRONX\t2961 MARION AVENUE\t10458\n32386044\t01/11/2016 09:28:51 PM\tPosting Advertisement\tSTATEN ISLAND\t177 LAREDO AVENUE\t10312\n32387898\t01/11/2016 09:28:39 PM\tHEAT/HOT WATER\tQUEENS\t168-64 93 AVENUE\t11433\n32386034\t01/11/2016 09:28:36 PM\tSAFETY\tBRONX\t1723 VAN BUREN STREET\t10460\n32391173\t01/11/2016 09:28:25 PM\tDOF Literature Request\tUnspecified\t\t\n32394063\t01/11/2016 09:28:23 PM\tIllegal Parking\tBROOKLYN\t416 EAST 2 STREET\t11218\n32391645\t01/11/2016 09:28:09 PM\tHomeless Person Assistance\tBROOKLYN\tPITKIN AVENUE\t11208\n32387421\t01/11/2016 09:28:03 PM\tNoise - Vehicle\tMANHATTAN\t324 WEST 4 STREET\t10014\n32390831\t01/11/2016 09:27:57 PM\tWATER LEAK\tBRONX\t1142 BRYANT AVENUE\t10459\n32394980\t01/11/2016 09:27:43 PM\tHEAT/HOT WATER\tQUEENS\t144-76 SANFORD AVENUE\t11355\n32392505\t01/11/2016 09:27:35 PM\tHEAT/HOT WATER\tMANHATTAN\t307 WEST  143 STREET\t10030\n32391879\t01/11/2016 09:27:00 PM\tNoise\tMANHATTAN\t\t10005\n32391844\t01/11/2016 09:26:56 PM\tPosting Advertisement\tSTATEN ISLAND\t177 LAREDO AVENUE\t10312\n32394971\t01/11/2016 09:26:42 PM\tHEAT/HOT WATER\tBRONX\t1265 OLMSTEAD AVENUE\t10462\n32390001\t01/11/2016 09:26:30 PM\tDOOR/WINDOW\tQUEENS\t103-29 107 STREET\t11417\n32389462\t01/11/2016 09:26:30 PM\tSAFETY\tQUEENS\t103-29 107 STREET\t11417\n32388898\t01/11/2016 09:26:30 PM\tELECTRIC\tQUEENS\t103-29 107 STREET\t11417\n32393561\t01/11/2016 09:26:30 PM\tFLOORING/STAIRS\tQUEENS\t103-29 107 STREET\t11417\n32394325\t01/11/2016 09:26:30 PM\tUNSANITARY CONDITION\tQUEENS\t103-29 107 STREET\t11417\n32388876\t01/11/2016 09:26:30 PM\tDOOR/WINDOW\tQUEENS\t103-29 107 STREET\t11417\n32388470\t01/11/2016 09:26:30 PM\tSAFETY\tQUEENS\t103-29 107 STREET\t11417\n32388460\t01/11/2016 09:26:30 PM\tSAFETY\tQUEENS\t103-29 107 STREET\t11417\n32386171\t01/11/2016 09:26:30 PM\tUNSANITARY CONDITION\tQUEENS\t103-29 107 STREET\t11417\n32386733\t01/11/2016 09:26:15 PM\tHEAT/HOT WATER\tBRONX\t1064 CARROLL PLACE\t10456\n32387578\t01/11/2016 09:25:57 PM\tConsumer Complaint\tUnspecified\t13 MIDLAND AVENUE\t11801\n32391927\t01/11/2016 09:25:49 PM\tStreet Condition\tBROOKLYN\t2845 CONEY ISLAND AVENUE\t11235\n32393909\t01/11/2016 09:25:30 PM\tHEAT/HOT WATER\tBRONX\t258 SOUNDVIEW AVENUE\t10473\n32389907\t01/11/2016 09:25:25 PM\tBlocked Driveway\tBROOKLYN\t554 GATES AVENUE\t11221\n32386890\t01/11/2016 09:25:25 PM\tHEAT/HOT WATER\tMANHATTAN\t51 EAST  129 STREET\t10035\n32388239\t01/11/2016 09:24:22 PM\tIllegal Parking\tQUEENS\t95-03 86 STREET\t11416\n32394829\t01/11/2016 09:24:15 PM\tHEAT/HOT WATER\tBRONX\t1763 MONTGOMERY AVENUE\t10453\n32386952\t01/11/2016 09:24:13 PM\tPAINT/PLASTER\tBRONX\t1723 VAN BUREN STREET\t10460\n32390724\t01/11/2016 09:24:00 PM\tNoise\tMANHATTAN\t\t10022\n32392693\t01/11/2016 09:23:22 PM\tHEAT/HOT WATER\tBROOKLYN\t1355 NEW YORK AVENUE\t11203\n32389390\t01/11/2016 09:23:19 PM\tIllegal Parking\tSTATEN ISLAND\t90 HUSSON STREET\t10305\n32390604\t01/11/2016 09:23:00 PM\tLead\tQUEENS\t170-43 PIDGEON MEADOW ROAD\t11365\n32389545\t01/11/2016 09:23:00 PM\tNoise\tBROOKLYN\tEAST   34 STREET\t11203\n32391737\t01/11/2016 09:22:58 PM\tIllegal Parking\tBROOKLYN\t411 EAST 2 STREET\t11218\n32394999\t01/11/2016 09:22:16 PM\tHEAT/HOT WATER\tBRONX\t1265 OLMSTEAD AVENUE\t10462\n32389153\t01/11/2016 09:22:07 PM\tHEAT/HOT WATER\tBRONX\t2269 HAMPDEN PLACE\t10468\n32386966\t01/11/2016 09:22:07 PM\tIllegal Parking\tQUEENS\t80-22 64 LANE\t11385\n32390502\t01/11/2016 09:22:00 PM\tDirty Conditions\tBRONX\t131 EAST GUNHILL ROAD\t10467\n32394821\t01/11/2016 09:21:37 PM\tHEAT/HOT WATER\tBRONX\t2025 SEWARD AVENUE\t10473\n32389717\t01/11/2016 09:20:58 PM\tGENERAL\tMANHATTAN\t534 EAST   14 STREET\t10009\n32392297\t01/11/2016 09:20:58 PM\tDOOR/WINDOW\tMANHATTAN\t534 EAST   14 STREET\t10009\n32390495\t01/11/2016 09:20:58 PM\tSAFETY\tMANHATTAN\t534 EAST   14 STREET\t10009\n32390790\t01/11/2016 09:20:55 PM\tUNSANITARY CONDITION\tBRONX\t2501 MORRIS AVENUE\t10468\n32390786\t01/11/2016 09:20:55 PM\tUNSANITARY CONDITION\tBRONX\t2501 MORRIS AVENUE\t10468\n32391470\t01/11/2016 09:20:55 PM\tHEAT/HOT WATER\tBRONX\t3910 SETON AVENUE\t10466\n32394706\t01/11/2016 09:20:55 PM\tFLOORING/STAIRS\tBRONX\t2501 MORRIS AVENUE\t10468\n32392489\t01/11/2016 09:20:55 PM\tHEAT/HOT WATER\tBRONX\t2501 MORRIS AVENUE\t10468\n32387006\t01/11/2016 09:20:55 PM\tDOOR/WINDOW\tBRONX\t2501 MORRIS AVENUE\t10468\n32388492\t01/11/2016 09:20:54 PM\tUNSANITARY CONDITION\tBRONX\t2501 MORRIS AVENUE\t10468\n32389293\t01/11/2016 09:20:54 PM\tPLUMBING\tBRONX\t2501 MORRIS AVENUE\t10468\n32386924\t01/11/2016 09:20:54 PM\tPAINT/PLASTER\tBRONX\t2501 MORRIS AVENUE\t10468\n32387666\t01/11/2016 09:20:54 PM\tFLOORING/STAIRS\tBRONX\t2501 MORRIS AVENUE\t10468\n32392729\t01/11/2016 09:20:52 PM\tHEAT/HOT WATER\tBRONX\t2308 HUGHES AVENUE\t10458\n32388052\t01/11/2016 09:20:41 PM\tHEAT/HOT WATER\tMANHATTAN\t767 9 AVENUE\t10019\n32394251\t01/11/2016 09:20:00 PM\tNoise\tMANHATTAN\t306 EAST   96 STREET\t10128\n32386135\t01/11/2016 09:20:00 PM\tNoise\tMANHATTAN\t353 EAST   53 STREET\t10022\n32389546\t01/11/2016 09:20:00 PM\tNoise\tBROOKLYN\t485 4 STREET\t11215\n32391806\t01/11/2016 09:19:29 PM\tNoise - Commercial\tBRONX\t647 EAST 180 STREET\t10457\n32386169\t01/11/2016 09:19:00 PM\tWater System\tQUEENS\t32-64 34 STREET\t11106\n32390377\t01/11/2016 09:18:41 PM\tHEAT/HOT WATER\tBRONX\t1650 HUTCH RIVER PARKWAY EAST\t10461\n32388127\t01/11/2016 09:18:39 PM\tPAINT/PLASTER\tBROOKLYN\t30 CLAVER PLACE\t11238\n32390472\t01/11/2016 09:18:39 PM\tPLUMBING\tBROOKLYN\t30 CLAVER PLACE\t11238\n32393136\t01/11/2016 09:18:39 PM\tUNSANITARY CONDITION\tBROOKLYN\t30 CLAVER PLACE\t11238\n32386014\t01/11/2016 09:18:00 PM\tNoise\tMANHATTAN\t315 EAST  102 STREET\t10029\n32391799\t01/11/2016 09:18:00 PM\tNoise\tMANHATTAN\t315 EAST  102 STREET\t10029\n32389775\t01/11/2016 09:18:00 PM\tDirty Conditions\tQUEENS\t64-35 102 STREET\t11374\n32392273\t01/11/2016 09:17:50 PM\tBlocked Driveway\tBROOKLYN\t8112 AVENUE K\t11236\n32388423\t01/11/2016 09:17:23 PM\tUNSANITARY CONDITION\tMANHATTAN\t17 POST AVENUE\t10034\n32392041\t01/11/2016 09:17:23 PM\tWATER LEAK\tMANHATTAN\t17 POST AVENUE\t10034\n32387369\t01/11/2016 09:17:23 PM\tWATER LEAK\tMANHATTAN\t17 POST AVENUE\t10034\n32389277\t01/11/2016 09:17:23 PM\tPAINT/PLASTER\tMANHATTAN\t17 POST AVENUE\t10034\n32389675\t01/11/2016 09:17:22 PM\tWATER LEAK\tQUEENS\t97-28 82 STREET\t11416\n32389572\t01/11/2016 09:17:00 PM\tNoise\tMANHATTAN\t\t10022\n32386085\t01/11/2016 09:17:00 PM\tNoise\tMANHATTAN\t\t10022\n32386726\t01/11/2016 09:16:19 PM\tHEAT/HOT WATER\tMANHATTAN\t3163 BROADWAY\t10027\n32385983\t01/11/2016 09:16:00 PM\tNoise\tQUEENS\t1823 GATES AVENUE\t11385\n32389547\t01/11/2016 09:16:00 PM\tNoise\tMANHATTAN\t\t10022\n32389375\t01/11/2016 09:15:46 PM\tIllegal Parking\tSTATEN ISLAND\tATLANTIC AVENUE\t10305\n32394804\t01/11/2016 09:15:42 PM\tBroken Muni Meter\tQUEENS\t\t11361\n32387548\t01/11/2016 09:15:24 PM\tDOOR/WINDOW\tBRONX\t3120 WILKINSON AVENUE\t10461\n32386017\t01/11/2016 09:15:15 PM\tNoise - Street/Sidewalk\tMANHATTAN\t411 EAST 53 STREET\t10022\n32390703\t01/11/2016 09:15:00 PM\tNoise\tMANHATTAN\t\t10022\n32389340\t01/11/2016 09:14:30 PM\tIllegal Parking\tQUEENS\t\t11429\n32389851\t01/11/2016 09:14:19 PM\tBlocked Driveway\tQUEENS\t35-11 105 STREET\t11368\n32388061\t01/11/2016 09:14:11 PM\tHEAT/HOT WATER\tBRONX\t3021 HOLLAND AVENUE\t10467\n32391326\t01/11/2016 09:13:19 PM\tHEAT/HOT WATER\tQUEENS\t91-31 LAMONT AVENUE\t11373\n32391273\t01/11/2016 09:13:15 PM\tStreet Condition\tBRONX\t3530 HENRY HUDSON PARKWAY\t10463\n32394255\t01/11/2016 09:13:00 PM\tNoise\tMANHATTAN\t\t10022\n32386741\t01/11/2016 09:13:00 PM\tStreet Light Condition\tBRONX\t\t\n32394702\t01/11/2016 09:12:56 PM\tGENERAL\tBRONX\t3120 WILKINSON AVENUE\t10461\n32388121\t01/11/2016 09:12:56 PM\tPAINT/PLASTER\tBRONX\t3120 WILKINSON AVENUE\t10461\n32386544\t01/11/2016 09:12:56 PM\tFLOORING/STAIRS\tBRONX\t3120 WILKINSON AVENUE\t10461\n32386173\t01/11/2016 09:12:56 PM\tUNSANITARY CONDITION\tBRONX\t3120 WILKINSON AVENUE\t10461\n32386951\t01/11/2016 09:12:55 PM\tPAINT/PLASTER\tBRONX\t3120 WILKINSON AVENUE\t10461\n32392421\t01/11/2016 09:12:55 PM\tGENERAL\tBRONX\t3120 WILKINSON AVENUE\t10461\n32395084\t01/11/2016 09:12:55 PM\tPAINT/PLASTER\tBRONX\t3120 WILKINSON AVENUE\t10461\n32386192\t01/11/2016 09:12:55 PM\tWATER LEAK\tBRONX\t3120 WILKINSON AVENUE\t10461\n32392772\t01/11/2016 09:12:55 PM\tPAINT/PLASTER\tBRONX\t3120 WILKINSON AVENUE\t10461\n32387762\t01/11/2016 09:12:55 PM\tFLOORING/STAIRS\tBRONX\t3120 WILKINSON AVENUE\t10461\n32393001\t01/11/2016 09:12:29 PM\tNoise - Street/Sidewalk\tQUEENS\t64-15 65 PLACE\t11379\n32386229\t01/11/2016 09:12:00 PM\tWater System\tBROOKLYN\t\t11220\n32387276\t01/11/2016 09:12:00 PM\tWater System\tQUEENS\t32-64 34 STREET\t11106\n32391381\t01/11/2016 09:11:38 PM\tHEAT/HOT WATER\tBROOKLYN\t7410 RIDGE BOULEVARD\t11209\n32391054\t01/11/2016 09:11:20 PM\tBlocked Driveway\tBROOKLYN\t541 88 STREET\t11209\n32393739\t01/11/2016 09:10:44 PM\tHEAT/HOT WATER\tBROOKLYN\t30 CLAVER PLACE\t11238\n32392527\t01/11/2016 09:10:36 PM\tHEAT/HOT WATER\tBROOKLYN\t570 RIDGEWOOD AVENUE\t11208\n32386526\t01/11/2016 09:10:36 PM\tAPPLIANCE\tBROOKLYN\t570 RIDGEWOOD AVENUE\t11208\n32390722\t01/11/2016 09:10:00 PM\tNoise\tMANHATTAN\t1 AVENUE\t10022\n32389498\t01/11/2016 09:10:00 PM\tNoise\tMANHATTAN\t1 AVENUE\t10022\n32394044\t01/11/2016 09:09:54 PM\tIllegal Parking\tBROOKLYN\t\t11236\n32389127\t01/11/2016 09:09:47 PM\tHEAT/HOT WATER\tBROOKLYN\t175 HAWTHORNE STREET\t11225\n32393901\t01/11/2016 09:09:25 PM\tHEAT/HOT WATER\tBROOKLYN\t258 12 STREET\t11215\n32389171\t01/11/2016 09:09:24 PM\tHEAT/HOT WATER\tMANHATTAN\t10 OVERLOOK TERRACE\t10033\n32386745\t01/11/2016 09:09:13 PM\tHEAT/HOT WATER\tBROOKLYN\t484 60 STREET\t11220\n32388955\t01/11/2016 09:09:13 PM\tGENERAL\tBROOKLYN\t484 60 STREET\t11220\n32393522\t01/11/2016 09:09:13 PM\tDOOR/WINDOW\tBROOKLYN\t484 60 STREET\t11220\n32390816\t01/11/2016 09:08:42 PM\tUNSANITARY CONDITION\tQUEENS\t103-29 107 STREET\t11417\n32392221\t01/11/2016 09:08:17 PM\tBenefit Card Replacement\tUnspecified\t\t\n32389394\t01/11/2016 09:08:04 PM\tIllegal Parking\tSTATEN ISLAND\t142 GURLEY AVENUE\t10308\n32388516\t01/11/2016 09:08:00 PM\tWater System\tQUEENS\t15-21 119 STREET\t11356\n32390303\t01/11/2016 09:07:44 PM\tHEAT/HOT WATER\tBRONX\t1025 BOYNTON AVENUE\t10472\n32385911\t01/11/2016 09:07:23 PM\tIllegal Parking\tQUEENS\t150-31 12 AVENUE\t11357\n32390124\t01/11/2016 09:07:09 PM\tStreet Sign - Damaged\tBROOKLYN\t\t11212\n32394959\t01/11/2016 09:07:05 PM\tHEAT/HOT WATER\tBRONX\t1030 BOYNTON AVENUE\t10472\n32391880\t01/11/2016 09:07:00 PM\tNoise\tMANHATTAN\t350 EAST   54 STREET\t10022\n32386794\t01/11/2016 09:06:57 PM\tHEAT/HOT WATER\tBROOKLYN\t520 EAST   21 STREET\t11226\n32385917\t01/11/2016 09:06:57 PM\tIllegal Parking\tSTATEN ISLAND\t142 GURLEY AVENUE\t10308\n32392663\t01/11/2016 09:06:39 PM\tHEAT/HOT WATER\tBROOKLYN\t2736 PITKIN AVENUE\t11208\n32386700\t01/11/2016 09:06:38 PM\tHEAT/HOT WATER\tBROOKLYN\t700 REMSEN AVENUE\t11236\n32392651\t01/11/2016 09:06:37 PM\tHEAT/HOT WATER\tBROOKLYN\t580 BRISTOL STREET\t11212\n32386819\t01/11/2016 09:06:33 PM\tHEAT/HOT WATER\tBRONX\t1025 BOYNTON AVENUE\t10472\n32386005\t01/11/2016 09:06:03 PM\tNoise - Commercial\tMANHATTAN\t1708 2 AVENUE\t10128\n32387925\t01/11/2016 09:06:00 PM\tStreet Light Condition\tSTATEN ISLAND\t\t10308\n32392048\t01/11/2016 09:05:42 PM\tSidewalk Condition\tMANHATTAN\t123 WEST 79 STREET\t10024\n32389542\t01/11/2016 09:04:48 PM\tMaintenance or Facility\tMANHATTAN\t\t10014\n32393142\t01/11/2016 09:04:45 PM\tUNSANITARY CONDITION\tMANHATTAN\t17 POST AVENUE\t10034\n32392545\t01/11/2016 09:04:42 PM\tHEAT/HOT WATER\tBROOKLYN\t437 JEFFERSON AVENUE\t11221\n32393828\t01/11/2016 09:04:38 PM\tHEAT/HOT WATER\tMANHATTAN\t75 CHRYSTIE STREET\t10002\n32390826\t01/11/2016 09:04:34 PM\tWATER LEAK\tBRONX\t185 MC CLELLAN STREET\t10456\n32392678\t01/11/2016 09:04:24 PM\tHEAT/HOT WATER\tQUEENS\t79-25 150 STREET\t11367\n32394955\t01/11/2016 09:04:22 PM\tHEAT/HOT WATER\tBRONX\t1030 BOYNTON AVENUE\t10472\n32392650\t01/11/2016 09:04:15 PM\tHEAT/HOT WATER\tQUEENS\t66-12 102 STREET\t11374\n32387141\t01/11/2016 09:04:00 PM\tNoise\tMANHATTAN\t\t10011\n32394238\t01/11/2016 09:04:00 PM\tNoise\tBROOKLYN\t542 5 AVENUE\t11215\n32392184\t01/11/2016 09:03:55 PM\tBlocked Driveway\tBRONX\t3168 ROCHAMBEAU AVENUE\t10467\n32393842\t01/11/2016 09:03:13 PM\tHEAT/HOT WATER\tMANHATTAN\t2548 FREDERICK DOUGLASS BOULEVARD\t10030\n32386163\t01/11/2016 09:03:00 PM\tSewer\tQUEENS\t95-02 35 AVENUE\t11372\n32389377\t01/11/2016 09:02:47 PM\tIllegal Parking\tQUEENS\t150-31 12 AVENUE\t11357\n32389224\t01/11/2016 09:02:20 PM\tHEAT/HOT WATER\tMANHATTAN\t109 ELDRIDGE STREET\t10002\n32387861\t01/11/2016 09:02:16 PM\tHEAT/HOT WATER\tBROOKLYN\t751 TROY AVENUE\t11203\n32392783\t01/11/2016 09:02:08 PM\tPAINT/PLASTER\tBROOKLYN\t190 IRVING AVENUE\t11237\n32390733\t01/11/2016 09:02:08 PM\tAPPLIANCE\tBROOKLYN\t190 IRVING AVENUE\t11237\n32392315\t01/11/2016 09:02:08 PM\tDOOR/WINDOW\tBROOKLYN\t190 IRVING AVENUE\t11237\n32388786\t01/11/2016 09:02:08 PM\tDOOR/WINDOW\tBROOKLYN\t190 IRVING AVENUE\t11237\n32387894\t01/11/2016 09:02:06 PM\tHEAT/HOT WATER\tQUEENS\t31-09 74 STREET\t11370\n32387787\t01/11/2016 09:01:47 PM\tGENERAL\tMANHATTAN\t10 WEST  135 STREET\t10037\n32388722\t01/11/2016 09:01:47 PM\tAPPLIANCE\tMANHATTAN\t10 WEST  135 STREET\t10037\n32393200\t01/11/2016 09:01:22 PM\tBenefit Card Replacement\tUnspecified\t\t\n32385907\t01/11/2016 09:01:14 PM\tIllegal Parking\tMANHATTAN\t\t10021\n32392611\t01/11/2016 09:01:06 PM\tHEAT/HOT WATER\tBRONX\t1030 BOYNTON AVENUE\t10472\n32391389\t01/11/2016 09:01:01 PM\tHEAT/HOT WATER\tQUEENS\t97-28 82 STREET\t11416\n32394495\t01/11/2016 09:01:00 PM\tLitter Basket / Request\tMANHATTAN\t\t10032\n32388387\t01/11/2016 09:00:50 PM\tNoise - Street/Sidewalk\tBROOKLYN\t126 QUINCY STREET\t11216\n32389162\t01/11/2016 09:00:07 PM\tHEAT/HOT WATER\tBROOKLYN\t302 COOPER STREET\t11237\n32393915\t01/11/2016 09:00:05 PM\tHEAT/HOT WATER\tBRONX\t185 MC CLELLAN STREET\t10456\n32386800\t01/11/2016 08:59:57 PM\tHEAT/HOT WATER\tBROOKLYN\t738 EAST   45 STREET\t11203\n32389222\t01/11/2016 08:59:56 PM\tHEAT/HOT WATER\tMANHATTAN\t421 AUDUBON AVENUE\t10033\n32387600\t01/11/2016 08:59:52 PM\tDOOR/WINDOW\tQUEENS\t84-38 CHARLECOTE RIDGE\t11432\n32394298\t01/11/2016 08:59:52 PM\tPLUMBING\tQUEENS\t84-38 CHARLECOTE RIDGE\t11432\n32393510\t01/11/2016 08:59:52 PM\tDOOR/WINDOW\tQUEENS\t84-38 CHARLECOTE RIDGE\t11432\n32393103\t01/11/2016 08:59:52 PM\tPLUMBING\tQUEENS\t84-38 CHARLECOTE RIDGE\t11432\n32394881\t01/11/2016 08:59:52 PM\tHEAT/HOT WATER\tQUEENS\t84-38 CHARLECOTE RIDGE\t11432\n32390409\t01/11/2016 08:59:52 PM\tPAINT/PLASTER\tQUEENS\t84-38 CHARLECOTE RIDGE\t11432\n32393992\t01/11/2016 08:59:52 PM\tPAINT/PLASTER\tQUEENS\t84-38 CHARLECOTE RIDGE\t11432\n32386473\t01/11/2016 08:59:52 PM\tDOOR/WINDOW\tQUEENS\t84-38 CHARLECOTE RIDGE\t11432\n32394053\t01/11/2016 08:59:23 PM\tIllegal Parking\tUnspecified\t272 31ST STREET\t\n32393821\t01/11/2016 08:59:17 PM\tHEAT/HOT WATER\tMANHATTAN\t1610 ST NICHOLAS AVENUE\t10040\n32387275\t01/11/2016 08:58:50 PM\tUNSANITARY CONDITION\tMANHATTAN\t210 WEST  107 STREET\t10025\n32390716\t01/11/2016 08:58:29 PM\tTaxi Complaint\tMANHATTAN\t442 WEST 57 STREET\t10019\n32389329\t01/11/2016 08:58:17 PM\tIllegal Parking\tBROOKLYN\t15 LENOX ROAD\t11226\n32394726\t01/11/2016 08:58:03 PM\tTaxi Complaint\tBROOKLYN\t7401 4 AVENUE\t11209\n32386857\t01/11/2016 08:57:55 PM\tHEAT/HOT WATER\tBRONX\t1030 BOYNTON AVENUE\t10472\n32392531\t01/11/2016 08:57:48 PM\tHEAT/HOT WATER\tBROOKLYN\t170 EAST    4 STREET\t11218\n32390318\t01/11/2016 08:57:46 PM\tHEAT/HOT WATER\tBRONX\t108 EAST  176 STREET\t10453\n32388177\t01/11/2016 08:57:15 PM\tIllegal Parking\tQUEENS\t66-26 GARFIELD AVENUE\t11377\n32394970\t01/11/2016 08:57:03 PM\tHEAT/HOT WATER\tBRONX\t1418 ZEREGA AVENUE\t10462\n32389796\t01/11/2016 08:57:00 PM\tDirty Conditions\tSTATEN ISLAND\t308 MALLORY AVENUE\t10305\n32393781\t01/11/2016 08:57:00 PM\tStreet Light Condition\tMANHATTAN\t\t10002\n32389902\t01/11/2016 08:56:48 PM\tBlocked Driveway\tQUEENS\t41-01 BENHAM STREET\t11373\n32388411\t01/11/2016 08:56:00 PM\tNoise\tBROOKLYN\t190 IRVING AVENUE\t11237\n32390824\t01/11/2016 08:55:43 PM\tUNSANITARY CONDITION\tMANHATTAN\t715 EAST    5 STREET\t10009\n32387995\t01/11/2016 08:55:39 PM\tHEAT/HOT WATER\tMANHATTAN\t1700 LEXINGTON AVENUE\t10029\n32390311\t01/11/2016 08:55:38 PM\tHEAT/HOT WATER\tBRONX\t1025 BOYNTON AVENUE\t10472\n32390768\t01/11/2016 08:55:28 PM\tUNSANITARY CONDITION\tSTATEN ISLAND\t16 DOE PLACE\t10310\n32386046\t01/11/2016 08:55:19 PM\tTaxi Complaint\tMANHATTAN\t\t10032\n32392838\t01/11/2016 08:55:00 PM\tDirty Conditions\tQUEENS\t77-12 65 STREET\t11385\n32392682\t01/11/2016 08:54:28 PM\tHEAT/HOT WATER\tBRONX\t1030 BOYNTON AVENUE\t10472\n32387606\t01/11/2016 08:53:51 PM\tBlocked Driveway\tQUEENS\t1800 HIMROD STREET\t11385\n32386242\t01/11/2016 08:53:26 PM\tTaxi Report\tMANHATTAN\t39 WHITEHALL STREET\t10004\n32392311\t01/11/2016 08:53:23 PM\tDOOR/WINDOW\tQUEENS\t31-14 42 STREET\t11103\n32389405\t01/11/2016 08:52:31 PM\tIllegal Parking\tQUEENS\t1720 LINDEN STREET\t11385\n32386785\t01/11/2016 08:52:27 PM\tHEAT/HOT WATER\tQUEENS\t94-06 34 AVENUE\t11372\n32391544\t01/11/2016 08:52:24 PM\tHEAT/HOT WATER\tBROOKLYN\t4710 AVENUE D\t11203\n32395056\t01/11/2016 08:52:14 PM\tHEAT/HOT WATER\tMANHATTAN\t2010 LEXINGTON AVENUE\t10035\n32387045\t01/11/2016 08:52:13 PM\tIllegal Parking\tBROOKLYN\t278 BAY 14 STREET\t11214\n32394992\t01/11/2016 08:52:13 PM\tHEAT/HOT WATER\tBRONX\t967 EAST  224 STREET\t10466\n32386791\t01/11/2016 08:52:12 PM\tHEAT/HOT WATER\tBROOKLYN\t893 ST JOHNS PLACE\t11216\n32391379\t01/11/2016 08:51:10 PM\tHEAT/HOT WATER\tMANHATTAN\t67 MACOMBS PLACE\t10039\n32389118\t01/11/2016 08:51:04 PM\tHEAT/HOT WATER\tMANHATTAN\t167 EAST  102 STREET\t10029\n32386183\t01/11/2016 08:50:00 PM\tWater System\tQUEENS\t32-17 33 STREET\t11106\n32392890\t01/11/2016 08:49:53 PM\tAnimal Abuse\tBROOKLYN\t6663 BERGEN PLACE\t11220\n32389034\t01/11/2016 08:49:48 PM\tHEAT/HOT WATER\tBRONX\t2080 GRAND CONCOURSE\t10457\n32385851\t01/11/2016 08:49:33 PM\tIllegal Parking\tBROOKLYN\t1916 54 STREET\t11204\n32393693\t01/11/2016 08:49:29 PM\tHEAT/HOT WATER\tBROOKLYN\t12 PLEASANT PLACE\t11233\n32390366\t01/11/2016 08:49:13 PM\tHEAT/HOT WATER\tBROOKLYN\t1680 EAST   22 STREET\t11229\n32394338\t01/11/2016 08:49:00 PM\tSewer\tBRONX\t2200 MORRIS AVENUE\t10453\n32386943\t01/11/2016 08:48:49 PM\tRodent\tMANHATTAN\t715 EAST 5 STREET\t10009\n32386631\t01/11/2016 08:48:24 PM\tGENERAL\tSTATEN ISLAND\t16 DOE PLACE\t10310\n32394721\t01/11/2016 08:48:17 PM\tDamaged Tree\tBROOKLYN\t1687 EAST 2 STREET\t11223\n32391032\t01/11/2016 08:47:53 PM\tBlocked Driveway\tBRONX\t1639 UNDERCLIFF AVENUE\t10453\n32391009\t01/11/2016 08:47:50 PM\tBlocked Driveway\tBROOKLYN\t2160 80 STREET\t11214\n32389487\t01/11/2016 08:47:28 PM\tRodent\tMANHATTAN\t360 WEST 22 STREET\t10011\n32390280\t01/11/2016 08:47:23 PM\tHEAT/HOT WATER\tBROOKLYN\t57 NEWEL STREET\t11222\n32388256\t01/11/2016 08:47:15 PM\tIllegal Parking\tMANHATTAN\t120 WEST 57 STREET\t10019\n32387196\t01/11/2016 08:47:00 PM\tDerelict Vehicles\tBROOKLYN\t155 NICHOLS AVENUE\t11208\n32386410\t01/11/2016 08:46:57 PM\tBlocked Driveway\tBROOKLYN\t8800 BAY PARKWAY\t11214\n32386279\t01/11/2016 08:46:00 PM\tDirty Conditions\tMANHATTAN\t\t10014\n32388299\t01/11/2016 08:46:00 PM\tTraffic Signal Condition\tBRONX\t\t\n32389128\t01/11/2016 08:45:46 PM\tHEAT/HOT WATER\tBRONX\t2081 CRUGER AVENUE\t10462\n32386696\t01/11/2016 08:45:38 PM\tHEAT/HOT WATER\tBROOKLYN\t77 SULLIVAN PLACE\t11225\n32393860\t01/11/2016 08:45:35 PM\tHEAT/HOT WATER\tBROOKLYN\t62 NEW YORK AVENUE\t11216\n32387983\t01/11/2016 08:45:17 PM\tHEAT/HOT WATER\tBRONX\t1025 BOYNTON AVENUE\t10472\n32387250\t01/11/2016 08:45:00 PM\tStreet Light Condition\tBRONX\t3151 ARNOW PLACE\t10461\n32394085\t01/11/2016 08:45:00 PM\tTraffic Signal Condition\tMANHATTAN\t\t\n32394143\t01/11/2016 08:44:48 PM\tNoise - Commercial\tBROOKLYN\t606 DEGRAW STREET\t11217\n32388074\t01/11/2016 08:44:41 PM\tHEAT/HOT WATER\tBRONX\t2201 DAVIDSON AVENUE\t10453\n32394843\t01/11/2016 08:44:06 PM\tHEAT/HOT WATER\tBROOKLYN\t163 COOPER STREET\t11207\n32393916\t01/11/2016 08:43:39 PM\tHEAT/HOT WATER\tBROOKLYN\t1751 UNION STREET\t11213\n32393615\t01/11/2016 08:43:34 PM\tGENERAL\tMANHATTAN\t338 EAST  100 STREET\t10029\n32387534\t01/11/2016 08:43:07 PM\tBlocked Driveway\tQUEENS\t60-35 CATALPA AVENUE\t11385\n32386056\t01/11/2016 08:43:02 PM\tNoise - Street/Sidewalk\tBROOKLYN\t461 SAINT JOHNS PLACE\t11238\n32387554\t01/11/2016 08:43:02 PM\tRoot/Sewer/Sidewalk Condition\tQUEENS\t133-31 233 STREET\t11422\n32391401\t01/11/2016 08:43:00 PM\tStreet Light Condition\tQUEENS\t\t11366\n32394877\t01/11/2016 08:42:56 PM\tHEAT/HOT WATER\tBRONX\t1030 BOYNTON AVENUE\t10472\n32391588\t01/11/2016 08:42:33 PM\tPAINT/PLASTER\tQUEENS\t103-29 107 STREET\t11417\n32393818\t01/11/2016 08:42:32 PM\tHEAT/HOT WATER\tQUEENS\t48-50 37 STREET\t11101\n32391505\t01/11/2016 08:42:29 PM\tHEAT/HOT WATER\tBRONX\t2201 DAVIDSON AVENUE\t10453\n32393417\t01/11/2016 08:42:06 PM\tBlocked Driveway\tBRONX\t774 ADEE AVENUE\t10467\n32391680\t01/11/2016 08:42:00 PM\tTraffic Signal Condition\tBROOKLYN\t\t11235\n32391336\t01/11/2016 08:41:54 PM\tHEAT/HOT WATER\tBROOKLYN\t225 PARKSIDE AVENUE\t11226\n32386738\t01/11/2016 08:41:18 PM\tHEAT/HOT WATER\tBROOKLYN\t9325 KINGS HIGHWAY\t11212\n32389449\t01/11/2016 08:41:05 PM\tNoise - Vehicle\tBROOKLYN\t127 QUINCY STREET\t11216\n32393607\t01/11/2016 08:41:00 PM\tWater System\tQUEENS\t32-56 87 STREET\t11369\n\n\u003cfont color\u003dred\u003eResults are limited by 1000.\u003c/font\u003e"
      },
      "dateCreated": "Feb 2, 2016 3:56:33 PM",
      "dateStarted": "Feb 11, 2016 3:30:48 PM",
      "dateFinished": "Feb 11, 2016 3:30:51 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "So which are the top complaints in New York City ?",
      "text": "%sql select `Complaint Type` as Complaint_Type, count(`Unique Key`) as Complaint_Count from nyc311f group by `Complaint Type` order by count(`Unique Key`) desc limit 5",
      "dateUpdated": "Feb 11, 2016 7:53:19 PM",
      "config": {
        "colWidth": 12.0,
        "editorMode": "ace/mode/sql",
        "title": true,
        "graph": {
          "mode": "multiBarChart",
          "height": 300.0,
          "optionOpen": false,
          "keys": [
            {
              "name": "Complaint_Type",
              "index": 0.0,
              "aggr": "sum"
            }
          ],
          "values": [
            {
              "name": "Complaint_Count",
              "index": 1.0,
              "aggr": "sum"
            }
          ],
          "groups": [],
          "scatter": {
            "xAxis": {
              "name": "Complaint_Type",
              "index": 0.0,
              "aggr": "sum"
            },
            "yAxis": {
              "name": "Complaint_Count",
              "index": 1.0,
              "aggr": "sum"
            }
          }
        },
        "tableHide": false
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1445790858232_1646604584",
      "id": "20151025-163418_666503094",
      "result": {
        "code": "SUCCESS",
        "type": "TABLE",
        "msg": "Complaint_Type\tComplaint_Count\nHEAT/HOT WATER\t134880\nStreet Condition\t100368\nBlocked Driveway\t84560\nStreet Light Condition\t83442\nIllegal Parking\t81940\n"
      },
      "dateCreated": "Oct 25, 2015 4:34:18 PM",
      "dateStarted": "Feb 11, 2016 7:52:59 PM",
      "dateFinished": "Feb 11, 2016 7:53:02 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Let us see what is Heating Complaints Count Zip Code wise on every day basis",
      "text": "%sql \nselect split(`Created Date`, \u0027 \u0027)[0] as Incident_Date, `Incident Zip` as Incident_Zip, count(`Unique Key`) as HeatingComplaintCount  from nyc311f where `Complaint Type` \u003d \"HEAT/HOT WATER\" group by split(`Created Date`, \u0027 \u0027)[0], `Incident Zip` order by count(`Unique Key`) desc limit 10",
      "dateUpdated": "Feb 11, 2016 7:55:33 PM",
      "config": {
        "colWidth": 12.0,
        "editorMode": "ace/mode/sql",
        "tableHide": false,
        "title": true,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [
            {
              "name": "Incident_Date",
              "index": 0.0,
              "aggr": "sum"
            }
          ],
          "values": [],
          "groups": [],
          "scatter": {
            "xAxis": {
              "name": "Incident_Date",
              "index": 0.0,
              "aggr": "sum"
            }
          }
        }
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1447255557280_1639488244",
      "id": "20151111-152557_272898361",
      "result": {
        "code": "SUCCESS",
        "type": "TABLE",
        "msg": "Incident_Date\tIncident_Zip\tHeatingComplaintCount\n01/05/2016\t11226\t180\n01/05/2016\t10467\t158\n01/05/2016\t10468\t149\n01/05/2016\t10457\t128\n01/05/2016\t10453\t125\n01/05/2016\t11213\t119\n01/05/2016\t10458\t119\n01/05/2016\t11225\t116\n01/05/2016\t11212\t110\n01/05/2016\t10456\t102\n"
      },
      "dateCreated": "Nov 11, 2015 3:25:57 PM",
      "dateStarted": "Feb 11, 2016 7:55:35 PM",
      "dateFinished": "Feb 11, 2016 7:55:38 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Let us create SparkR Data Farmes for Heating Complaint Count and Street Complaint Count for each zip code on every day basis",
      "text": "%spark.r\n\nnyc_heatf \u003c- sql(sqlContext, \"select split(`Created Date`, \u0027 \u0027)[0] as Incident_Date, `Incident Zip` as Incident_Zip, count(`Unique Key`) as HeatingComplaintCount from nyc311f where `Complaint Type` \u003d \u0027HEAT/HOT WATER\u0027 group by split(`Created Date`, \u0027 \u0027)[0], `Incident Zip`\")\nregisterTempTable(nyc_heatf, \"nycheatf\")\nnyc_streetf \u003c- sql(sqlContext, \"select split(`Created Date`, \u0027 \u0027)[0] as Incident_Date, `Incident Zip` as Incident_Zip, count(`Unique Key`) as StreetComplaintCount from nyc311f where `Complaint Type` \u003d \u0027Street Condition\u0027 group by split(`Created Date`, \u0027 \u0027)[0], `Incident Zip`\")\nregisterTempTable(nyc_streetf, \"nycstreetf\")\nnyc_top \u003c- sql(sqlContext, \"select a1.Incident_Date as IncidentDay, cast(a1.Incident_Zip as STRING) as Zip, cast(a1.HeatingComplaintCount as BIGINT) as HeatingCount, cast(b1.StreetComplaintCount as BIGINT) as StreetProblemCount from nycheatf a1, nycstreetf b1 where a1.Incident_Zip \u003d b1.Incident_Zip and a1.Incident_Date \u003d b1.Incident_Date\")\nregisterTempTable(nyc_top, \"nyctopf\")",
      "dateUpdated": "Feb 11, 2016 7:56:36 PM",
      "config": {
        "colWidth": 12.0,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        },
        "editorMode": "ace/mode/scala",
        "title": true
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1454431900755_-257072687",
      "id": "20160202-165140_1562946480",
      "result": {
        "code": "SUCCESS",
        "type": "TEXT",
        "msg": "\n\n\nnyc_heatf \u003c- sql(sqlContext, \"select split(`Created Date`, \u0027 \u0027)[0] as Incident_Date, `Incident Zip` as Incident_Zip, count(`Unique Key`) as HeatingComplaintCount from nyc311f where `Complaint Type` \u003d \u0027HEAT/HOT WATER\u0027 group by split(`Created Date`, \u0027 \u0027)[0], `Incident Zip`\")\n\n\nregisterTempTable(nyc_heatf, \"nycheatf\")\n\n\nnyc_streetf \u003c- sql(sqlContext, \"select split(`Created Date`, \u0027 \u0027)[0] as Incident_Date, `Incident Zip` as Incident_Zip, count(`Unique Key`) as StreetComplaintCount from nyc311f where `Complaint Type` \u003d \u0027Street Condition\u0027 group by split(`Created Date`, \u0027 \u0027)[0], `Incident Zip`\")\n\n\nregisterTempTable(nyc_streetf, \"nycstreetf\")\n\n\nnyc_top \u003c- sql(sqlContext, \"select a1.Incident_Date as IncidentDay, cast(a1.Incident_Zip as STRING) as Zip, cast(a1.HeatingComplaintCount as BIGINT) as HeatingCount, cast(b1.StreetComplaintCount as BIGINT) as StreetProblemCount from nycheatf a1, nycstreetf b1 where a1.Incident_Zip \u003d b1.Incident_Zip and a1.Incident_Date \u003d b1.Incident_Date\")\n\n\nregisterTempTable(nyc_top, \"nyctopf\")"
      },
      "dateCreated": "Feb 2, 2016 4:51:40 PM",
      "dateStarted": "Feb 11, 2016 7:56:38 PM",
      "dateFinished": "Feb 11, 2016 7:56:40 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Let us use R Visualization Component for Day wise Visualization of the Complaints",
      "text": "%spark.knitr results\u003d\u0027asis\u0027\n\nlibrary(googleVis) \n\nnyc_top_local \u003c- take(nyc_top, nrow(nyc_top))\nnyc_top_local$IncidentDt \u003c- as.Date(nyc_top_local$IncidentDay, format \u003d \"%m/%d/%Y\")\nnyc_top_local$IncidentDay \u003c- NULL\n\noptions(gvis.plot.tag\u003d\u0027chart\u0027)\nNYC_Complaint \u003c- gvisMotionChart(nyc_top_local, idvar\u003d\"Zip\", timevar\u003d\"IncidentDt\", options\u003dlist(width\u003d1000, height\u003d600))\n",
      "dateUpdated": "Feb 11, 2016 7:57:08 PM",
      "config": {
        "colWidth": 12.0,
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\n],\n[\n \"10033\",\nnew Date(2015,2,27),\n5,\n6 \n],\n[\n \"10035\",\nnew Date(2015,7,26),\n1,\n2 \n],\n[\n \"10036\",\nnew Date(2015,6,22),\n1,\n3 \n],\n[\n \"10036\",\nnew Date(2015,10,12),\n2,\n1 \n],\n[\n \"10040\",\nnew Date(2015,11,29),\n16,\n1 \n],\n[\n \"10075\",\nnew Date(2015,10,11),\n1,\n1 \n],\n[\n \"10128\",\nnew Date(2015,5,25),\n1,\n1 \n],\n[\n \"10128\",\nnew Date(2015,9,15),\n1,\n7 \n],\n[\n \"10301\",\nnew Date(2015,10,17),\n5,\n4 \n],\n[\n \"10304\",\nnew Date(2015,3,23),\n2,\n22 \n],\n[\n \"10306\",\nnew Date(2015,8,22),\n1,\n6 \n],\n[\n \"10314\",\nnew Date(2015,9,21),\n1,\n4 \n],\n[\n \"10451\",\nnew Date(2015,5,22),\n1,\n3 \n],\n[\n \"10451\",\nnew Date(2015,9,12),\n1,\n1 \n],\n[\n \"10455\",\nnew Date(2015,4,3),\n4,\n4 \n],\n[\n \"10456\",\nnew Date(2016,0,5),\n102,\n6 \n],\n[\n \"10461\",\nnew Date(2015,9,15),\n4,\n1 \n],\n[\n \"10462\",\nnew Date(2015,2,16),\n26,\n2 \n],\n[\n \"10466\",\nnew Date(2016,0,8),\n18,\n1 \n],\n[\n \"10466\",\nnew Date(2015,7,20),\n5,\n2 \n],\n[\n \"10466\",\nnew Date(2015,11,10),\n3,\n1 \n],\n[\n \"10467\",\nnew Date(2015,4,11),\n11,\n4 \n],\n[\n \"10471\",\nnew Date(2015,9,18),\n5,\n7 \n],\n[\n \"10472\",\nnew Date(2015,2,19),\n19,\n3 \n],\n[\n \"10474\",\nnew Date(2015,2,24),\n5,\n2 \n],\n[\n \"10474\",\nnew Date(2015,11,8),\n1,\n6 \n],\n[\n \"11103\",\nnew Date(2015,2,15),\n2,\n2 \n],\n[\n \"11204\",\nnew Date(2015,6,2),\n1,\n3 \n],\n[\n \"11204\",\nnew Date(2015,10,25),\n6,\n2 \n],\n[\n \"11207\",\nnew Date(2015,8,25),\n1,\n1 \n],\n[\n \"11208\",\nnew Date(2015,5,16),\n1,\n5 \n],\n[\n \"11208\",\nnew Date(2015,9,6),\n5,\n3 \n],\n[\n \"11210\",\nnew Date(2015,3,6),\n7,\n3 \n],\n[\n \"11210\",\nnew Date(2015,7,24),\n1,\n5 \n],\n[\n \"11211\",\nnew Date(2015,4,15),\n2,\n1 \n],\n[\n \"11215\",\nnew Date(2015,5,1),\n2,\n2 \n],\n[\n \"11217\",\nnew Date(2015,8,28),\n1,\n2 \n],\n[\n \"11218\",\nnew Date(2015,2,30),\n5,\n3 \n],\n[\n \"11218\",\nnew Date(2015,5,19),\n1,\n7 \n],\n[\n \"11218\",\nnew Date(2015,11,14),\n1,\n1 \n],\n[\n \"11220\",\nnew Date(2015,3,9),\n13,\n6 \n],\n[\n \"11220\",\nnew Date(2015,7,27),\n1,\n7 \n],\n[\n \"11221\",\nnew Date(2015,4,18),\n3,\n3 \n],\n[\n \"11222\",\nnew Date(2015,3,14),\n2,\n2 \n],\n[\n \"11223\",\nnew Date(2015,4,23),\n1,\n2 \n],\n[\n \"11225\",\nnew Date(2015,5,4),\n1,\n1 \n],\n[\n \"11231\",\nnew Date(2015,10,16),\n3,\n2 \n],\n[\n \"11232\",\nnew Date(2015,3,17),\n2,\n1 \n],\n[\n \"11234\",\nnew Date(2015,3,22),\n1,\n12 \n],\n[\n \"11236\",\nnew Date(2015,8,21),\n2,\n1 \n],\n[\n \"11237\",\nnew Date(2015,5,12),\n1,\n1 \n],\n[\n \"11237\",\nnew Date(2015,9,2),\n10,\n1 \n],\n[\n \"11355\",\nnew Date(2015,11,5),\n2,\n1 \n],\n[\n \"11365\",\nnew Date(2015,2,24),\n1,\n2 \n],\n[\n \"11374\",\nnew Date(2015,11,31),\n4,\n1 \n],\n[\n \"11375\",\nnew Date(2015,2,27),\n5,\n8 \n],\n[\n \"11377\",\nnew Date(2015,3,8),\n3,\n13 \n],\n[\n \"11378\",\nnew Date(2015,10,12),\n2,\n1 \n],\n[\n \"11385\",\nnew Date(2015,4,2),\n2,\n2 \n],\n[\n \"11411\",\nnew Date(2015,3,5),\n2,\n1 \n],\n[\n \"11416\",\nnew Date(2015,9,23),\n2,\n2 \n],\n[\n \"11420\",\nnew Date(2016,0,1),\n1,\n1 \n],\n[\n \"11423\",\nnew Date(2015,3,13),\n1,\n2 \n],\n[\n \"11432\",\nnew Date(2015,10,15),\n6,\n3 \n],\n[\n \"11434\",\nnew Date(2015,6,30),\n1,\n1 \n],\n[\n \"11436\",\nnew Date(2015,9,29),\n1,\n1 \n],\n[\n \"11691\",\nnew Date(2015,4,11),\n1,\n3 \n],\n[\n \"10001\",\nnew Date(2015,3,2),\n1,\n4 \n],\n[\n \"10002\",\nnew Date(2015,2,31),\n6,\n3 \n],\n[\n \"10007\",\nnew Date(2015,11,5),\n1,\n2 \n],\n[\n \"10009\",\nnew Date(2016,0,8),\n6,\n1 \n],\n[\n \"10012\",\nnew Date(2015,11,18),\n4,\n3 \n],\n[\n \"10014\",\nnew Date(2015,9,18),\n9,\n1 \n],\n[\n \"10014\",\nnew Date(2015,11,23),\n2,\n3 \n],\n[\n \"10018\",\nnew Date(2015,4,9),\n1,\n1 \n],\n[\n \"10019\",\nnew Date(2015,3,5),\n1,\n5 \n],\n[\n \"10021\",\nnew Date(2015,9,3),\n12,\n1 \n],\n[\n \"10024\",\nnew Date(2015,11,26),\n4,\n3 \n],\n[\n \"10025\",\nnew Date(2015,4,27),\n3,\n2 \n],\n[\n \"10027\",\nnew Date(2015,2,27),\n8,\n6 \n],\n[\n \"10029\",\nnew Date(2015,3,8),\n12,\n4 \n],\n[\n \"10029\",\nnew Date(2015,7,26),\n4,\n2 \n],\n[\n \"10031\",\nnew Date(2015,5,16),\n9,\n4 \n],\n[\n \"10031\",\nnew Date(2015,9,6),\n3,\n2 \n],\n[\n \"10032\",\nnew Date(2015,8,30),\n2,\n1 \n],\n[\n \"10033\",\nnew Date(2015,5,21),\n4,\n1 \n],\n[\n \"10040\",\nnew Date(2015,3,1),\n19,\n1 \n],\n[\n \"10065\",\nnew Date(2015,11,30),\n2,\n2 \n],\n[\n \"10301\",\nnew Date(2015,11,6),\n2,\n1 \n],\n[\n \"10451\",\nnew Date(2015,6,11),\n1,\n1 \n],\n[\n \"10452\",\nnew Date(2015,3,2),\n5,\n5 \n],\n[\n \"10452\",\nnew Date(2015,8,29),\n4,\n1 \n],\n[\n \"10453\",\nnew Date(2015,2,31),\n13,\n8 \n],\n[\n \"10456\",\nnew Date(2015,2,16),\n16,\n1 \n],\n[\n \"10460\",\nnew Date(2015,5,5),\n3,\n2 \n],\n[\n \"10460\",\nnew Date(2015,9,28),\n9,\n2 \n],\n[\n \"10461\",\nnew Date(2015,6,14),\n1,\n1 \n],\n[\n \"10465\",\nnew Date(2015,9,18),\n3,\n1 \n],\n[\n \"10465\",\nnew Date(2015,11,23),\n1,\n4 \n],\n[\n \"10466\",\nnew Date(2015,2,19),\n14,\n6 \n],\n[\n \"10468\",\nnew Date(2015,2,24),\n25,\n4 \n],\n[\n \"11101\",\nnew Date(2015,9,27),\n1,\n1 \n],\n[\n \"11103\",\nnew Date(2015,3,4),\n2,\n2 \n],\n[\n \"11106\",\nnew Date(2015,9,17),\n11,\n1 \n],\n[\n \"11203\",\nnew Date(2015,10,5),\n1,\n3 \n],\n[\n \"11204\",\nnew Date(2015,3,6),\n3,\n9 \n],\n[\n \"11205\",\nnew Date(2015,10,10),\n1,\n5 \n],\n[\n \"11207\",\nnew Date(2015,4,20),\n2,\n6 \n],\n[\n \"11208\",\nnew Date(2015,6,5),\n1,\n3 \n],\n[\n \"11208\",\nnew Date(2015,8,10),\n4,\n7 \n],\n[\n \"11209\",\nnew Date(2015,3,29),\n3,\n4 \n],\n[\n \"11209\",\nnew Date(2015,9,24),\n14,\n2 \n],\n[\n \"11213\",\nnew Date(2015,10,8),\n13,\n1 \n],\n[\n \"11214\",\nnew Date(2015,3,9),\n16,\n7 \n],\n[\n \"11215\",\nnew Date(2015,10,13),\n5,\n1 \n],\n[\n \"11216\",\nnew Date(2015,3,14),\n6,\n1 \n],\n[\n \"11219\",\nnew Date(2015,5,4),\n2,\n20 \n],\n[\n \"11219\",\nnew Date(2015,9,27),\n6,\n3 \n],\n[\n \"11221\",\nnew Date(2015,7,12),\n1,\n2 \n],\n[\n \"11223\",\nnew Date(2016,0,5),\n26,\n2 \n],\n[\n \"11225\",\nnew Date(2016,0,10),\n7,\n2 \n],\n[\n \"11226\",\nnew Date(2015,3,17),\n1,\n3 \n],\n[\n \"11229\",\nnew Date(2015,5,7),\n1,\n4 \n],\n[\n \"11233\",\nnew Date(2016,0,8),\n9,\n1 \n],\n[\n \"11233\",\nnew Date(2015,7,20),\n1,\n4 \n],\n[\n \"11235\",\nnew Date(2015,10,19),\n9,\n4 \n],\n[\n \"11237\",\nnew Date(2015,6,1),\n2,\n3 \n],\n[\n \"11238\",\nnew Date(2015,3,25),\n8,\n4 \n],\n[\n \"11238\",\nnew Date(2015,9,20),\n10,\n1 \n],\n[\n \"11373\",\nnew Date(2015,9,6),\n4,\n5 \n],\n[\n \"11374\",\nnew Date(2015,8,30),\n1,\n2 \n],\n[\n \"11378\",\nnew Date(2015,11,1),\n1,\n1 \n],\n[\n \"11385\",\nnew Date(2015,5,24),\n1,\n8 \n],\n[\n \"11385\",\nnew Date(2015,9,14),\n1,\n3 \n],\n[\n \"11415\",\nnew Date(2015,3,8),\n1,\n6 \n],\n[\n \"11420\",\nnew Date(2015,11,29),\n1,\n1 \n],\n[\n \"11429\",\nnew Date(2015,3,21),\n1,\n1 \n],\n[\n \"11432\",\nnew Date(2015,2,20),\n3,\n3 \n],\n[\n \"11433\",\nnew Date(2015,4,5),\n1,\n2 \n],\n[\n \"11434\",\nnew Date(2016,0,7),\n2,\n3 \n],\n[\n \"11694\",\nnew Date(2015,11,13),\n1,\n2 \n] \n];\ndata.addColumn(\u0027string\u0027,\u0027Zip\u0027);\ndata.addColumn(\u0027date\u0027,\u0027IncidentDt\u0027);\ndata.addColumn(\u0027number\u0027,\u0027HeatingCount\u0027);\ndata.addColumn(\u0027number\u0027,\u0027StreetProblemCount\u0027);\ndata.addRows(datajson);\nreturn(data);\n}\n \n// jsDrawChart\nfunction drawChartMotionChartID36b18c6a4cd22() {\nvar data \u003d gvisDataMotionChartID36b18c6a4cd22();\nvar options \u003d {};\noptions[\"width\"] \u003d   1000;\noptions[\"height\"] \u003d    600;\noptions[\"state\"] \u003d \"\";\n\n    var chart \u003d new google.visualization.MotionChart(\n    document.getElementById(\u0027MotionChartID36b18c6a4cd22\u0027)\n    );\n    chart.draw(data,options);\n    \n\n}\n  \n \n// jsDisplayChart\n(function() {\nvar pkgs \u003d window.__gvisPackages \u003d window.__gvisPackages || [];\nvar callbacks \u003d window.__gvisCallbacks \u003d window.__gvisCallbacks || [];\nvar chartid \u003d \"motionchart\";\n  \n// Manually see if chartid is in pkgs (not all browsers support Array.indexOf)\nvar i, newPackage \u003d true;\nfor (i \u003d 0; newPackage \u0026\u0026 i \u003c pkgs.length; i++) {\nif (pkgs[i] \u003d\u003d\u003d chartid)\nnewPackage \u003d false;\n}\nif (newPackage)\n  pkgs.push(chartid);\n  \n// Add the drawChart function to the global list of callbacks\ncallbacks.push(drawChartMotionChartID36b18c6a4cd22);\n})();\nfunction displayChartMotionChartID36b18c6a4cd22() {\n  var pkgs \u003d window.__gvisPackages \u003d window.__gvisPackages || [];\n  var callbacks \u003d window.__gvisCallbacks \u003d window.__gvisCallbacks || [];\n  window.clearTimeout(window.__gvisLoad);\n  // The timeout is set to 100 because otherwise the container div we are\n  // targeting might not be part of the document yet\n  window.__gvisLoad \u003d setTimeout(function() {\n  var pkgCount \u003d pkgs.length;\n  google.load(\"visualization\", \"1\", { packages:pkgs, callback: function() {\n  if (pkgCount !\u003d pkgs.length) {\n  // Race condition where another setTimeout call snuck in after us; if\n  // that call added a package, we must not shift its callback\n  return;\n}\nwhile (callbacks.length \u003e 0)\ncallbacks.shift()();\n} });\n}, 100);\n}\n \n// jsFooter\n\u003c/script\u003e \n\u003c!-- jsChart --\u003e \n\u003cscript type\u003d\"text/javascript\" src\u003d\"https://www.google.com/jsapi?callback\u003ddisplayChartMotionChartID36b18c6a4cd22\"\u003e\u003c/script\u003e \n\u003c!-- divChart --\u003e \n\u003cdiv id\u003d\"MotionChartID36b18c6a4cd22\" style\u003d\"width: 1000; height: 600;\"\u003e \n\u003c/div\u003e"
      },
      "dateCreated": "Oct 26, 2015 6:37:53 PM",
      "dateStarted": "Feb 11, 2016 7:57:49 PM",
      "dateFinished": "Feb 11, 2016 7:58:25 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Subset the Complaint Data only for Heating/Hot Water Complaint Group By Incident Address",
      "text": "val nyc_sdfht \u003d nyc_sdff.where($\"Complaint Type\" \u003d\u003d\u003d \"HEAT/HOT WATER\").groupBy($\"Incident Address\" as \"IncidentAddress\").agg(count(\"Incident Address\") as \"IncidentCount\")\nnyc_sdfht.registerTempTable(\"nycht\")",
      "dateUpdated": "Feb 11, 2016 8:00:02 PM",
      "config": {
        "colWidth": 12.0,
        "editorMode": "ace/mode/scala",
        "title": true,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        }
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1442889336204_1973769277",
      "id": "20150921-193536_580419326",
      "result": {
        "code": "SUCCESS",
        "type": "TEXT",
        "msg": "nyc_sdfht: org.apache.spark.sql.DataFrame \u003d [IncidentAddress: string, IncidentCount: bigint]\n"
      },
      "dateCreated": "Sep 21, 2015 7:35:36 PM",
      "dateStarted": "Feb 11, 2016 8:00:04 PM",
      "dateFinished": "Feb 11, 2016 8:00:06 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Number of Address or Buildings which had No Heat/Hot Water problems in last one year",
      "text": "%sql select count(*) as NumberOfBuildingsWithHeatingComplaints from nycht",
      "dateUpdated": "Feb 11, 2016 3:43:04 PM",
      "config": {
        "colWidth": 12.0,
        "graph": {
          "mode": "table",
          "height": 95.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        },
        "title": true
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1454435154383_465278081",
      "id": "20160202-174554_1774122323",
      "result": {
        "code": "SUCCESS",
        "type": "TABLE",
        "msg": "NumberOfBuildingsWithHeatingComplaints\n26922\n"
      },
      "dateCreated": "Feb 2, 2016 5:45:54 PM",
      "dateStarted": "Feb 11, 2016 3:43:07 PM",
      "dateFinished": "Feb 11, 2016 3:43:09 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Let Us get Building Data for New York City from HDFS",
      "text": "val pluto_nyc \u003d sqlContext.read.format(\"com.databricks.spark.csv\").option(\"header\",\"true\").load(\"hdfs:////biginsights/spark-enablement/datasets/NewYorkCityBuildingData\").cache()\n\n//pluto_df.count\n\npluto_nyc.registerTempTable(\"nycplutof\")\n",
      "dateUpdated": "Feb 11, 2016 8:01:19 PM",
      "config": {
        "colWidth": 12.0,
        "editorMode": "ace/mode/scala",
        "title": true,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        }
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1445793564081_-511874645",
      "id": "20151025-171924_603260698",
      "result": {
        "code": "SUCCESS",
        "type": "TEXT",
        "msg": "pluto_nyc: org.apache.spark.sql.DataFrame \u003d [Borough: string, Block: string, Lot: string, CD: string, CT2010: string, CB2010: string, SchoolDist: string, Council: string, ZipCode: string, FireComp: string, PolicePrct: string, Address: string, ZoneDist1: string, ZoneDist2: string, ZoneDist3: string, ZoneDist4: string, Overlay1: string, Overlay2: string, SPDist1: string, SPDist2: string, LtdHeight: string, AllZoning1: string, AllZoning2: string, SplitZone: string, BldgClass: string, LandUse: string, Easements: string, OwnerType: string, OwnerName: string, LotArea: string, BldgArea: string, ComArea: string, ResArea: string, OfficeArea: string, RetailArea: string, GarageArea: string, StrgeArea: string, FactryArea: string, OtherArea: string, AreaSource: string, NumBldgs: string, NumFloors: s..."
      },
      "dateCreated": "Oct 25, 2015 5:19:24 PM",
      "dateStarted": "Feb 11, 2016 8:01:21 PM",
      "dateFinished": "Feb 11, 2016 8:01:23 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Total Building Count in New York City",
      "text": "pluto_nyc.count",
      "dateUpdated": "Feb 11, 2016 3:45:21 PM",
      "config": {
        "colWidth": 12.0,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        },
        "editorMode": "ace/mode/scala",
        "title": true
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1454473708456_1787697433",
      "id": "20160203-042828_534398077",
      "result": {
        "code": "SUCCESS",
        "type": "TEXT",
        "msg": "res11: Long \u003d 859382\n"
      },
      "dateCreated": "Feb 3, 2016 4:28:28 AM",
      "dateStarted": "Feb 11, 2016 3:45:23 PM",
      "dateFinished": "Feb 11, 2016 3:45:54 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "using SparkSQL Select the building Attributes to be Used in Model",
      "text": "%sql select Address, cast(BldgArea as DOUBLE) as Bldg_Area, cast(ResArea as DOUBLE) as Res_Area, (2014 - cast(YearBuilt as BIGINT)) as Bldg_Age, cast(LotArea as DOUBLE) as Lot_Area from nycplutof where cast(BldgArea as DOUBLE) \u003e 0 and cast(ResArea as DOUBLE) \u003e 0 and cast(LotArea as DOUBLE) \u003e 0 and YearBuilt is not null and cast(YearBuilt as BIGINT) \u003e 0 limit 20",
      "dateUpdated": "Feb 11, 2016 3:46:05 PM",
      "config": {
        "colWidth": 12.0,
        "editorMode": "ace/mode/sql",
        "title": true,
        "graph": {
          "mode": "table",
          "height": 330.0,
          "optionOpen": false,
          "keys": [
            {
              "name": "Address",
              "index": 0.0,
              "aggr": "sum"
            }
          ],
          "values": [
            {
              "name": "Bldg_Area",
              "index": 1.0,
              "aggr": "sum"
            }
          ],
          "groups": [],
          "scatter": {
            "xAxis": {
              "name": "Address",
              "index": 0.0,
              "aggr": "sum"
            }
          }
        }
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1445793774199_1498513335",
      "id": "20151025-172254_201415781",
      "result": {
        "code": "SUCCESS",
        "type": "TABLE",
        "msg": "Address\tBldg_Area\tRes_Area\tBldg_Age\tLot_Area\n25 JAY STREET\t47735.0\t38188.0\t94\t9545.0\n85 WATER STREET\t4720.0\t3540.0\t114\t1262.0\n1 MAIN STREET\t241434.0\t227916.0\t101\t27273.0\n25 WASHINGTON STREET\t101790.0\t96790.0\t112\t14370.0\n31 WASHINGTON STREET\t35978.0\t25023.0\t84\t6000.0\n133 WATER STREET\t57668.0\t54917.0\t8\t10010.0\n39 PEARL STREET\t31500.0\t18900.0\t114\t6200.0\n50 JAY STREET\t86350.0\t22000.0\t101\t13812.0\n52 JAY STREET\t3650.0\t1825.0\t84\t2300.0\n54 JAY STREET\t2822.0\t1838.0\t16\t1340.0\n177 WATER STREET\t9800.0\t7350.0\t99\t2450.0\n218 PLYMOUTH STREET\t20798.0\t10401.0\t114\t4170.0\n50 BRIDGE STREET\t170227.0\t68985.0\t110\t41238.0\n205 WATER STREET\t82736.0\t70784.0\t4\t21740.0\n37 BRIDGE STREET\t59244.0\t51952.0\t98\t12500.0\n50 HUDSON AVENUE\t3200.0\t1600.0\t115\t1458.0\n52 HUDSON AVENUE\t2960.0\t1600.0\t115\t1458.0\n54 HUDSON AVENUE\t2560.0\t1680.0\t115\t1458.0\n311 WATER STREET\t2140.0\t2140.0\t113\t1200.0\n301 WATER STREET\t3340.0\t3340.0\t115\t2000.0\n"
      },
      "dateCreated": "Oct 25, 2015 5:22:54 PM",
      "dateStarted": "Feb 11, 2016 3:46:08 PM",
      "dateFinished": "Feb 11, 2016 3:46:10 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Create Subset of Building Data To use for Modeling using Spark SQL",
      "text": "val pluto_dfsub \u003d sqlContext.sql(\"select Address, ZipCode, cast(BldgArea as DOUBLE) as Bldg_Area, cast(ResArea as DOUBLE) as Res_Area, (2014 - cast(YearBuilt as BIGINT)) as Bldg_Age, cast(LotArea as DOUBLE) as Lot_Area, cast(XCoord as DOUBLE) as XCoord, cast(YCoord as DOUBLE) as YCoord from nycplutof where cast(BldgArea as DOUBLE) \u003e 0 and cast(ResArea as DOUBLE) \u003e 0 and cast(LotArea as DOUBLE) \u003e 0 and YearBuilt is not null and cast(YearBuilt as BIGINT) \u003e 0\")\npluto_dfsub.registerTempTable(\"nycplutosub\")",
      "dateUpdated": "Feb 11, 2016 8:06:06 PM",
      "config": {
        "colWidth": 12.0,
        "editorMode": "ace/mode/scala",
        "title": true,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        }
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1445796640409_1587862091",
      "id": "20151025-181040_1374008374",
      "result": {
        "code": "SUCCESS",
        "type": "TEXT",
        "msg": "pluto_dfsub: org.apache.spark.sql.DataFrame \u003d [Address: string, ZipCode: string, Bldg_Area: double, Res_Area: double, Bldg_Age: bigint, Lot_Area: double, XCoord: double, YCoord: double]\n"
      },
      "dateCreated": "Oct 25, 2015 6:10:40 PM",
      "dateStarted": "Feb 11, 2016 8:06:14 PM",
      "dateFinished": "Feb 11, 2016 8:06:16 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Add ID to the Pluto Dataframe For using System ML",
      "text": "import org.apache.sysml.runtime.instructions.spark.utils.{RDDConverterUtilsExt \u003d\u003e RDDConverterUtils}\nval pluto_id_df \u003d RDDConverterUtils.addIDToDataFrame(pluto_dfsub, sqlContext, \"ID\")\n\npluto_id_df.registerTempTable(\"plutoiddataf\")",
      "dateUpdated": "Feb 11, 2016 8:06:48 PM",
      "config": {
        "colWidth": 12.0,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        },
        "editorMode": "ace/mode/scala",
        "title": true
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1454438915136_-338917760",
      "id": "20160202-184835_1983586984",
      "result": {
        "code": "SUCCESS",
        "type": "TEXT",
        "msg": "import org.apache.sysml.runtime.instructions.spark.utils.{RDDConverterUtilsExt\u003d\u003eRDDConverterUtils}\npluto_id_df: org.apache.spark.sql.DataFrame \u003d [Address: string, ZipCode: string, Bldg_Area: double, Res_Area: double, Bldg_Age: bigint, Lot_Area: double, XCoord: double, YCoord: double, ID: double]\n"
      },
      "dateCreated": "Feb 2, 2016 6:48:35 PM",
      "dateStarted": "Feb 11, 2016 8:06:51 PM",
      "dateFinished": "Feb 11, 2016 8:07:12 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Total Number of Buildings",
      "text": "%sql select count(*) as TotalNumOfBuildings from plutoiddataf",
      "dateUpdated": "Feb 11, 2016 8:07:48 PM",
      "config": {
        "colWidth": 12.0,
        "graph": {
          "mode": "table",
          "height": 95.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        },
        "editorMode": "ace/mode/sql",
        "title": false,
        "tableHide": true,
        "editorHide": true
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1454439829823_633862088",
      "id": "20160202-190349_1729590576",
      "result": {
        "code": "SUCCESS",
        "type": "TABLE",
        "msg": "TotalNumOfBuildings\n757324\n"
      },
      "dateCreated": "Feb 2, 2016 7:03:49 PM",
      "dateStarted": "Feb 11, 2016 3:48:18 PM",
      "dateFinished": "Feb 11, 2016 3:48:21 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Merge These Two Datasets for same Address",
      "text": "val merged_df \u003d nyc_sdfht.join(pluto_id_df, $\"Address\" \u003d\u003d\u003d $\"IncidentAddress\")\n\nmerged_df.registerTempTable(\"mdf\")\n\n",
      "dateUpdated": "Feb 11, 2016 8:08:02 PM",
      "config": {
        "colWidth": 12.0,
        "editorMode": "ace/mode/scala",
        "title": true,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        }
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1442889435247_-1600994051",
      "id": "20150921-193715_1458062594",
      "result": {
        "code": "SUCCESS",
        "type": "TEXT",
        "msg": "merged_df: org.apache.spark.sql.DataFrame \u003d [IncidentAddress: string, IncidentCount: bigint, Address: string, ZipCode: string, Bldg_Area: double, Res_Area: double, Bldg_Age: bigint, Lot_Area: double, XCoord: double, YCoord: double, ID: double]\n"
      },
      "dateCreated": "Sep 21, 2015 7:37:15 PM",
      "dateStarted": "Feb 11, 2016 8:08:05 PM",
      "dateFinished": "Feb 11, 2016 8:08:07 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Create Final Dataset to be used for Modeling",
      "text": "val mddFinal \u003d sqlContext.sql(\"select ID, cast(IncidentCount as DOUBLE) as Incident_Count, Address, ZipCode, Bldg_Area, Lot_Area, Bldg_Age, Res_Area, XCoord, YCoord from mdf\")\n\nmddFinal.registerTempTable(\"modelf\")",
      "dateUpdated": "Feb 11, 2016 8:08:33 PM",
      "config": {
        "colWidth": 12.0,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        },
        "editorMode": "ace/mode/scala",
        "title": true
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1454478468203_-1123173780",
      "id": "20160203-054748_852734527",
      "result": {
        "code": "SUCCESS",
        "type": "TEXT",
        "msg": "mddFinal: org.apache.spark.sql.DataFrame \u003d [ID: double, Incident_Count: double, Address: string, ZipCode: string, Bldg_Area: double, Lot_Area: double, Bldg_Age: bigint, Res_Area: double, XCoord: double, YCoord: double]\n"
      },
      "dateCreated": "Feb 3, 2016 5:47:48 AM",
      "dateStarted": "Feb 11, 2016 8:08:36 PM",
      "dateFinished": "Feb 11, 2016 8:08:38 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Dataset To be  used for developing the model",
      "text": "%sql select * from modelf limit 10",
      "dateUpdated": "Feb 11, 2016 3:49:49 PM",
      "config": {
        "colWidth": 12.0,
        "editorMode": "ace/mode/sql",
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [
            {
              "name": "ID",
              "index": 0.0,
              "aggr": "sum"
            }
          ],
          "values": [
            {
              "name": "Incident_Count",
              "index": 1.0,
              "aggr": "sum"
            }
          ],
          "groups": [],
          "scatter": {
            "xAxis": {
              "name": "ID",
              "index": 0.0,
              "aggr": "sum"
            },
            "yAxis": {
              "name": "Incident_Count",
              "index": 1.0,
              "aggr": "sum"
            }
          }
        },
        "title": true
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1447373331311_323753400",
      "id": "20151113-000851_1093711545",
      "result": {
        "code": "SUCCESS",
        "type": "TABLE",
        "msg": "ID\tIncident_Count\tAddress\tZipCode\tBldg_Area\tLot_Area\tBldg_Age\tRes_Area\tXCoord\tYCoord\n336922.0\t33.0\t10 MITCHELL PLACE\t10017\t67000.0\t10185.0\t83\t65000.0\t993757.0\t213680.0\n352474.0\t5.0\t107 VERMILYEA AVENUE\t10034\t23370.0\t7500.0\t100\t23370.0\t1005883.0\t254789.0\n336910.0\t5.0\t1075 1 AVENUE\t10022\t22362.0\t5000.0\t76\t18882.0\t994659.0\t216002.0\n110254.0\t9.0\t1077 NEW YORK AVENUE\t11203\t22120.0\t7900.0\t88\t22120.0\t999258.0\t174911.0\n555410.0\t2.0\t111-64 166 STREET\t11433\t1724.0\t3042.0\t89\t1724.0\t1044016.0\t191204.0\n271428.0\t2.0\t1151 ST LAWRENCE AVENUE\t10472\t1920.0\t5000.0\t84\t1920.0\t1021059.0\t241405.0\n115115.0\t1.0\t1176 NOSTRAND AVENUE\t11225\t6400.0\t2000.0\t83\t5600.0\t997913.0\t179289.0\n532723.0\t6.0\t125-01 JAMAICA AVENUE\t11418\t4250.0\t2725.0\t83\t2125.0\t1032601.0\t194788.0\n532786.0\t1.0\t127-03 JAMAICA AVENUE\t11418\t74542.0\t21475.0\t2\t74542.0\t1033169.0\t194999.0\n33019.0\t1.0\t1277 PARK PLACE\t11213\t2712.0\t2395.0\t115\t2712.0\t1002253.0\t184427.0\n"
      },
      "dateCreated": "Nov 13, 2015 12:08:51 AM",
      "dateStarted": "Feb 11, 2016 3:49:17 PM",
      "dateFinished": "Feb 11, 2016 3:49:25 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Total number of records in the Dataset to be used for modeling",
      "text": "%sql select count(*) as RecodsforModeling from modelf",
      "dateUpdated": "Feb 11, 2016 3:49:57 PM",
      "config": {
        "colWidth": 12.0,
        "graph": {
          "mode": "table",
          "height": 145.0,
          "optionOpen": false,
          "keys": [
            {
              "name": "RecodsforModeling",
              "index": 0.0,
              "aggr": "sum"
            }
          ],
          "values": [],
          "groups": [],
          "scatter": {
            "xAxis": {
              "name": "RecodsforModeling",
              "index": 0.0,
              "aggr": "sum"
            }
          }
        },
        "editorMode": "ace/mode/sql",
        "title": true
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1454435003778_-1541619799",
      "id": "20160202-174323_1837198395",
      "result": {
        "code": "SUCCESS",
        "type": "TABLE",
        "msg": "RecodsforModeling\n18082\n"
      },
      "dateCreated": "Feb 2, 2016 5:43:23 PM",
      "dateStarted": "Feb 11, 2016 3:49:59 PM",
      "dateFinished": "Feb 11, 2016 3:52:11 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Import System ML packages to Build Model Using System ML",
      "text": "import org.apache.spark.mllib.util.MLUtils\nimport org.apache.spark.ml.feature.VectorAssembler\nimport org.apache.sysml.api.MLContext\nimport org.apache.sysml.runtime.matrix.MatrixCharacteristics;\nimport org.apache.sysml.api.MLOutput\n\n\n\n",
      "dateUpdated": "Feb 11, 2016 8:13:55 PM",
      "config": {
        "colWidth": 12.0,
        "editorMode": "ace/mode/scala",
        "title": true,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        }
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1449515584428_809404966",
      "id": "20151207-191304_1339590377",
      "result": {
        "code": "SUCCESS",
        "type": "TEXT",
        "msg": "import org.apache.spark.mllib.util.MLUtils\nimport org.apache.spark.ml.feature.VectorAssembler\nimport org.apache.sysml.api.MLContext\nimport org.apache.sysml.runtime.matrix.MatrixCharacteristics\nimport org.apache.sysml.api.MLOutput\n"
      },
      "dateCreated": "Dec 7, 2015 7:13:04 PM",
      "dateStarted": "Feb 11, 2016 8:13:59 PM",
      "dateFinished": "Feb 11, 2016 8:14:01 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Create a system ML context for Spark",
      "text": "val mlf \u003d new MLContext(sc)",
      "dateUpdated": "Feb 11, 2016 8:14:14 PM",
      "config": {
        "colWidth": 12.0,
        "editorMode": "ace/mode/scala",
        "title": true,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        }
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1450372517147_609028361",
      "id": "20151217-171517_1742414096",
      "result": {
        "code": "SUCCESS",
        "type": "TEXT",
        "msg": "mlf: org.apache.sysml.api.MLContext \u003d org.apache.sysml.api.MLContext@6b72afb4\n"
      },
      "dateCreated": "Dec 17, 2015 5:15:17 PM",
      "dateStarted": "Feb 11, 2016 8:14:16 PM",
      "dateFinished": "Feb 11, 2016 8:14:18 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Set Parameters to be passed to the DML Script of System ML for creating a Model",
      "text": "\nval mdfFeaturesIn \u003d sqlContext.sql(\"select Res_Area, Bldg_Area, Lot_Area, Bldg_Age from modelf\")\nval mdfLabelIn \u003d sqlContext.sql(\"select Incident_Count from modelf\")\n\nmlf.registerInput(\"X\", mdfFeaturesIn)\nmlf.registerInput(\"Y\", mdfLabelIn)\nmlf.registerOutput(\"beta_out\")\n\nval cmdLineParamsf1 \u003d Map(\"X\" -\u003e \" \", \"Y\" -\u003e \" \", \"B\" -\u003e \" \")",
      "dateUpdated": "Feb 11, 2016 8:15:55 PM",
      "config": {
        "colWidth": 12.0,
        "editorMode": "ace/mode/scala",
        "title": true,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        }
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1450372592342_2110241948",
      "id": "20151217-171632_687917708",
      "result": {
        "code": "SUCCESS",
        "type": "TEXT",
        "msg": "mdfFeaturesIn: org.apache.spark.sql.DataFrame \u003d [Res_Area: double, Bldg_Area: double, Lot_Area: double, Bldg_Age: bigint]\nmdfLabelIn: org.apache.spark.sql.DataFrame \u003d [Incident_Count: double]\ncmdLineParamsf1: scala.collection.immutable.Map[String,String] \u003d Map(X -\u003e \" \", Y -\u003e \" \", B -\u003e \" \")\n"
      },
      "dateCreated": "Dec 17, 2015 5:16:32 PM",
      "dateStarted": "Feb 11, 2016 8:15:57 PM",
      "dateFinished": "Feb 11, 2016 8:16:28 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Create a GLM Model using supplied GLM script of System ML",
      "text": "\n\nval outputsf \u003d mlf.execute(\"/home/biadmin/spark-enablement/installs/SystemML/algorithms/GLM.dml\", cmdLineParamsf1)\n",
      "dateUpdated": "Feb 11, 2016 8:16:41 PM",
      "config": {
        "colWidth": 12.0,
        "editorMode": "ace/mode/scala",
        "title": true,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        }
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1449585916929_748881600",
      "id": "20151208-144516_57482265",
      "result": {
        "code": "SUCCESS",
        "type": "TEXT",
        "msg": "outputsf: org.apache.sysml.api.MLOutput \u003d org.apache.sysml.api.MLOutput@991c8b2\n"
      },
      "dateCreated": "Dec 8, 2015 2:45:16 PM",
      "dateStarted": "Feb 11, 2016 8:16:55 PM",
      "dateFinished": "Feb 11, 2016 8:17:09 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Create Dataset for generating prediction from System ML",
      "text": "val betaf \u003d outputsf.getBinaryBlockedRDD(\"beta_out\")\nval betaMCf \u003d outputsf.getMatrixCharacteristics(\"beta_out\")\n\nval Xinf \u003d sqlContext.sql(\"select ID, Res_Area, Bldg_Area, Lot_Area, Bldg_Age from plutoiddataf\")",
      "dateUpdated": "Feb 11, 2016 8:19:26 PM",
      "config": {
        "colWidth": 12.0,
        "editorMode": "ace/mode/scala",
        "title": true,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        }
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1450372821174_1076527278",
      "id": "20151217-172021_1738228696",
      "result": {
        "code": "SUCCESS",
        "type": "TEXT",
        "msg": "betaf: org.apache.spark.api.java.JavaPairRDD[org.apache.sysml.runtime.matrix.data.MatrixIndexes,org.apache.sysml.runtime.matrix.data.MatrixBlock] \u003d org.apache.spark.api.java.JavaPairRDD@1b1526dc\nbetaMCf: org.apache.sysml.runtime.matrix.MatrixCharacteristics \u003d [4 x 1, nnz\u003d4, blocks (1000 x 1000)]\nXinf: org.apache.spark.sql.DataFrame \u003d [ID: double, Res_Area: double, Bldg_Area: double, Lot_Area: double, Bldg_Age: bigint]\n"
      },
      "dateCreated": "Dec 17, 2015 5:20:21 PM",
      "dateStarted": "Feb 11, 2016 8:19:29 PM",
      "dateFinished": "Feb 11, 2016 8:19:31 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Set Parameters to be passed to DML Script for generating prediction from System ML",
      "text": "mlf.reset()\n\n\n\nmlf.registerInput(\"X\", Xinf, true)\nmlf.registerInput(\"B_full\", betaf, betaMCf)\nmlf.registerOutput(\"means\")\n\nval cmdLineParamsPredictf2 \u003d Map(\"X\" -\u003e \" \", \"B\" -\u003e \" \")",
      "dateUpdated": "Feb 11, 2016 8:19:51 PM",
      "config": {
        "colWidth": 12.0,
        "editorMode": "ace/mode/scala",
        "title": true,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        }
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1449587602009_1468015044",
      "id": "20151208-151322_440232687",
      "result": {
        "code": "SUCCESS",
        "type": "TEXT",
        "msg": "cmdLineParamsPredictf2: scala.collection.immutable.Map[String,String] \u003d Map(X -\u003e \" \", B -\u003e \" \")\n"
      },
      "dateCreated": "Dec 8, 2015 3:13:22 PM",
      "dateStarted": "Feb 11, 2016 8:19:56 PM",
      "dateFinished": "Feb 11, 2016 8:20:06 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Get Prediction from System ML\u0027s GLM model",
      "text": "val outputsPredictf \u003d mlf.execute(\"/home/biadmin/spark-enablement/installs/SystemML/algorithms/GLM-predict.dml\", cmdLineParamsPredictf2)\n\n\nval predfDF \u003d outputsPredictf.getDF(sqlContext, \"means\").withColumnRenamed(\"C1\",\"prediction\")\n\n\npredfDF.registerTempTable(\"predf\")\n\n//mddf.registerTempTable(\"inputDF\")\n\n",
      "dateUpdated": "Feb 11, 2016 8:20:19 PM",
      "config": {
        "colWidth": 12.0,
        "editorMode": "ace/mode/scala",
        "title": true,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        }
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1449779001139_1930168688",
      "id": "20151210-202321_1438657713",
      "result": {
        "code": "SUCCESS",
        "type": "TEXT",
        "msg": "outputsPredictf: org.apache.sysml.api.MLOutput \u003d org.apache.sysml.api.MLOutput@3e72a3d0\npredfDF: org.apache.spark.sql.DataFrame \u003d [ID: double, prediction: double]\n"
      },
      "dateCreated": "Dec 10, 2015 8:23:21 PM",
      "dateStarted": "Feb 11, 2016 8:20:22 PM",
      "dateFinished": "Feb 11, 2016 8:20:55 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "text": "%sql select count(*) from predf",
      "dateUpdated": "Feb 9, 2016 8:48:59 PM",
      "config": {
        "colWidth": 12.0,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        },
        "editorMode": "ace/mode/sql",
        "editorHide": true,
        "tableHide": true
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1454439631133_1377906809",
      "id": "20160202-190031_1229058617",
      "result": {
        "code": "SUCCESS",
        "type": "TABLE",
        "msg": "_c0\n757324\n"
      },
      "dateCreated": "Feb 2, 2016 7:00:31 PM",
      "dateStarted": "Feb 3, 2016 6:01:32 AM",
      "dateFinished": "Feb 3, 2016 6:01:38 AM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "text": "%sql select count(*) from predf, plutoiddataf where plutoiddataf.ID \u003d predf.ID",
      "dateUpdated": "Feb 9, 2016 8:48:37 PM",
      "config": {
        "colWidth": 12.0,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        },
        "editorMode": "ace/mode/sql",
        "editorHide": true,
        "tableHide": true
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1454439881911_-1859433378",
      "id": "20160202-190441_1052686309",
      "result": {
        "code": "SUCCESS",
        "type": "TABLE",
        "msg": "_c0\n757323\n"
      },
      "dateCreated": "Feb 2, 2016 7:04:41 PM",
      "dateStarted": "Feb 3, 2016 6:02:09 AM",
      "dateFinished": "Feb 3, 2016 6:02:17 AM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Create Dataset for Visualization from Sysytem ML\u0027s Prediction Model",
      "text": "val predsysmldf \u003d sqlContext.sql(\"select XCoord, YCoord, prediction from predf, plutoiddataf where plutoiddataf.ID \u003d predf.ID\")\n\npredsysmldf.registerTempTable(\"predsysmlnycf\")\n\n",
      "dateUpdated": "Feb 11, 2016 8:21:22 PM",
      "config": {
        "colWidth": 12.0,
        "title": true,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        },
        "editorMode": "ace/mode/scala"
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1449779664982_1832188535",
      "id": "20151210-203424_436175008",
      "result": {
        "code": "SUCCESS",
        "type": "TEXT",
        "msg": "predsysmldf: org.apache.spark.sql.DataFrame \u003d [XCoord: double, YCoord: double, prediction: double]\n"
      },
      "dateCreated": "Dec 10, 2015 8:34:24 PM",
      "dateStarted": "Feb 11, 2016 8:21:25 PM",
      "dateFinished": "Feb 11, 2016 8:21:27 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "text": "%sql select count(*) from predsysmlnycf",
      "dateUpdated": "Feb 9, 2016 9:15:31 PM",
      "config": {
        "colWidth": 12.0,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        },
        "editorMode": "ace/mode/sql",
        "editorHide": true,
        "tableHide": true
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1454439687899_-1626369134",
      "id": "20160202-190127_876322933",
      "result": {
        "code": "SUCCESS",
        "type": "TABLE",
        "msg": "_c0\n679547\n"
      },
      "dateCreated": "Feb 2, 2016 7:01:27 PM",
      "dateStarted": "Feb 2, 2016 7:06:00 PM",
      "dateFinished": "Feb 2, 2016 7:06:08 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Create R dataframe to visualize predictions from SystemML using R visualization libraries",
      "text": "%spark.r\n\n\nheat_vis \u003c- sql(sqlContext, \"select XCoord, YCoord, prediction from predsysmlnycf where prediction \u003e 10\")\nheat_vis_local \u003c- take(heat_vis, nrow(heat_vis))\nnrow(heat_vis_local)",
      "dateUpdated": "Feb 11, 2016 8:21:48 PM",
      "config": {
        "colWidth": 12.0,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        },
        "editorMode": "ace/mode/scala",
        "title": true
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1454438429435_-146044190",
      "id": "20160202-184029_1135167891",
      "result": {
        "code": "SUCCESS",
        "type": "TEXT",
        "msg": "\n\n\n\n\n\nheat_vis \u003c- sql(sqlContext, \"select XCoord, YCoord, prediction from predsysmlnycf where prediction \u003e 10\")\n\n\nheat_vis_local \u003c- take(heat_vis, nrow(heat_vis))\n\n\nnrow(heat_vis_local)\n\n[1] 233"
      },
      "dateCreated": "Feb 2, 2016 6:40:29 PM",
      "dateStarted": "Feb 11, 2016 8:21:50 PM",
      "dateFinished": "Feb 11, 2016 8:22:03 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Visualize Address wise Heating Complaint Prediction from SystemML",
      "text": "%spark.r\n\nlibrary(ggplot2)\nlibrary(maptools)\ncounties \u003c-readShapeSpatial(\"/home/biadmin/spark-enablement/datasets/NewYorkCityMap/nybb/nybb.shp\", proj4string\u003dCRS(\"+proj\u003dlonglat\"))\n\n\nggplot() +  \ngeom_polygon(data\u003dcounties, aes(x\u003dlong, y\u003dlat, group\u003dgroup), fill\u003d\"grey40\", colour\u003d\"grey90\", alpha\u003d1) +\nlabs(x\u003d\"\", y\u003d\"\", title\u003d\"Predicting Heating Complaints In New York City\") + \n#labels\ntheme(axis.ticks.y \u003d element_blank(),axis.text.y \u003d element_blank(), \n# get rid of x ticks/text\naxis.ticks.x \u003d element_blank(),axis.text.x \u003d element_blank(), \n# get rid of y ticks/text\nplot.title \u003d element_text(lineheight\u003d.8, face\u003d\"bold\", vjust\u003d1)) + \n# make title bold and add space\n# geom_point(aes(x\u003dXCoord, y\u003dYCoord, color\u003dprediction), data\u003dheat_vis_local, alpha\u003d1, size\u003d1, color\u003d\"grey20\") +\n# to get outline\ngeom_point(aes(x\u003dXCoord, y\u003dYCoord, color\u003dprediction), data\u003dheat_vis_local, alpha\u003d1, size\u003d1)+\nscale_colour_gradientn(\"Heating Complaint Count\", colours\u003dc( \"#f9f3c2\",\"#660000\"))+ \n# change color scale\ncoord_equal(ratio\u003d1)",
      "dateUpdated": "Feb 11, 2016 8:22:19 PM",
      "config": {
        "colWidth": 12.0,
        "editorMode": "ace/mode/scala",
        "title": true,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        }
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1449787788222_945235206",
      "id": "20151210-224948_1673129009",
      "result": {
        "code": "SUCCESS",
        "type": "IMG",
        "msg": 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      },
      "dateCreated": "Dec 10, 2015 10:49:48 PM",
      "dateStarted": "Feb 11, 2016 8:22:21 PM",
      "dateFinished": "Feb 11, 2016 8:22:25 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Create a sparkR dataFrame to be used for Model creation",
      "text": "%spark.r\nnycmodel_df \u003c- sql(sqlContext, \"select Address, Bldg_Area, Res_Area, Lot_Area, Bldg_Age, XCoord, YCoord, Incident_Count from modelf\")\n",
      "dateUpdated": "Feb 11, 2016 8:23:35 PM",
      "config": {
        "tableHide": false,
        "colWidth": 12.0,
        "editorMode": "ace/mode/scala",
        "editorHide": false,
        "title": true,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        }
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1445798110541_1512921946",
      "id": "20151025-183510_1974396082",
      "result": {
        "code": "SUCCESS",
        "type": "TEXT",
        "msg": "nycmodel_df \u003c- sql(sqlContext, \"select Address, Bldg_Area, Res_Area, Lot_Area, Bldg_Age, XCoord, YCoord, Incident_Count from modelf\")"
      },
      "dateCreated": "Oct 25, 2015 6:35:10 PM",
      "dateStarted": "Feb 11, 2016 8:23:37 PM",
      "dateFinished": "Feb 11, 2016 8:23:39 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Create A GLM Model out of the data using SparkR and Spark MLLib",
      "text": "%spark.r\n\nnyc_heat_glm \u003c- glm(Incident_Count ~ Bldg_Area + Res_Area + Lot_Area + Bldg_Age, data \u003d nycmodel_df, family \u003d\"gaussian\", alpha \u003d 1)\n",
      "dateUpdated": "Feb 11, 2016 8:23:48 PM",
      "config": {
        "tableHide": false,
        "colWidth": 12.0,
        "editorMode": "ace/mode/scala",
        "editorHide": false,
        "title": true,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        }
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1442890666636_-724393205",
      "id": "20150921-195746_1801233930",
      "result": {
        "code": "SUCCESS",
        "type": "TEXT",
        "msg": "\n\n\nnyc_heat_glm \u003c- glm(Incident_Count ~ Bldg_Area + Res_Area + Lot_Area + Bldg_Age, data \u003d nycmodel_df, family \u003d\"gaussian\", alpha \u003d 1)"
      },
      "dateCreated": "Sep 21, 2015 7:57:46 PM",
      "dateStarted": "Feb 11, 2016 8:23:50 PM",
      "dateFinished": "Feb 11, 2016 8:24:30 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Let us predict Heating Problem for all buildings in New York City using the model",
      "text": "%spark.r\n\nnycpluto_df \u003c- sql(sqlContext, \"select * from plutoiddataf\")\nnyc_heat_prediction \u003c- predict(nyc_heat_glm, nycpluto_df)\n\nregisterTempTable(nyc_heat_prediction, \"nycheatpred\")",
      "dateUpdated": "Feb 11, 2016 8:24:52 PM",
      "config": {
        "tableHide": false,
        "colWidth": 12.0,
        "editorMode": "ace/mode/scala",
        "editorHide": false,
        "title": true,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        }
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1445797342036_1852949150",
      "id": "20151025-182222_515173471",
      "result": {
        "code": "SUCCESS",
        "type": "TEXT",
        "msg": "\n\n\nnycpluto_df \u003c- sql(sqlContext, \"select * from plutoiddataf\")\n\n\nnyc_heat_prediction \u003c- predict(nyc_heat_glm, nycpluto_df)\n\n\n\n\n\nregisterTempTable(nyc_heat_prediction, \"nycheatpred\")"
      },
      "dateCreated": "Oct 25, 2015 6:22:22 PM",
      "dateStarted": "Feb 11, 2016 8:24:54 PM",
      "dateFinished": "Feb 11, 2016 8:24:56 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Create R Dataframe to Visualize Heating Problem Prediction from SparkR\u0027s GLM Model",
      "text": "%spark.r\n\nnyc_heat_vis \u003c- sql(sqlContext, \"select XCoord, YCoord, prediction, CASE WHEN prediction \u003c 10 THEN \u00273 - Low\u0027 WHEN prediction \u003c 50 THEN \u00272 - High\u0027 ELSE \u00271 - Very High\u0027 END as Probability from nycheatpred where prediction \u003e 0\")\n\nnyc_heat_vis_local \u003c- take(nyc_heat_vis, nrow(nyc_heat_vis))\n\nnrow(nyc_heat_vis_local)",
      "dateUpdated": "Feb 11, 2016 8:25:12 PM",
      "config": {
        "colWidth": 12.0,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
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          "scatter": {}
        },
        "editorMode": "ace/mode/scala",
        "title": true
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1454437370275_1669496690",
      "id": "20160202-182250_1869405290",
      "result": {
        "code": "SUCCESS",
        "type": "TEXT",
        "msg": "\n\n\nnyc_heat_vis \u003c- sql(sqlContext, \"select XCoord, YCoord, prediction, CASE WHEN prediction \u003c 10 THEN \u00273 - Low\u0027 WHEN prediction \u003c 50 THEN \u00272 - High\u0027 ELSE \u00271 - Very High\u0027 END as Probability from nycheatpred where prediction \u003e 0\")\n\n\n\n\n\nnyc_heat_vis_local \u003c- take(nyc_heat_vis, nrow(nyc_heat_vis))\n\n\n\n\n\nnrow(nyc_heat_vis_local)\n\n[1] 757238"
      },
      "dateCreated": "Feb 2, 2016 6:22:50 PM",
      "dateStarted": "Feb 11, 2016 8:25:15 PM",
      "dateFinished": "Feb 11, 2016 8:26:56 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Visualize the Areas with high probablity of Heating Problems As predicted by SparkR\u0027s GLM Model",
      "text": "%spark.r\n\nlibrary(ggplot2)\nlibrary(maptools)\ngpclibPermit()\ncounties \u003c-readShapeSpatial(\"/home/biadmin/spark-enablement/datasets/NewYorkCityMap/nybb/nybb.shp\", proj4string\u003dCRS(\"+proj\u003dlonglat\"))\n\n\nggplot() +  \ngeom_polygon(data\u003dcounties, aes(x\u003dlong, y\u003dlat, group\u003dgroup), fill\u003d\"grey40\", colour\u003d\"grey90\", alpha\u003d1) +\nlabs(x\u003d\"\", y\u003d\"\", title\u003d\"Predicting Heating Complaints In New York City\") + \n#labels\ntheme(axis.ticks.y \u003d element_blank(),axis.text.y \u003d element_blank(), \n# get rid of x ticks/text\naxis.ticks.x \u003d element_blank(),axis.text.x \u003d element_blank(), \n# get rid of y ticks/text\nplot.title \u003d element_text(lineheight\u003d.8, face\u003d\"bold\", vjust\u003d1)) + \n# make title bold and add space\n#geom_point(aes(x\u003dXCoord, y\u003dYCoord, color\u003dprediction), data\u003dnyc_heat_vis_local, alpha\u003d1, size\u003d1, color\u003d\"grey20\") +\n# to get outline\ngeom_point(aes(x\u003dXCoord, y\u003dYCoord, color\u003dProbability), data\u003dnyc_heat_vis_local, alpha\u003d1, size\u003d1)+\n#scale_colour_gradient(\"Heating Complaint Count\", colours\u003dc( \"#f9f3c2\",\"#660000\"))+ \nscale_colour_hue(\"Heating Complaint Count\", h\u003dc(0, 90))+ \n# change color scale\ncoord_equal(ratio\u003d1)\n",
      "dateUpdated": "Feb 11, 2016 8:27:13 PM",
      "config": {
        "tableHide": false,
        "colWidth": 12.0,
        "editorMode": "ace/mode/scala",
        "editorHide": false,
        "title": true,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
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          "scatter": {}
        }
      },
      "settings": {
        "params": {},
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      },
      "jobName": "paragraph_1445798367261_196648342",
      "id": "20151025-183927_625039392",
      "result": {
        "code": "SUCCESS",
        "type": "IMG",
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      },
      "dateCreated": "Oct 25, 2015 6:39:27 PM",
      "dateStarted": "Feb 11, 2016 8:27:17 PM",
      "dateFinished": "Feb 11, 2016 8:28:16 PM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Save Data used for Modeling To HDFS",
      "text": "mddFinal.write.save(\"hdfs:///biginsights/spark-enablement/outputdata/ComplaintAndBuildingData\")",
      "dateUpdated": "Feb 11, 2016 4:10:40 PM",
      "config": {
        "colWidth": 12.0,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        },
        "editorMode": "ace/mode/scala",
        "title": true,
        "tableHide": true
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1455063324837_-1939055444",
      "id": "20160210-001524_160105855",
      "result": {
        "code": "ERROR",
        "type": "TEXT",
        "msg": "org.apache.spark.SparkException: Job aborted.\n\tat org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1.apply$mcV$sp(InsertIntoHadoopFsRelation.scala:156)\n\tat org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1.apply(InsertIntoHadoopFsRelation.scala:108)\n\tat org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1.apply(InsertIntoHadoopFsRelation.scala:108)\n\tat org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:56)\n\tat org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation.run(InsertIntoHadoopFsRelation.scala:108)\n\tat org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult$lzycompute(commands.scala:57)\n\tat org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult(commands.scala:57)\n\tat org.apache.spark.sql.execution.ExecutedCommand.doExecute(commands.scala:69)\n\tat org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:140)\n\tat org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$5.apply(SparkPlan.scala:138)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:147)\n\tat org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:138)\n\tat org.apache.spark.sql.SQLContext$QueryExecution.toRdd$lzycompute(SQLContext.scala:933)\n\tat org.apache.spark.sql.SQLContext$QueryExecution.toRdd(SQLContext.scala:933)\n\tat org.apache.spark.sql.execution.datasources.ResolvedDataSource$.apply(ResolvedDataSource.scala:197)\n\tat org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:146)\n\tat org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:137)\n\tat $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.\u003cinit\u003e(\u003cconsole\u003e:41)\n\tat $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.\u003cinit\u003e(\u003cconsole\u003e:46)\n\tat $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.\u003cinit\u003e(\u003cconsole\u003e:48)\n\tat $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.\u003cinit\u003e(\u003cconsole\u003e:50)\n\tat $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.\u003cinit\u003e(\u003cconsole\u003e:52)\n\tat $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.\u003cinit\u003e(\u003cconsole\u003e:54)\n\tat $iwC$$iwC$$iwC$$iwC$$iwC$$iwC.\u003cinit\u003e(\u003cconsole\u003e:56)\n\tat $iwC$$iwC$$iwC$$iwC$$iwC.\u003cinit\u003e(\u003cconsole\u003e:58)\n\tat $iwC$$iwC$$iwC$$iwC.\u003cinit\u003e(\u003cconsole\u003e:60)\n\tat $iwC$$iwC$$iwC.\u003cinit\u003e(\u003cconsole\u003e:62)\n\tat $iwC$$iwC.\u003cinit\u003e(\u003cconsole\u003e:64)\n\tat $iwC.\u003cinit\u003e(\u003cconsole\u003e:66)\n\tat \u003cinit\u003e(\u003cconsole\u003e:68)\n\tat .\u003cinit\u003e(\u003cconsole\u003e:72)\n\tat .\u003cclinit\u003e(\u003cconsole\u003e)\n\tat .\u003cinit\u003e(\u003cconsole\u003e:7)\n\tat .\u003cclinit\u003e(\u003cconsole\u003e)\n\tat $print(\u003cconsole\u003e)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:497)\n\tat org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065)\n\tat org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1340)\n\tat org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:840)\n\tat org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:871)\n\tat org.apache.spark.repl.SparkIMain.interpret(SparkIMain.scala:819)\n\tat org.apache.zeppelin.spark.SparkInterpreter.interpretInput(SparkInterpreter.java:646)\n\tat org.apache.zeppelin.spark.SparkInterpreter.interpret(SparkInterpreter.java:611)\n\tat org.apache.zeppelin.spark.SparkInterpreter.interpret(SparkInterpreter.java:604)\n\tat org.apache.zeppelin.interpreter.ClassloaderInterpreter.interpret(ClassloaderInterpreter.java:57)\n\tat org.apache.zeppelin.interpreter.LazyOpenInterpreter.interpret(LazyOpenInterpreter.java:93)\n\tat org.apache.zeppelin.interpreter.remote.RemoteInterpreterServer$InterpretJob.jobRun(RemoteInterpreterServer.java:292)\n\tat org.apache.zeppelin.scheduler.Job.run(Job.java:170)\n\tat org.apache.zeppelin.scheduler.FIFOScheduler$1.run(FIFOScheduler.java:118)\n\tat java.util.concurrent.Executors$RunnableAdapter.call(Executors.java:511)\n\tat java.util.concurrent.FutureTask.run(FutureTask.java:266)\n\tat java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.access$201(ScheduledThreadPoolExecutor.java:180)\n\tat java.util.concurrent.ScheduledThreadPoolExecutor$ScheduledFutureTask.run(ScheduledThreadPoolExecutor.java:293)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)\n\tat java.lang.Thread.run(Thread.java:745)\nCaused by: org.apache.spark.SparkException: Job cancelled because SparkContext was shut down\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$cleanUpAfterSchedulerStop$1.apply(DAGScheduler.scala:703)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$cleanUpAfterSchedulerStop$1.apply(DAGScheduler.scala:702)\n\tat scala.collection.mutable.HashSet.foreach(HashSet.scala:79)\n\tat org.apache.spark.scheduler.DAGScheduler.cleanUpAfterSchedulerStop(DAGScheduler.scala:702)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onStop(DAGScheduler.scala:1514)\n\tat org.apache.spark.util.EventLoop.stop(EventLoop.scala:84)\n\tat org.apache.spark.scheduler.DAGScheduler.stop(DAGScheduler.scala:1438)\n\tat org.apache.spark.SparkContext$$anonfun$stop$7.apply$mcV$sp(SparkContext.scala:1724)\n\tat org.apache.spark.util.Utils$.tryLogNonFatalError(Utils.scala:1185)\n\tat org.apache.spark.SparkContext.stop(SparkContext.scala:1723)\n\tat org.apache.spark.scheduler.cluster.YarnClientSchedulerBackend$MonitorThread.run(YarnClientSchedulerBackend.scala:146)\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:567)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:1824)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:1837)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:1914)\n\tat org.apache.spark.sql.execution.datasources.InsertIntoHadoopFsRelation$$anonfun$run$1.apply$mcV$sp(InsertIntoHadoopFsRelation.scala:150)\n\t... 58 more\n\n"
      },
      "dateCreated": "Feb 10, 2016 12:15:24 AM",
      "dateStarted": "Feb 11, 2016 5:29:43 AM",
      "dateFinished": "Feb 11, 2016 5:32:28 AM",
      "status": "ERROR",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Save Prediction from System ML to HDFS",
      "text": "predsysmldf.write.save(\"hdfs:///biginsights/spark-enablement/outputdata/SystemMLPrediction\")",
      "dateUpdated": "Feb 11, 2016 4:10:48 PM",
      "config": {
        "colWidth": 12.0,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        },
        "editorMode": "ace/mode/scala",
        "title": true,
        "tableHide": true
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1455064354461_1924691534",
      "id": "20160210-003234_298508933",
      "result": {
        "code": "SUCCESS",
        "type": "TEXT",
        "msg": ""
      },
      "dateCreated": "Feb 10, 2016 12:32:34 AM",
      "dateStarted": "Feb 10, 2016 3:34:45 AM",
      "dateFinished": "Feb 10, 2016 3:34:57 AM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Save Prediction  From SparkR to HDFS",
      "text": "val sparkrpred \u003d sqlContext.sql(\"select * from nycheatpred where prediction \u003e 0\")\n\nsparkrpred.write.save(\"hdfs:///biginsights/spark-enablement/outputdata/SparkRMlLibPrediction\")",
      "dateUpdated": "Feb 11, 2016 4:10:51 PM",
      "config": {
        "colWidth": 12.0,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        },
        "editorMode": "ace/mode/scala",
        "title": true,
        "tableHide": true
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1455075373176_-1262555574",
      "id": "20160210-033613_1527211327",
      "result": {
        "code": "SUCCESS",
        "type": "TEXT",
        "msg": "sparkrpred: org.apache.spark.sql.DataFrame \u003d [Address: string, Bldg_Area: double, Res_Area: double, Bldg_Age: bigint, Lot_Area: double, XCoord: double, YCoord: double, ID: double, features: vector, prediction: double]\n"
      },
      "dateCreated": "Feb 10, 2016 3:36:13 AM",
      "dateStarted": "Feb 10, 2016 3:47:06 AM",
      "dateFinished": "Feb 10, 2016 3:47:15 AM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "title": "Save Subset of Data For All Building with fields related to Heating problems",
      "text": "pluto_dfsub.write.save(\"hdfs:///biginsights/spark-enablement/outputdata/NYCBuildingSubsetData\")",
      "dateUpdated": "Feb 11, 2016 4:11:36 PM",
      "config": {
        "colWidth": 12.0,
        "graph": {
          "mode": "table",
          "height": 300.0,
          "optionOpen": false,
          "keys": [],
          "values": [],
          "groups": [],
          "scatter": {}
        },
        "editorMode": "ace/mode/scala",
        "title": true
      },
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1455166117375_-266249737",
      "id": "20160211-044837_1844526947",
      "result": {
        "code": "SUCCESS",
        "type": "TEXT",
        "msg": ""
      },
      "dateCreated": "Feb 11, 2016 4:48:37 AM",
      "dateStarted": "Feb 11, 2016 5:17:47 AM",
      "dateFinished": "Feb 11, 2016 5:18:02 AM",
      "status": "FINISHED",
      "progressUpdateIntervalMs": 500
    },
    {
      "config": {},
      "settings": {
        "params": {},
        "forms": {}
      },
      "jobName": "paragraph_1455052852564_-809835070",
      "id": "20160209-212052_1788614969",
      "dateCreated": "Feb 9, 2016 9:20:52 PM",
      "status": "READY",
      "progressUpdateIntervalMs": 500
    }
  ],
  "name": "Modeling Heating Problem In New York City",
  "id": "2BCHR4T1Q",
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