Fixes for move to TLP.
Remove "incubat*", DISCLAIMER-WIP, disclaimer clauses in page footers.
diff --git a/DISCLAIMER-WIP b/DISCLAIMER-WIP
deleted file mode 100644
index ae9f942..0000000
--- a/DISCLAIMER-WIP
+++ /dev/null
@@ -1,26 +0,0 @@
-Apache DataSketches (incubating) is an effort undergoing incubation
-at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.
-
-Incubation is required of all newly accepted projects until a further review
-indicates that the infrastructure, communications, and decision making process
-have stabilized in a manner consistent with other successful ASF projects.
-
-While incubation status is not necessarily a reflection of the
-completeness or stability of the code, it does indicate that the
-project has yet to be fully endorsed by the ASF.
-
-Some of the incubating project's releases may not be fully compliant
-with ASF policy. For example, releases may have incomplete or
-un-reviewed licensing conditions. What follows is a list of known
-issues the project is currently aware of (note that this list, by
-definition, is likely to be incomplete):
-
- * No issues are known at this time.
-
-If you are planning to incorporate this work into your
-product or project, please be aware that you will need to conduct a
-thorough licensing review to determine the overall implications of
-including this work. For the current status of this project through the Apache
-Incubator visit:
-
-http://incubator.apache.org/projects/datasketches.html
diff --git a/README.md b/README.md
index 13951ae..ec63dd4 100644
--- a/README.md
+++ b/README.md
@@ -51,6 +51,3 @@
- Lastly, submit your pull request for review!
-----
-
-Disclaimer: Apache DataSketches is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.
diff --git a/_config.yml b/_config.yml
index dcdc536..3d51edb 100644
--- a/_config.yml
+++ b/_config.yml
@@ -32,16 +32,16 @@
docs_dir: '/docs'
docs_img_dir: '/docs/img'
-docs_pdf_dir: 'https://github.com/apache/incubator-datasketches-website/tree/master/docs/pdf'
+docs_pdf_dir: 'https://github.com/apache/datasketches-website/tree/master/docs/pdf'
java_api_snapshot: 'https://datasketches.apache.org/api/java/snapshot/apidocs/index.html'
pig_api_snapshot: 'https://datasketches.apache.org/api/pig/snapshot/apidocs/index.html'
hive_api_snapshot: 'https://datasketches.apache.org/api/hive/snapshot/apidocs/index.html'
memory_api_snapshot: 'https://datasketches.apache.org/api/memory/snapshot/apidocs/index.html'
-java_readme: 'https://github.com/apache/incubator-datasketches-java/blob/master/README.md'
-pig_readme: 'https://github.com/apache/incubator-datasketches-pig/blob/master/README.md'
-hive_readme: 'https://github.com/apache/incubator-datasketches-hive/blob/master/README.md'
+java_readme: 'https://github.com/apache/datasketches-java/blob/master/README.md'
+pig_readme: 'https://github.com/apache/datasketches-pig/blob/master/README.md'
+hive_readme: 'https://github.com/apache/datasketches-hive/blob/master/README.md'
druid_datasketches: 'https://github.com/apache/druid/tree/master/extensions-core/datasketches'
diff --git a/_includes/page_footer.html b/_includes/page_footer.html
index 4aef104..2cbcee4 100644
--- a/_includes/page_footer.html
+++ b/_includes/page_footer.html
@@ -6,11 +6,8 @@
<div>Copyright © 2020 <a href="https://www.apache.org">Apache Software Foundation</a>,
Licensed under the Apache License, Version 2.0. All Rights Reserved.<br/>
Apache DataSketches, Apache, the Apache feather logo, and the Apache DataSketches project logos are trademarks of The Apache Software Foundation.<br/>
- All other marks mentioned may be trademarks or registered trademarks of their respective owners.<br/>
- <br/>
- <b>Disclaimer:</b> Apache DataSketches is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision making process have stabilized in a manner consistent with other successful ASF projects. While incubation status is not necessarily a reflection of the completeness or stability of the code, it does indicate that the project has yet to be fully endorsed by the ASF.
+ All other marks mentioned may be trademarks or registered trademarks of their respective owners.
</div>
-
</p>
</div>
</div>
diff --git a/docs/Architecture/Components.md b/docs/Architecture/Components.md
index a59036e..9f49d50 100644
--- a/docs/Architecture/Components.md
+++ b/docs/Architecture/Components.md
@@ -29,9 +29,9 @@
## Core Algorithms
If you like what you see give us a **Star** on one of these two sites!
-* **[Java](https://github.com/apache/incubator-datasketches-java)** (Versioned, Apache Released) This is the original and the most comprehensive collection of sketch algorithms. It has a dependence on the Memory component and the Java Adaptors have a dependence on this component.
+* **[Java](https://github.com/apache/datasketches-java)** (Versioned, Apache Released) This is the original and the most comprehensive collection of sketch algorithms. It has a dependence on the Memory component and the Java Adaptors have a dependence on this component.
-* **[C++](https://github.com/apache/incubator-datasketches-cpp)/[Python](https://github.com/apache/incubator-datasketches-cpp/tree/master/python)** (Versioned, Apache Released) This is newer and provides most of the major algorithms available in Java. Our C++ adaptors have a dependence on this component. The Pybind adaptors for Python are included for all the C++ sketches.
+* **[C++](https://github.com/apache/datasketches-cpp)/[Python](https://github.com/apache/datasketches-cpp/tree/master/python)** (Versioned, Apache Released) This is newer and provides most of the major algorithms available in Java. Our C++ adaptors have a dependence on this component. The Pybind adaptors for Python are included for all the C++ sketches.
## Adapters
Adapters integrate the core components into the aggregation APIs of specific data processing systems. Some of these adapters are available as part of the library, other adapters are directly integrated into the target data processing application.
@@ -52,10 +52,10 @@
them available to the PostgreSQL database users. PostgreSQL users should download the PostgreSQL extension from [pgxn.org](https://pgxn.org/dist/datasketches/). For examples refer to the README on the component site.
## Other Components
-* **[Memory](https://github.com/apache/incubator-datasketches-memory):** (Versioned, Apache Released) This is a low-level library that enables fast access to off-heap memory for Java.
-* **[Characterization](https://github.com/apache/incubator-datasketches-characterization):** This is a collection of Java and C++ code that we use for long-running studies of accuracy and speed performance over many different parameters. Feel free to run these tests to reproduce many of the graphs and charts you see on our website.
-* **[Vector (Experimental)](https://github.com/apache/incubator-datasketches-vector):** This component implements the [Frequent Directions Algorithm](/docs/Community/Research.html) [GLP16]. It is still experimental in that the theoretical work has not yet supplied a suitable measure of error for production work. It can be used as is, but it will not go through a formal Apache Release until we can find a way to provide better error properties. It has a dependence on the Memory component.
-* **[Website](https://github.com/apache/incubator-datasketches-website):** This repository is the home of our website and is constantly being updated with new material.
+* **[Memory](https://github.com/apache/datasketches-memory):** (Versioned, Apache Released) This is a low-level library that enables fast access to off-heap memory for Java.
+* **[Characterization](https://github.com/apache/datasketches-characterization):** This is a collection of Java and C++ code that we use for long-running studies of accuracy and speed performance over many different parameters. Feel free to run these tests to reproduce many of the graphs and charts you see on our website.
+* **[Vector (Experimental)](https://github.com/apache/datasketches-vector):** This component implements the [Frequent Directions Algorithm](/docs/Community/Research.html) [GLP16]. It is still experimental in that the theoretical work has not yet supplied a suitable measure of error for production work. It can be used as is, but it will not go through a formal Apache Release until we can find a way to provide better error properties. It has a dependence on the Memory component.
+* **[Website](https://github.com/apache/datasketches-website):** This repository is the home of our website and is constantly being updated with new material.
diff --git a/docs/Architecture/KeyFeatures.md b/docs/Architecture/KeyFeatures.md
index 3851725..0441e3c 100644
--- a/docs/Architecture/KeyFeatures.md
+++ b/docs/Architecture/KeyFeatures.md
@@ -29,22 +29,21 @@
* Query results are <b>approximate</b> but within well defined error bounds that are user
configurable by trading off sketch size with accuracy.
* Designed for <a href="{{site.docs_dir}}/Architecture/LargeScale.html">Large-scale</a> computing environments
- that must handle <b>Big Data</b>, e.g.,
-<a href="https://hadoop.apache.org/">Hadoop</a>,
-<a href="https://pig.apache.org/">Pig</a>,
-<a href="https://hive.apache.org/">Hive</a>,
-<a href="https://druid.apache.org">Druid</a>,
-<a href="https://spark.apache.org">Spark</a>.
-* <b>Maven deployable</b> and registered with
-<a href="https://search.maven.org/#search|ga|1|DataSketches">The Central Repository</a>.
+ that must handle <b>Big Data</b>, e.g.:
+ * [Hadoop](https://hadoop.apache.org/)
+ * [Pig](https://pig.apache.org)
+ * [Hive](https://hive.apache.org)
+ * [Druid](https://druid.apache.org)
+ * [Spark](https://spark.apache.org)
+* <b>Maven deployable</b> and registered with the [Central Repository](https://search.maven.org/#search|ga|1|DataSketches).
* Extensive documentation with the systems developer in mind.
* Designed for production environments:
- * Available in multiple languages: Java, C++, [Python](https://github.com/apache/incubator-datasketches-cpp/tree/master/python)
+ * Available in multiple languages: Java, C++, [Python](https://github.com/apache/datasketches-cpp/tree/master/python)
* Binary compatible across systems and languages
### Built-In, General Purpose Functions
-* General purpose <a href="{{site.docs_dir}}/Memory/MemoryPackage.html">Memory Package</a> for managing data off the Java Heap.
+* General purpose [Memory Package]({{site.docs_dir}}/Memory/MemoryPackage.html) for managing data off the Java Heap.
This enables systems designers the ability to manage their own large data heaps with
dedicated processor threads that would otherwise put undue pressure on the Java heap and
its garbage collection.
@@ -53,15 +52,14 @@
### Robust, High Quality Implementations.
* Unit Tests:
- * Extensive test code leveraging <a href="https://testng.org">TestNG</a>.
- * High Test Code coverage (> 90%) as measured by
-<a href="https://www.eclemma.org/jacoco/">JaCoCo</a> and published by
-<a href="https://coveralls.io">Coveralls</a>.
+ * Extensive test code leveraging [TestNG](https://testng.org).
+ * High Test Code coverage (> 90%) as measured by [JaCoCo]https://www.eclemma.org/jacoco) and published by
+[Coveralls](https://coveralls.io).
* Reproducible Characterization Studies
* All our published speed and accuracy performance results can be reproduced using the code included in the
-<a href="https://github.com/apache/incubator-datasketches-characterization">characterization</a> repository.
+[Characterization](https://github.com/apache/datasketches-characterization) repository.
* Comprehensive Javadocs that satisfy
-<a href="https://docs.oracle.com/javase/8/docs/technotes/guides/javadoc/index.html">JDK8 Javadoc</a> standards.
+[JDK8 Javadoc](https://docs.oracle.com/javase/8/docs/technotes/guides/javadoc/index.html) standards.
### Opportunities to Extend
@@ -74,29 +72,29 @@
#### Solves Computational Challenges Associated with Unique Identifiers
* <b>Estimating cardinality</b> of a stream with many duplicates
-* Performing <a href="{{site.docs_dir}}/Theta/ThetaSketchSetOps.html">set operations</a> (e.g., Union, Intersection,
+* Performing [Set Operations]({{site.docs_dir}}/Theta/ThetaSketchSetOps.html) (e.g., Union, Intersection,
and Difference) on sets of unique identifiers
* Estimates of the <b>error bounds</b> of the result can be obtained directly from the result sketch
#### Four families of Count Unique algorithms:
-* <a href="{{site.docs_dir}}/HLL/HLL.html">The HLL sketch</a>. The famous HyperLogLog algorithm when stored sketch size is of utmost concern.
-* <a href="{{site.docs_dir}}/CPC/CPC.html">The CPC sketch</a>. The Compressed Probabilistic Counting algorithm when maximizing accuracy per stored sketch size is of utmost concern.
-* <a href="{{site.docs_dir}}/Theta/ThetaSketchFramework.html">The Theta Sketch Framework</a>. Theta sketches enable real-time set-expression computations and can operate on or off the java heap.
-* <a href="{{site.docs_dir}}/Tuple/TupleOverview.html">The Tuple Sketch</a>. Tuple sketches are associative sketches that are useful for performing approximate join operations and extracting other kinds of statistical behavior associated with unique identifiers.
+* [The HLL Sketch]({{site.docs_dir}}/HLL/HLL.html). The famous HyperLogLog algorithm when stored sketch size is of utmost concern.
+* [The CPC Sketch]({{site.docs_dir}}/CPC/CPC.html). The Compressed Probabilistic Counting algorithm when maximizing accuracy per stored sketch size is of utmost concern.
+* [The Theta Sketch Framework]({{site.docs_dir}}/Theta/ThetaSketchFramework.html). Theta sketches enable real-time set-expression computations and can operate on or off the java heap.
+* [The Tuple Sketch]({{site.docs_dir}}/Tuple/TupleOverview.html). Tuple sketches are associative sketches that are useful for performing approximate join operations and extracting other kinds of statistical behavior associated with unique identifiers.
### Quantiles
-* <a href="{{site.docs_dir}}/Quantiles/QuantilesOverview.html">Quantiles Overview</a>. Get normal or inverse PDFs or CDFs of the distributions of any numeric value from your raw data in a single pass with well defined error bounds on the results.
+* [Quantiles Overview]({{site.docs_dir}}/Quantiles/QuantilesOverview.html). Get normal or inverse PDFs or CDFs of the distributions of any numeric value from your raw data in a single pass with well defined error bounds on the results.
### Frequent Items
-* <a href="{{site.docs_dir}}/Frequency/FrequencySketchesOverview.html">Frequent Items Sketches</a> Get the most frequent items from a stream of items.
+* [Frequent Items Sketches]({{site.docs_dir}}/Frequency/FrequencySketchesOverview.html) Get the most frequent items from a stream of items.
### Sampling
-* <a href="{{site.docs_dir}}/Sampling/ReservoirSampling.html">Reservoir Sampling</a> Knuth's well known Reservoir sampling "Algorithm R", but extended to enable merging across different sized reservoirs.
-* <a href="{{site.docs_dir}}/Sampling/VarOptSampling.html">Weighted Sampling</a> Edith Cohen's famous sampling algorithm that enables computing subset sums of weighted samples with optimum variance.
+* [Reservoir Sampling]({{site.docs_dir}}/Sampling/ReservoirSampling.html) Knuth's well known Reservoir sampling "Algorithm R", but extended to enable merging across different sized reservoirs.
+* [Weighted Sampling]({{site.docs_dir}}/Sampling/VarOptSampling.html) Edith Cohen's famous sampling algorithm that enables computing subset sums of weighted samples with optimum variance.
diff --git a/docs/Architecture/LargeScale.md b/docs/Architecture/LargeScale.md
index afedfaa..449e67a 100644
--- a/docs/Architecture/LargeScale.md
+++ b/docs/Architecture/LargeScale.md
@@ -29,7 +29,7 @@
* [C++ Core](https://datasketches.apache.org/docs/Community/Downloads.html)
* The C++ core is written as all header files allowing easy integration into a wide range of operating system environments.
-* [Python](https://github.com/apache/incubator-datasketches-cpp/tree/master/python)
+* [Python](https://github.com/apache/datasketches-cpp/tree/master/python)
* The C++ Core is extended using the python binding library [pybind11](https://github.com/pybind/pybind11) enabling high performance operation from Python.
### Cross Language Binary Compatibility
@@ -59,10 +59,10 @@
* <b>Hash Seed Handling</b>. Additional protection for managing hash seeds which is
particularly important when processing sensitive user identifiers. Available with Theta Sketches.
-* <a href="{{site.docs_dir}}/Theta/ThetaPSampling.html"><b>Pre-Sampling</b></a>. Built-in up-front sampling for cases where additional
+* [Pre-Sampling]({{site.docs_dir}}/Theta/ThetaPSampling.html). Built-in up-front sampling for cases where additional
contol is required to limit overall memory consumption when dealing with millions of sketches. Available with Theta Sketches.
-* <a href="{{site.docs_dir}}/Memory/MemoryPackage.html"><b>Memory Package</b></a>.
+* [Memory Package]({{site.docs_dir}}/Memory/MemoryPackage.html).
Large query systems often require their own heaps outside the JVM in order to better manage garbage collection latencies.
The Java sketches utilize this powerful package.
diff --git a/docs/Architecture/MajorSketchFamilies.md b/docs/Architecture/MajorSketchFamilies.md
index c511041..5381b43 100644
--- a/docs/Architecture/MajorSketchFamilies.md
+++ b/docs/Architecture/MajorSketchFamilies.md
@@ -39,26 +39,26 @@
Computing cardinalities with massive data requires lots of computer resources and time.
However, if an approximate answer to these problems is acceptable, [Theta Sketches]({{site.docs_dir}}/Theta/ThetaSketchFramework.html) can provide reasonable estimates, in a single pass, orders of magnitude faster, even fast enough for analysis in near-real time.
-The [theta/Sketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/theta/Sketch.java) can operate both on-heap and off-heap, has powerful Union, Intersection, AnotB and Jaccard operators, has a high-performance concurrent form for multi-threaded environments, has both immutable compact, and updatable representations, and is quite fast. Because of its flexibility, it is one of the most popular sketches in our library.
+The [theta/Sketch](https://github.com/apache/datasketches-java/blob/master/src/main/java/org/apache/datasketches/theta/Sketch.java) can operate both on-heap and off-heap, has powerful Union, Intersection, AnotB and Jaccard operators, has a high-performance concurrent form for multi-threaded environments, has both immutable compact, and updatable representations, and is quite fast. Because of its flexibility, it is one of the most popular sketches in our library.
### [Tuple Sketches]({{site.docs_dir}}/Tuple/TupleOverview.html): Extending Theta Sketches to Perform Associative Analysis
-It is often not enough to perform stream expressions on sets of unique identifiers, it is very valuable to be able to associate additive data with these identifiers, such as impression counts, clicks or timestamps. Tuple Sketches are a natural extension of the Theta sketch and have Java Genric forms that enable the user to define the sketch with arbitrary "summary" data. The [tuple/Sketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/tuple/Sketch.java) can operate both on-heap and off-heap, includes both Union and Intersection operators, and has both immutable compact and updatable representations.
+It is often not enough to perform stream expressions on sets of unique identifiers, it is very valuable to be able to associate additive data with these identifiers, such as impression counts, clicks or timestamps. Tuple Sketches are a natural extension of the Theta sketch and have Java Genric forms that enable the user to define the sketch with arbitrary "summary" data. The [tuple/Sketch](https://github.com/apache/datasketches-java/blob/master/src/main/java/org/apache/datasketches/tuple/Sketch.java) can operate both on-heap and off-heap, includes both Union and Intersection operators, and has both immutable compact and updatable representations.
The Tuple sketch is effectively infinitely extendable and there are several common variants of the Tuple Sketch, which also serve as examples on how to extend the base classes that are also in the library, including:
-- [tuple/adouble/DoubleSketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/tuple/adouble/DoubleSketch.java) with a single column of *double* values as the *summary*.
-- [tuple/aninteger/IntegerSketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/tuple/aninteger/IntegerSketch.java) with a single column of *int* values as the *summary*.
-- [tuple/strings/ArrayOfStringsSketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/tuple/strings/ArrayOfStringsSketch.java), which is effectively a variable number of columns of strings as the *summary*.
-- [tuple/ArrayOfDoublesSketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/tuple/ArrayOfDoublesSketch.java), which enables the user to specify the number of columns of double values as the *summary*. This variant also provides both on-heap and off-heap operation.
+- [tuple/adouble/DoubleSketch](https://github.com/apache/datasketches-java/blob/master/src/main/java/org/apache/datasketches/tuple/adouble/DoubleSketch.java) with a single column of *double* values as the *summary*.
+- [tuple/aninteger/IntegerSketch](https://github.com/apache/datasketches-java/blob/master/src/main/java/org/apache/datasketches/tuple/aninteger/IntegerSketch.java) with a single column of *int* values as the *summary*.
+- [tuple/strings/ArrayOfStringsSketch](https://github.com/apache/datasketches-java/blob/master/src/main/java/org/apache/datasketches/tuple/strings/ArrayOfStringsSketch.java), which is effectively a variable number of columns of strings as the *summary*.
+- [tuple/ArrayOfDoublesSketch](https://github.com/apache/datasketches-java/blob/master/src/main/java/org/apache/datasketches/tuple/ArrayOfDoublesSketch.java), which enables the user to specify the number of columns of double values as the *summary*. This variant also provides both on-heap and off-heap operation.
### [HyperLogLog Sketches]({{site.docs_dir}}/HLL/HLL.html): Estimating Stream Cardinalities
The HyperLogLog (HLL) is a cardinality sketch similar to the above Theta sketches except they are anywhere from 2 to 16 times smaller in size. The HLL sketches can be merged via the Union operator, but set intersection and difference operations are not provided intrinsically, because the resulting error would be quite poor. If your application only requires cardinality estimation and merging and space is at a premium, the HLL or CPC sketches would be your best choice.
-The [hll/HllSketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/hll/HllSketch.java) can operate both on-heap and off-heap, provides the Union operators, and has both immutable compact and updatable representations.
+The [hll/HllSketch](https://github.com/apache/datasketches-java/blob/master/src/main/java/org/apache/datasketches/hll/HllSketch.java) can operate both on-heap and off-heap, provides the Union operators, and has both immutable compact and updatable representations.
### [HyperLogLog Map Sketch]({{site.docs_dir}}/HLL/HllMap.html): Estimating Stream Cardinalities of Key-Value Pairs
-This is a specially designed sketch that addresses the problem of individually tracking value cardinalities of Key-Value (K,V) pairs in real-time, where the number of keys can be very large, such as IP addresses, or Geo identifiers, etc. Assigning individual sketches to each key would create unnecessary overhead. This sketch streamlines the process with much better space management. This [hllmap/UniqueCountMap](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/hllmap/UniqueCountMap.java) only operates on heap and does not provide Union capabilites. This sketch is effectively a optimized hash-map of HLL sketches. Like other hash-maps, the overall size is a direct function of the number of keys that it has seen. This sketch was designed to operate as a stand-alone sketch.
+This is a specially designed sketch that addresses the problem of individually tracking value cardinalities of Key-Value (K,V) pairs in real-time, where the number of keys can be very large, such as IP addresses, or Geo identifiers, etc. Assigning individual sketches to each key would create unnecessary overhead. This sketch streamlines the process with much better space management. This [hllmap/UniqueCountMap](https://github.com/apache/datasketches-java/blob/master/src/main/java/org/apache/datasketches/hllmap/UniqueCountMap.java) only operates on heap and does not provide Union capabilites. This sketch is effectively a optimized hash-map of HLL sketches. Like other hash-maps, the overall size is a direct function of the number of keys that it has seen. This sketch was designed to operate as a stand-alone sketch.
## Quantiles Sketches
@@ -67,37 +67,37 @@
<img class="doc-img-full" src="{{site.docs_img_dir}}/quantiles/TimeSpentHistogram.png" alt="TimeSpentHistogram" />
-There are two different families of quantiles sketches, the original [quantiles/DoublesSketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/quantiles/DoublesSketch.java), which can be operated either on-heap or off-heap, and is also available in a Java Generic form for arbitrary comparable objects.
+There are two different families of quantiles sketches, the original [quantiles/DoublesSketch](https://github.com/apache/datasketches-java/blob/master/src/main/java/org/apache/datasketches/quantiles/DoublesSketch.java), which can be operated either on-heap or off-heap, and is also available in a Java Generic form for arbitrary comparable objects.
-Later we developed the [kll/KllFloatsSketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/kll/KllFloatsSketch.java) (Named after its authors), which is also a quantiles sketch, that achieves near optimal small size for a given accuracy.
+Later we developed the [kll/KllFloatsSketch](https://github.com/apache/datasketches-java/blob/master/src/main/java/org/apache/datasketches/kll/KllFloatsSketch.java) (Named after its authors), which is also a quantiles sketch, that achieves near optimal small size for a given accuracy.
## Frequent Items / Heavy Hitters Sketches
### [Frequent Items Sketches]({{site.docs_dir}}/Frequency/FrequentItemsOverview.html): Finding the Heavy Hitter Objects from a Stream
It is very useful to be able to scan a stream of objects, such as song titles, and be able to quickly identify those titles that occur most frequently. The term <i>Heavy Hitter</i> is defined to be an item that occurs more frequently than its fair share of occurrences. This "fair share" is simply the total count of all occurrences of all items divided by the number of distinct items. Suppose you have a stream of 1M song titles, but in that stream there are only 100K song titles that are unique. If any single title consumes more than 10% of the stream elements it is a Heavy Hitter. The 10% is a threshold parameter we call epsilon.
-The accuracy of a Frequent Items Sketch is proportional to the configured size of the sketch, the larger the sketch, the smaller is the epsilon threshold that can detect Heavy Hitters. This sketch is available in two forms, as the [frequencies/LongsSketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/frequencies/LongsSketch.java) used for processing a stream of tuples {*long*, weight}, and the [frequencies/ItemsSketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/frequencies/ItemsSketch.java) used for processing tuples of {*T*, weight}, where *T* is arbitrary, uniquely identifyable object. These sketches are aggregating sketches in that the frequency of occurances of an item (or *long* key), is accumulated as the stream is processed.
+The accuracy of a Frequent Items Sketch is proportional to the configured size of the sketch, the larger the sketch, the smaller is the epsilon threshold that can detect Heavy Hitters. This sketch is available in two forms, as the [frequencies/LongsSketch](https://github.com/apache/datasketches-java/blob/master/src/main/java/org/apache/datasketches/frequencies/LongsSketch.java) used for processing a stream of tuples {*long*, weight}, and the [frequencies/ItemsSketch](https://github.com/apache/datasketches-java/blob/master/src/main/java/org/apache/datasketches/frequencies/ItemsSketch.java) used for processing tuples of {*T*, weight}, where *T* is arbitrary, uniquely identifyable object. These sketches are aggregating sketches in that the frequency of occurances of an item (or *long* key), is accumulated as the stream is processed.
### [Frequent Distinct Tuples Sketch]({{site.docs_dir}}/Frequency/FrequentDistinctTuplesSketch.html): Finding the Heavy Hitter tuples from a Stream.
Suppose our data is a stream of pairs {IP address, User ID} and we want to identify the IP addresses that have the most distinct User IDs. Or conversely, we would like to identify the User IDs that have the most distinct IP addresses. This is a common challenge in the analysis of big data and the FDT sketch helps solve this problem using probabilistic techniques.
More generally, given a multiset of tuples with *N* dimensions *{d1,d2, d3, …, dN}*, and a primary subset of dimensions *M < N*, our task is to identify the combinations of *M* subset dimensions that have the most frequent number of distinct combinations of the *N - M* non-primary dimensions.
-The [fdt/FdtSketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/fdt/FdtSketch.java) is currently only available in Java, but because it is an extension of the Tuple Sketch family, it inherits many of the same properties: it can operate both on-heap and off-heap, it includes both Union and Intersection operators, and it has both immutable compact and updatable representations.
+The [fdt/FdtSketch](https://github.com/apache/datasketches-java/blob/master/src/main/java/org/apache/datasketches/fdt/FdtSketch.java) is currently only available in Java, but because it is an extension of the Tuple Sketch family, it inherits many of the same properties: it can operate both on-heap and off-heap, it includes both Union and Intersection operators, and it has both immutable compact and updatable representations.
### Frequent Directions: Distributed, mergeable Singular Value Decomposition
-Part of a new separate datasketches-vector component, Frequent Directions is in many ways a generalization of the Frequent Items sketch in order to handle vector data. This sketch computes an approximate singular value decomposition (SVD) of a matrix, providing a projection matrix that can be used for dimensionality reduction. SVD is a key technique in many recommender systems, such as providing shopping suggestions based on a customer's past purchases compared with other similar customers. This sketch is still experimental and feedback from interested users would be welcome. This sketch can be found in the [Vector](https://github.com/apache/incubator-datasketches-vector) repository dedicated to vector and matrix sketches.
+Part of a new separate datasketches-vector component, Frequent Directions is in many ways a generalization of the Frequent Items sketch in order to handle vector data. This sketch computes an approximate singular value decomposition (SVD) of a matrix, providing a projection matrix that can be used for dimensionality reduction. SVD is a key technique in many recommender systems, such as providing shopping suggestions based on a customer's past purchases compared with other similar customers. This sketch is still experimental and feedback from interested users would be welcome. This sketch can be found in the [Vector](https://github.com/apache/datasketches-vector) repository dedicated to vector and matrix sketches.
## Sampling Sketches
### [Sampling Sketches]({{site.docs_dir}}/Sampling/ReservoirSampling.html): Uniform and Weighted Sampling of a Stream into a fixed size space
This family of sketches implements an enhanced version of the famous Reservoir sampling algorithm and extends it with the capabilities that large-scale distributed systems really need: mergability (even with different sized sketches). The Java implementaion uses Java Generics so that the base classes can be trivially extended for any input type (even polymorphic types), and also enables an extensible means of performing serialization and deserialization.
-The [sampling/ReservoirLongsSketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/sampling/ReservoirLongsSketch.java) accepts a stream of *long* values as identifiers with a weight of one, and produces a result Reservoir of a pre-determined size that represents a uniform random sample of the stream.
+The [sampling/ReservoirLongsSketch](https://github.com/apache/datasketches-java/blob/master/src/main/java/org/apache/datasketches/sampling/ReservoirLongsSketch.java) accepts a stream of *long* values as identifiers with a weight of one, and produces a result Reservoir of a pre-determined size that represents a uniform random sample of the stream.
-The [sampling/ReservoirItemsSketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/sampling/ReservoirItemsSketch.java) accepts a stream of type *T* as identifiers with a weight of one, and produces a result Reservoir of a pre-determined size that represents a uniform random sample of the stream.
+The [sampling/ReservoirItemsSketch](https://github.com/apache/datasketches-java/blob/master/src/main/java/org/apache/datasketches/sampling/ReservoirItemsSketch.java) accepts a stream of type *T* as identifiers with a weight of one, and produces a result Reservoir of a pre-determined size that represents a uniform random sample of the stream.
-The [sampling/VarOptItemsSketch](https://github.com/apache/incubator-datasketches-java/blob/master/src/main/java/org/apache/datasketches/sampling/VarOptItemsSketch.java) extends the Reservoir family to weighted sampling, additionally providing subset sum estimates from the sample with provably optimal variance.
+The [sampling/VarOptItemsSketch](https://github.com/apache/datasketches-java/blob/master/src/main/java/org/apache/datasketches/sampling/VarOptItemsSketch.java) extends the Reservoir family to weighted sampling, additionally providing subset sum estimates from the sample with provably optimal variance.
diff --git a/docs/Architecture/SketchFeaturesMatrix.md b/docs/Architecture/SketchFeaturesMatrix.md
index 1cc0ae7..93713bb 100644
--- a/docs/Architecture/SketchFeaturesMatrix.md
+++ b/docs/Architecture/SketchFeaturesMatrix.md
@@ -63,7 +63,7 @@
<sup>4</sup> Theta Sketch: C++/Python has no implementaion of the Jaccard, yet.<br>
<sup>5</sup> Frequent Items Sketch: PostgreSQL implemented for Strings only.<br>
<sup>6</sup> KLL Sketch: Python implemented for both just floats and ints.<br>
-<sup>7</sup> See [Python Install Instructions](https://github.com/apache/incubator-datasketches-cpp/tree/master/python)<br>
+<sup>7</sup> See [Python Install Instructions](https://github.com/apache/datasketches-cpp/tree/master/python)<br>
## Definitions
diff --git a/docs/Architecture/SketchesByComponent.md b/docs/Architecture/SketchesByComponent.md
index 37f5a24..2cd0575 100644
--- a/docs/Architecture/SketchesByComponent.md
+++ b/docs/Architecture/SketchesByComponent.md
@@ -25,11 +25,11 @@
## Java
-### incubator-datasketches-java
+### datasketches-java
This repository has the core-java sketching classes, which are leveraged by some of the other repositories.
This repository has no external dependencies outside of the DataSketches/memory repository, Java and TestNG for unit tests.
This code is versioned and the latest release can be obtained from
-<a href="https://www.apache.org/dyn/closer.cgi?path=/incubator/datasketches/java">incubator-datasketches-java</a>.
+[Downloads](https://datasketches.apache.org/docs/Community/Downloads.html).
<b>High-level Repositories Structure</b>
@@ -51,21 +51,21 @@
org.apache.datasketches.tuple.aninteger | A Tuple sketch with a Summary of a single integer
org.apache.datasketches.tuple.Strings | A Tuple sketch with a Summary of an array of Strings
-### incubator-datasketches-memory
+### datasketches-memory
This code is versioned and the latest release can be obtained from
-<a href="https://www.apache.org/dyn/closer.cgi?path=/incubator/datasketches/memory">incubator-datasketches-memory</a>.
+[Downloads](https://datasketches.apache.org/docs/Community/Downloads.html).
Memory Packages | Package Description
-------------------------------|---------------------
org.apache.datasketches.memory | Low level, high-performance Memory data-structure management primarily for off-heap.
-### incubator-datasketches-hive
+### datasketches-hive
This repository contains Hive UDFs and UDAFs for use within Hadoop grid enviornments.
This code has dependencies on sketches-core as well as Hadoop and Hive.
Users of this code are advised to use Maven to bring in all the required dependencies.
This code is versioned and the latest release can be obtained from
-<a href="https://www.apache.org/dyn/closer.cgi?path=/incubator/datasketches/hive">incubator-datasketches-hive</a>.
+[Downloads](https://datasketches.apache.org/docs/Community/Downloads.html).
Sketches-hive Packages | Package Description
-------------------------------------|---------------------
@@ -77,12 +77,12 @@
org.apache.datasketches.hive.theta | Hive UDF and UDAFs for Theta sketches
org.apache.datasketches.hive.tuple | Hive UDF and UDAFs for Tuple sketches
-### incubator-datasketches-pig
+### datasketches-pig
This repository contains Pig User Defined Functions (UDF) for use within Hadoop grid environments.
This code has dependencies on sketches-core as well as Hadoop and Pig.
Users of this code are advised to use Maven to bring in all the required dependencies.
This code is versioned and the latest release can be obtained from
-<a href="https://www.apache.org/dyn/closer.cgi?path=/incubator/datasketches/pig">incubator-datasketches-pig</a>.
+[Downloads](https://datasketches.apache.org/docs/Community/Downloads.html).
Sketches-pig Packages | Package Description
-----------------------------------|---------------------
@@ -97,9 +97,11 @@
org.apache.datasketches.pig.tuple | Pig UDFs for Tuple sketches
-### incubator-datasketches-characterization
+### datasketches-characterization
This relatively new repository is for Java and C++ code that we use to characterize the accuracy and speed performance of the sketches in
-the library and is constantly being updated. Examples of the job command files used for various tests can be found in the src/main/resources directory. Some of these tests can run for hours depending on its configuration.
+the library and is constantly being updated. Examples of the job command files used for various tests can be found in the src/main/resources directory.
+Some of these tests can run for hours depending on its configuration. This component is not formally released and code must be obtained from
+the [GitHub site](https://github.com/apache/datasketches-characterization).
Characterization Packages | Package Description
------------------------------------------------|---------------------
@@ -115,6 +117,11 @@
org.apache.datasketches.characterization.theta | Theta Sketch
org.apache.datasketches.characterization.uniquecount | Base Profiles for Unique Counting Sketches
+### datasketches-server
+This is a new repository for our experimental docker/container server that enables easy access to the core sketches in the library via HTTP.
+This component is not formally released and code must be obtained from
+the [GitHub site](https://github.com/apache/datasketches-server).
+
#### C++ Characterizations
* CPC
* Frequent Items
@@ -123,22 +130,27 @@
* Theta
-### incubator-datasketches-vector
+### datasketches-vector
This component implements the [Frequent Directions Algorithm](/docs/Community/Research.html) [GLP16]. It is still experimental in that the theoretical work has not yet supplied a suitable measure of error for production work. It can be used as is, but it will not go through a formal Apache Release until we can find a way to provide better error properties. It has a dependence on the Memory component.
+This component is not formally released and code must be obtained from
+the [GitHub site](https://github.com/apache/datasketches-vector).
-## C++ and [Python](https://github.com/apache/incubator-datasketches-cpp/tree/master/python)
+## C++ and Python
-### incubator-datasketches-cpp
+### datasketches-cpp
This is the evolving C++ implementations of the same sketches that are available in Java.
These implementations are *binary compatible* with their counterparts in Java.
In other words, a sketch created and stored in C++ can be opened and read in Java and visa-versa.
+This code is versioned and the latest release can be obtained from
+[Downloads](https://datasketches.apache.org/docs/Community/Downloads.html).
-This site also has our [Python adaptors](https://github.com/apache/incubator-datasketches-cpp/tree/master/python) that basically wrap the C++ implementations, making the high performance C++ implementations available from Python.
+This site also has our [Python adaptors](https://github.com/apache/datasketches-cpp/tree/master/python) that basically wrap the C++ implementations, making the high performance C++ implementations available from Python.
-### incubator-datasketches-postgresql
+### datasketches-postgresql
This site provides the postgres-specific adaptors that wrap the C++ implementations making
-them available to the PostgreSQL database users. PostgreSQL users should download the PostgreSQL extension from [pgxn.org](https://pgxn.org/dist/datasketches/). For examples refer to the README on the component site.
+them available to the PostgreSQL database users. PostgreSQL users should download the PostgreSQL extension from [pgxn.org](https://pgxn.org/dist/datasketches/). For examples refer to the README on the component site. This code is versioned and the latest release can be obtained from
+[Downloads](https://datasketches.apache.org/docs/Community/Downloads.html).
diff --git a/docs/CPC/CpcPerformance.md b/docs/CPC/CpcPerformance.md
index 87da9b5..e83f576 100644
--- a/docs/CPC/CpcPerformance.md
+++ b/docs/CPC/CpcPerformance.md
@@ -21,7 +21,7 @@
-->
# Performance of CPC sketch
-The code used to produce the data for these plots can be found in the [characterization repository](https://github.com/apache/incubator-datasketches-characterization)
+The code used to produce the data for these plots can be found in the [characterization repository](https://github.com/apache/datasketches-characterization)
<img class="doc-img-full" src="{{site.docs_img_dir}}/cpc/CPC_sketch_accuracy_lgk12.png"/>
diff --git a/docs/Community/Downloads.md b/docs/Community/Downloads.md
index 7ec7d68..e7b4793 100644
--- a/docs/Community/Downloads.md
+++ b/docs/Community/Downloads.md
@@ -21,7 +21,7 @@
-->
# Downloads
-## [Signing Keys](https://downloads.apache.org/incubator/datasketches/KEYS)
+## [Signing Keys](https://downloads.apache.org/datasketches/KEYS)
It is essential that you verify the integrity of release downloads. See [instructions here.](https://www.apache.org/dyn/closer.cgi#verify)
## Latest Source Zip Files
@@ -32,11 +32,11 @@
From [Maven Central](https://search.maven.org/search?q=g:%20org.apache.datasketches).
## Enabling Python
-* First download the C++ core above, then read the [Python Installation Instructions](https://github.com/apache/incubator-datasketches-cpp/tree/master/python)
+* First download the C++ core above, then read the [Python Installation Instructions](https://github.com/apache/datasketches-cpp/tree/master/python)
## Download Earlier Versions
-* **[ZIP Files](http://archive.apache.org/dist/incubator/datasketches/java/)**
+* **[ZIP Files](http://archive.apache.org/dist/datasketches/java/)** OR **[ZIP Files](http://archive.apache.org/dist/incubator/datasketches/java/)**
* **[Java Jar Files](https://search.maven.org/search?q=g:%20org.apache.datasketches)**
## Version Numbers
@@ -48,8 +48,8 @@
Note: we maintain *forward binary compatiblity* of a specific sketch type. For example, a Java Theta Sketch stored as a binary image using version 0.1.0 (Aug, 2015) can be merged into a C++ Theta Union version 1.2.0 (Jan, 2020). The languages can be swapped in this scenario, but the versions cannot be.
-The zip files downloaded from [incubator-datasketches-xxx](https://www.apache.org/dyn/closer.cgi?path=/incubator/datasketches)
-include a version number in the name, as in _apache-datasketches-java-1.1.0-incubating-src.zip_.
+The zip files downloaded from [datasketches-xxx](https://www.apache.org/dyn/closer.cgi?path=/datasketches)
+include a version number in the name, as in _apache-datasketches-java-1.1.0-src.zip_.
This same number is also in the top section of the pom.xml file.
If you are developing using Maven and want to use, for example, datasketches-java, add the following dependencies to your pom.xml file:
@@ -58,7 +58,7 @@
<dependency>
<groupId>org.apache.datasketches</groupId>
<artifactId>datasketches-java</artifactId>
- <version>1.2.0-incubating</version>
+ <version>1.2.0</version>
</dependency>
```
@@ -74,7 +74,7 @@
As stated above, the <b>major</b> digit is being reserved for major refactorings of the library where backward API
compatibility may not be guaranteed.
-For the repositories that depend on java core, such as <i>incubator-datasketches-hive</i>,
+For the repositories that depend on java core, such as <i>datasketches-hive</i>,
the version number may be incremented just to be in sync with the java core repository,
and may not reflect any actual code changes other than a change in the pom.xml to reflect the new
dependency.
diff --git a/docs/Community/KDD_Tutorial_Summary.md b/docs/Community/KDD_Tutorial_Summary.md
index 828fe70..c0e97e5 100644
--- a/docs/Community/KDD_Tutorial_Summary.md
+++ b/docs/Community/KDD_Tutorial_Summary.md
@@ -107,7 +107,7 @@
2. Accepting approximation
3. Understanding error
4. System planning
- 4. Demonstration in [Python](https://github.com/apache/incubator-datasketches-cpp/tree/master/python)
+ 4. Demonstration in [Python](https://github.com/apache/datasketches-cpp/tree/master/python)
1. Examples of several sketches
2. Deeper dive with sampling
4. Extra topics (Time permitting) (10 minutes)
@@ -123,7 +123,7 @@
## Contributor Bio
-Lee Rhodes is a Distinguished Architect at Yahoo (now Verizon Media). He created the DataSketches project in 2012 to address analysis problems in Yahoo's large data processing pipelines. DataSketches was Open Sourced in 2015 and is now in incubation at Apache Software Foundation. He was an author or coauthor on sketching work published in ICDT, IMC, and JCGS. He obtained his Master's Degree in Electrical Engineering from Stanford University.
+Lee Rhodes is a Distinguished Architect at Yahoo (now Verizon Media). He created the DataSketches project in 2012 to address analysis problems in Yahoo's large data processing pipelines. DataSketches was Open Sourced in 2015 and is now a top level project in the Apache Software Foundation. He was an author or coauthor on sketching work published in ICDT, IMC, and JCGS. He obtained his Master's Degree in Electrical Engineering from Stanford University.
## Societal Impact
@@ -133,7 +133,7 @@
1. P. K. Agarwal, G. Cormode, Z. Huang, J. M. Phillips, Z. Wei, and K. Yi. Mergeable Summaries. ACM Transactions on Database Systems, 38(4):26, 2013.
1. N. Alon, Y. Matias, and M. Szegedy. The space complexity of approximating the frequency moments. Journal of Computer and System Sciences, 58(1):137–147, 1999.
-1. Apache. Datasketches (incubating). https://datasketches.apache.org, 2020.
+1. Apache. Datasketches. https://datasketches.apache.org, 2020.
1. Apache. Druid. https://druid.apache.org, 2020.
1. A. Broder and M. Mitzenmacher. Network applications of bloom filters: A survey. Internet mathematics, 1(4):485–509, 2004.
1. E. Cohen. All-distances sketches, revisited: Hip estimators for massive graphsanalysis.IEEE Transactions on Knowledge and Data Engineering, 27(9):2320–2334, 2015.
diff --git a/docs/Community/NewCommitterProcess.md b/docs/Community/NewCommitterProcess.md
index 22434c0..3aea9d7 100644
--- a/docs/Community/NewCommitterProcess.md
+++ b/docs/Community/NewCommitterProcess.md
@@ -151,7 +151,7 @@
Hello [Candidate]
-The datasketches(incubating) Project Management Committee (PMC) hereby offers
+The datasketches Project Management Committee (PMC) hereby offers
you committer privileges to the project. These privileges are offered on
the understanding that you'll use them reasonably and with common sense. We
like to work on trust rather than unnecessary constraints.
@@ -205,7 +205,7 @@
* <https://www.apache.org/foundation/how-it-works.html>
* <https://www.apache.org/dev/>
-The incubator also has some useful information for new committers in incubating projects:
+The Apache Incubator project also has some useful information for new committers:
* <https://incubator.apache.org/guides/committer.html>
* <https://incubator.apache.org/guides/ppmc.html>
@@ -242,7 +242,7 @@
If the ICLA identifies the project and a valid Apache id, and the [RESULT][VOTE] message has been posted to the PMC private list, then the account creation request is made by the secretary or assistant who files the ICLA.
-Otherwise, new account requests will only be accepted from PMC chairs and ASF members. Projects in incubation can ask their Mentors to make the account request for them.
+Otherwise, new account requests will only be accepted from PMC chairs and ASF members.
* See <https://www.apache.org/dev/pmc.html#newcommitter>
@@ -269,16 +269,16 @@
As a committer on the DataSketches Project you will have commit access to the following DataSketches repositories as follows:
-* https://github.com/apache/incubator-datasketches-java
-* https://github.com/apache/incubator-datasketches-cpp
-* https://github.com/apache/incubator-datasketches-hive
-* https://github.com/apache/incubator-datasketches-pig
-* https://github.com/apache/incubator-datasketches-postgresql
-* https://github.com/apache/incubator-datasketches-characterization
-* https://github.com/apache/incubator-datasketches-website
-* https://github.com/apache/incubator-datasketches-memory
-* https://github.com/apache/incubator-datasketches-vector
-* https://github.com/apache/incubator-datasketches-server
+* https://github.com/apache/datasketches-java
+* https://github.com/apache/datasketches-cpp
+* https://github.com/apache/datasketches-hive
+* https://github.com/apache/datasketches-pig
+* https://github.com/apache/datasketches-postgresql
+* https://github.com/apache/datasketches-characterization
+* https://github.com/apache/datasketches-website
+* https://github.com/apache/datasketches-memory
+* https://github.com/apache/datasketches-vector
+* https://github.com/apache/datasketches-server
Please become familiar with the following ASF resources:
diff --git a/docs/Community/Research.md b/docs/Community/Research.md
index e1bab92..02db366 100644
--- a/docs/Community/Research.md
+++ b/docs/Community/Research.md
@@ -113,7 +113,7 @@
**[CKLTV+20]** Graham Cormode, Zohar Karnin, Edo Liberty, Justin Thaler, Pavel Vesely. Relative Error Streaming Quantiles, In *arXiv.org*, 2020.
-**[DLRT16]** Anirban Dasgupta, Kevin J. Lang, Lee Rhodes, and Justin Thaler. A framework for estimating stream expression cardinalities. In *EDBT/ICDT Proceedings '16 *, pages 6:1–6:17, 2016. [Paper](https://arxiv.org/pdf/1510.01455.pdf). [Talk Slides](https://github.com/apache/incubator-datasketches-website/blob/master/docs/pdf/icdt-talk.pdf).
+**[DLRT16]** Anirban Dasgupta, Kevin J. Lang, Lee Rhodes, and Justin Thaler. A framework for estimating stream expression cardinalities. In *EDBT/ICDT Proceedings '16 *, pages 6:1–6:17, 2016. [Paper](https://arxiv.org/pdf/1510.01455.pdf). [Talk Slides](https://github.com/apache/datasketches-website/blob/master/docs/pdf/icdt-talk.pdf).
**[FFGM07]** Philippe Flajolet, E ́ric Fusy, Olivier Gandouet, and Fre ́de ́ric Meunier. Hyperloglog: the analysis of a near-optimal cardinality estimation algorithm. In *DMTCS Conference on Analysis of Algorithms*, pages 137–156, 2007.
diff --git a/docs/Community/Transitioning.md b/docs/Community/Transitioning.md
index dc07c3b..74cbf82 100644
--- a/docs/Community/Transitioning.md
+++ b/docs/Community/Transitioning.md
@@ -22,7 +22,7 @@
# Transitioning from our previous GitHub Site.
-After 8 years of development and 5 years in Open Source, we began in May, 2019 the important migration from a stand-alone GitHub site to being a member of the [Apache Software Foundation](https://apache.org) community.
+After 8 years of development and 5 years in Open Source, we began in May, 2019 the important migration from a stand-alone GitHub site to being a member of the [Apache Software Foundation](https://apache.org) community.
While we undergo this migration, we beg your patience.
@@ -34,26 +34,26 @@
* Web Site and Web Site Source
* **datasketches.github.io** moved to [datasketches.apache.org](https://datasketches.apache.org)
- * **github.com/DataSketches/DataSketches.github.io** moved to [github.com/apache/incubator-datasketches-website](https://github.com/apache/incubator-datasketches-website)
+ * **github.com/DataSketches/DataSketches.github.io** moved to [github.com/apache/datasketches-website](https://github.com/apache/datasketches-website)
* Java Core
- * **sketches-core** moved to [incubator-datasketches-java](https://github.com/apache/incubator-datasketches-java) This is the **core** library that contains all sketch algorithms written in Java.
- * **memory** moved to [incubator-datasketches-memory](https://github.com/apache/incubator-datasketches-memory) Low-level component used by other java components.
+ * **sketches-core** moved to [datasketches-java](https://github.com/apache/datasketches-java) This is the **core** library that contains all sketch algorithms written in Java.
+ * **memory** moved to [datasketches-memory](https://github.com/apache/datasketches-memory) Low-level component used by other java components.
* Java Adaptors
- * **sketches-hive** moved to [incubator-datasketches-hive](https://github.com/apache/incubator-datasketches-hive) Adapts the Java core to Apache Hive.
- * **sketches-pig** moved to [incubator-datasketches-pig](https://github.com/apache/incubator-datasketches-pig) Adapts the Java core to Apache Pig.
- * **sketches-vector** moved to [incubator-datasketches-vector](https://github.com/apache/incubator-datasketches-vector) Experimental sketches for vector and matrix processing.
+ * **sketches-hive** moved to [datasketches-hive](https://github.com/apache/datasketches-hive) Adapts the Java core to Apache Hive.
+ * **sketches-pig** moved to [datasketches-pig](https://github.com/apache/datasketches-pig) Adapts the Java core to Apache Pig.
+ * **sketches-vector** moved to [datasketches-vector](https://github.com/apache/datasketches-vector) Experimental sketches for vector and matrix processing.
* [Apache Druid adaptors](https://datasketches.apache.org/docs/SystemIntegrations/ApacheDruidIntegration.html)
-* C++ / [Python](https://github.com/apache/incubator-datasketches-cpp/tree/master/python) Core
- * **sketches-core-cpp** moved to [incubator-datasketches-cpp](https://github.com/apache/incubator-datasketches-cpp) This is the **core** library that contains all major sketch algorithms written in C++ and Python.
+* C++ / [Python](https://github.com/apache/datasketches-cpp/tree/master/python) Core
+ * **sketches-core-cpp** moved to [datasketches-cpp](https://github.com/apache/datasketches-cpp) This is the **core** library that contains all major sketch algorithms written in C++ and Python.
* C++ Adaptors
- * **sketches-postgres** moved to [incubator-datasketches-postgresql](https://github.com/apache/incubator-datasketches-postgresql) Adapts the C++ core to PostgreSQL.
+ * **sketches-postgres** moved to [datasketches-postgresql](https://github.com/apache/datasketches-postgresql) Adapts the C++ core to PostgreSQL.
* Java and C++
- * **characterization** moved to [incubator-datasketches-characterization](https://github.com/apache/incubator-datasketches-characterization) Java and C++ Characterization suites for benchmarking and exhaustive testing.
+ * **characterization** moved to [datasketches-characterization](https://github.com/apache/datasketches-characterization) Java and C++ Characterization suites for benchmarking and exhaustive testing.
* Obsolete Components
* [sketches-android](https://github.com/DataSketches/sketches-android) An experimental proof-of-concept sketch for the Android cell-phone.
@@ -62,7 +62,7 @@
* [characterization-cpp](https://github.com/DataSketches/characterization-cpp)
* [experimental-cpp](https://github.com/DataSketches/experimental-cpp)
-* Command Line Functions (Mostly obsolete. These may move to Apache later or replaced by [Python](https://github.com/apache/incubator-datasketches-cpp/tree/master/python))
+* Command Line Functions (Mostly obsolete. These may move to Apache later or replaced by [Python](https://github.com/apache/datasketches-cpp/tree/master/python))
* [homebrew-sketches-cmd](https://github.com/DataSketches/homebrew-sketches-cmd)
* [sketches-cmd](https://github.com/DataSketches/sketches-cmd)
* [homebrew-sketches](https://github.com/DataSketches/homebrew-sketches)
diff --git a/docs/Community/index.md b/docs/Community/index.md
index 8f68a9f..fe98759 100644
--- a/docs/Community/index.md
+++ b/docs/Community/index.md
@@ -33,7 +33,7 @@
There are many ways that are available for our community to communicate with each other and directly with our developers. Please review the following for methods that meet your needs.
### [Users Mailing List]({{page.users}})
-This is a great place for all users (new and experienced) to ask general questions about the library, its general capabilities, and where to get help and find more information. This is also a great place to give the developers general feedback about the library. If you like what you see, please give us a [Star (Java)](https://github.com/apache/incubator-datasketches-java) and/or [Star (C++/Python)](https://github.com/apache/incubator-datasketches-cpp) If you have general suggestions on how we can improve we would like to hear from you. All of our developers follow this list and all sincere questions and comments will get a response.
+This is a great place for all users (new and experienced) to ask general questions about the library, its general capabilities, and where to get help and find more information. This is also a great place to give the developers general feedback about the library. If you like what you see, please give us a [Star (Java)](https://github.com/apache/datasketches-java) and/or [Star (C++/Python)](https://github.com/apache/datasketches-cpp) If you have general suggestions on how we can improve we would like to hear from you. All of our developers follow this list and all sincere questions and comments will get a response.
* To subscribe send an empty email to [users-subscribe@datasketches.apache.org](mailto:users-subscribe@datasketches.apache.org).
* To unsubscribe send an empty email to [users-unsubscribe@datasketches.apache.org](mailto:users-unsubscribe@datasketches.apache.org).
@@ -63,7 +63,7 @@
### What to work on
* **[GitHub Components]({{site.docs_dir}}/Architecture/Components.html):** Our library is made up of components that are partitioned into GitHub repositories. If you have a specific issue or bug report that impacts only one of these components please open an issue on the respective component. If you are a developer and wish to submit a PR, please choose the appropriate repository.
-* We have three TODO lists for [Java](https://github.com/apache/incubator-datasketches-java/projects/1), [C++](https://github.com/apache/incubator-datasketches-cpp/projects/1) and the [Website](https://github.com/apache/incubator-datasketches-website/projects/1). Please check these out for potential contribution!
+* We have three TODO lists for [Java](https://github.com/apache/datasketches-java/projects/1), [C++](https://github.com/apache/datasketches-cpp/projects/1) and the [Website](https://github.com/apache/datasketches-website/projects/1). Please check these out for potential contribution!
### Getting your proposed changes accepted
diff --git a/docs/DistinctCountFeaturesMatrix.md b/docs/DistinctCountFeaturesMatrix.md
index a4d54b7..8be5429 100644
--- a/docs/DistinctCountFeaturesMatrix.md
+++ b/docs/DistinctCountFeaturesMatrix.md
@@ -71,6 +71,6 @@
____
<sup>1</sup> Space Accuracy Merit (Lower is better) = b * C^2 = b * K * RSE^2<br>
- <sup>2</sup> See [Python Install Instructions](https://github.com/apache/incubator-datasketches-cpp/tree/master/python)<br>
+ <sup>2</sup> See [Python Install Instructions](https://github.com/apache/datasketches-cpp/tree/master/python)<br>
\ No newline at end of file
diff --git a/docs/DistinctCountMeritComparisons.md b/docs/DistinctCountMeritComparisons.md
index f9c53d9..a4ba451 100644
--- a/docs/DistinctCountMeritComparisons.md
+++ b/docs/DistinctCountMeritComparisons.md
@@ -98,4 +98,4 @@
Depending on what is important in your environment you should choose the sketch that best suits your needs.
-Note that all of these tests were performed with the Java implementations of these sketches. The results for C++ or [Python](https://github.com/apache/incubator-datasketches-cpp/tree/master/python) may be completely different.
\ No newline at end of file
+Note that all of these tests were performed with the Java implementations of these sketches. The results for C++ or [Python](https://github.com/apache/datasketches-cpp/tree/master/python) may be completely different.
\ No newline at end of file
diff --git a/docs/Frequency/FrequentItemsPerformance.md b/docs/Frequency/FrequentItemsPerformance.md
index dcbfe60..66dd40a 100644
--- a/docs/Frequency/FrequentItemsPerformance.md
+++ b/docs/Frequency/FrequentItemsPerformance.md
@@ -21,7 +21,7 @@
-->
# Performance of Frequent Items sketch
-The code used to produce the data for these plots can be found in the [characterization repository](https://github.com/apache/incubator-datasketches-characterization)
+The code used to produce the data for these plots can be found in the [characterization repository](https://github.com/apache/datasketches-characterization)
<img class="doc-img-full" src="{{site.docs_img_dir}}/fi/fi_update_time.png"/>
diff --git a/docs/Governance/ChairResponsibilities.md b/docs/Governance/ChairResponsibilities.md
index ae53353..f6a9e26 100644
--- a/docs/Governance/ChairResponsibilities.md
+++ b/docs/Governance/ChairResponsibilities.md
@@ -29,7 +29,7 @@
* [PMC Chair Duties](http://www.apache.org/dev/pmc.html#chair)
* [PMC Duties](http://www.apache.org/dev/#pmc)
* [PMC Chair Role](https://cwiki.apache.org/confluence/display/jakarta/RoleOfChair)
-* [PPMC Chair Guide](http://incubator.apache.org/guides/chair.html)
+* [Incubator PPMC Chair Guide](http://incubator.apache.org/guides/chair.html)
*
@@ -67,7 +67,7 @@
### Where
The content should be committed to the meeting agenda in the board directory
in the foundation repository, trying to keep a similar format to the others.
-This can be found at:
+Exmaples can be found at the following link. See past "board_agenda_YYYY_MM_DD.txt".
* <https://svn.apache.org/repos/private/foundation/board>
@@ -75,9 +75,8 @@
## Record Maintenance
* Reference: **Committee-info**
-* LDAP: [Roster](https://whimsy.apache.org/roster/committee/datasketches)
-* <https://whimsy.apache.org/roster/committee/datasketches>
-* <https://incubator.apache.org/projects/datasketches.html>
+* LDAP: [DataSketches Roster](https://whimsy.apache.org/roster/committee/datasketches)
+* [DataSketches Incubation Status](https://incubator.apache.org/projects/datasketches.html)
* [Apache Committee Report Helper](https://reporter.apache.org/?datasketches)
diff --git a/docs/HLL/HllPerformance.md b/docs/HLL/HllPerformance.md
index b71b7d1..a26632f 100644
--- a/docs/HLL/HllPerformance.md
+++ b/docs/HLL/HllPerformance.md
@@ -21,7 +21,7 @@
-->
# Performance of HLL sketch
-The code used to produce the data for these plots can be found in the [characterization repository](https://github.com/apache/incubator-datasketches-characterization)
+The code used to produce the data for these plots can be found in the [characterization repository](https://github.com/apache/datasketches-characterization)
<img class="doc-img-full" src="{{site.docs_img_dir}}/hll/HLL_sketch_accuracy_lgk12.png"/>
diff --git a/docs/HLL/HllSketchVsDruidHyperLogLogCollector.md b/docs/HLL/HllSketchVsDruidHyperLogLogCollector.md
index 721c9cb..aaf9c64 100644
--- a/docs/HLL/HllSketchVsDruidHyperLogLogCollector.md
+++ b/docs/HLL/HllSketchVsDruidHyperLogLogCollector.md
@@ -25,7 +25,7 @@
## Versions
-* HLL sketch form <a href="https://github.com/apache/incubator-datasketches-java">Java Version 0.11.1, April 20, 2018</a>
+* HLL sketch form <a href="https://github.com/apache/datasketches-java">Java Version 0.11.1, April 20, 2018</a>
* Druid HyperLogLogCollector from <a href="https://github.com/apache/druid">Apache Druid Version 0.12.0, March 8, 2018</a>
## Size
@@ -85,7 +85,7 @@
## Source code
-The code to reproduce these measurements is available in the <a href="https://github.com/apache/incubator-datasketches-characterization">Datasketches/characterization repository</a>
+The code to reproduce these measurements is available in the <a href="https://github.com/apache/datasketches-characterization">Datasketches/characterization repository</a>
## DataSketches HLL Sketch Druid module
diff --git a/docs/HLL/Hll_vs_CS_Hllpp.md b/docs/HLL/Hll_vs_CS_Hllpp.md
index 110a335..9b8d369 100644
--- a/docs/HLL/Hll_vs_CS_Hllpp.md
+++ b/docs/HLL/Hll_vs_CS_Hllpp.md
@@ -38,7 +38,7 @@
The colored curves represent different quantile contours of the empirical error distribution. The orange and green curves are the contours corresponding to +/- one standard deviation from the mean error, and which define the 68% confidence bounds. The red and blue curves are the contours at +/- two standard deviations and define the 95.4% confidence bounds. The brown and purple curves are the contours at +/- three standard deviations and define the 99.7% confidence bounds. The mean (gray) and median (black) overlap each other and hug the axis close to zero.
### Introduction to Detailed Accuracy Measurements
-Measuring the error properties of these stochastic algorithms is tricky and requires a great deal of thought into the design of the program that measures it. Getting smooth-looking plots requires many tens of thousands of trials, which even with fast CPUs requires a lot of time. The code used to produce the data for the plots in this paper can be found on the [characterization repository](https://github.com/apache/incubator-datasketches-characterization)
+Measuring the error properties of these stochastic algorithms is tricky and requires a great deal of thought into the design of the program that measures it. Getting smooth-looking plots requires many tens of thousands of trials, which even with fast CPUs requires a lot of time. The code used to produce the data for the plots in this paper can be found on the [characterization repository](https://github.com/apache/datasketches-characterization)
For accuracy purposes, the *HllSketch* sketch is configured with one parameter, *Log_2(K)* which we abbreviate as *LgK*. This defines the number of bins of the final HyperLogLog-Array (HLL-Array)\[6\] mode, and defines the bounds[7] on the accuracy of the sketch as well as its ultimate size. Thus, specifying a *LgK = 12*, means that the final HyperLogLog mode of the sketch will have *k = 2<sup>12</sup> = 4096* bins. A sketch with *LgK = 21* will ultimately have *k =2,097,152* bins, which is a very large sketch.
@@ -225,7 +225,7 @@
****
-* [1] [DataSketches HllSketch GitHub](https://github.com/apache/incubator-datasketches-java/tree/master/src/main/java/org/apache/datasketches/hll)
+* [1] [DataSketches HllSketch GitHub](https://github.com/apache/datasketches-java/tree/master/src/main/java/org/apache/datasketches/hll)
* [2] [DataSketches HllSketch JavaDocs](/api/java/snapshot/apidocs/index.html)
* [3] [HyperLogLogPlus GitHub](https://github.com/addthis/stream-lib/blob/master/src/main/java/com/clearspring/analytics/stream/cardinality/HyperLogLogPlus.java)
* [4] [Google: HyperLogLog in Practice: Algorithmic Engineering of a State of The Art Cardinality Estimation Algorithm](https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/40671.pdf)
diff --git a/docs/Quantiles/DruidApproxHistogramStudy.md b/docs/Quantiles/DruidApproxHistogramStudy.md
index e0b95f9..bb8f44d 100644
--- a/docs/Quantiles/DruidApproxHistogramStudy.md
+++ b/docs/Quantiles/DruidApproxHistogramStudy.md
@@ -29,7 +29,7 @@
## Versions
-* Druid <a href="https://github.com/apache/incubator-druid/blob/master/extensions-core/histogram/src/main/java/org/apache/druid/query/aggregation/histogram/ApproximateHistogram.java">ApproximateHistogram.java Nov 6, 2018</a>
+* Druid <a href="https://github.com/apache/druid/blob/master/extensions-core/histogram/src/main/java/org/apache/druid/query/aggregation/histogram/ApproximateHistogram.java">ApproximateHistogram.java Nov 6, 2018</a>
## The Paper
diff --git a/docs/Quantiles/KllSketchVsTDigest.md b/docs/Quantiles/KllSketchVsTDigest.md
index 54beda8..d3c432c 100644
--- a/docs/Quantiles/KllSketchVsTDigest.md
+++ b/docs/Quantiles/KllSketchVsTDigest.md
@@ -119,4 +119,4 @@
## Source code
-The code to reproduce these measurements is available in the <a href="https://github.com/apache/incubator-datasketches-characterization">Datasketches/characterization repository</a>
+The code to reproduce these measurements is available in the <a href="https://github.com/apache/datasketches-characterization">Datasketches/characterization repository</a>
diff --git a/docs/REQ/ReqAccuracyAdversarial.md b/docs/REQ/ReqAccuracyAdversarial.md
index 193fca7..ddfb27e 100644
--- a/docs/REQ/ReqAccuracyAdversarial.md
+++ b/docs/REQ/ReqAccuracyAdversarial.md
@@ -27,8 +27,8 @@
For those that are interested in the actual code that run these tests can examine the following links.
-* [Code](https://github.com/apache/incubator-datasketches-characterization/blob/master/src/test/java/org/apache/datasketches/characterization/quantiles/ReqSketchAccuracyProfile.java): The code used to generate these characterization studies.
-* [Config](https://github.com/apache/incubator-datasketches-characterization/blob/master/src/main/resources/quantiles/ReqSketchAccuracyJob.conf): The human readable and editable configuration file that instructs the above code with the specific properties used to run the test. These configuration properties are different for each of the following plots and summarized below with each plot.
+* [Code](https://github.com/apache/datasketches-characterization/blob/master/src/test/java/org/apache/datasketches/characterization/quantiles/ReqSketchAccuracyProfile.java): The code used to generate these characterization studies.
+* [Config](https://github.com/apache/datasketches-characterization/blob/master/src/main/resources/quantiles/ReqSketchAccuracyJob.conf): The human readable and editable configuration file that instructs the above code with the specific properties used to run the test. These configuration properties are different for each of the following plots and summarized below with each plot.
## Test Design
* Stream Length (SL): 2^20 = 1,048,576 floats
diff --git a/docs/REQ/ReqAccuracyRandomShuffled.md b/docs/REQ/ReqAccuracyRandomShuffled.md
index ee18762..67e508e 100644
--- a/docs/REQ/ReqAccuracyRandomShuffled.md
+++ b/docs/REQ/ReqAccuracyRandomShuffled.md
@@ -26,8 +26,8 @@
For those that are interested in the actual code that run these tests can examine the following links.
-* [Code](https://github.com/apache/incubator-datasketches-characterization/blob/master/src/test/java/org/apache/datasketches/characterization/quantiles/ReqSketchAccuracyProfile.java): The code used to generate these characterization studies.
-* [Config](https://github.com/apache/incubator-datasketches-characterization/blob/master/src/main/resources/quantiles/ReqSketchAccuracyJob.conf): The human readable and editable configuration file that instructs the above code with the specific properties used to run the test. These configuration properties are different for each of the following plots and summarized below with each plot.
+* [Code](https://github.com/apache/datasketches-characterization/blob/master/src/test/java/org/apache/datasketches/characterization/quantiles/ReqSketchAccuracyProfile.java): The code used to generate these characterization studies.
+* [Config](https://github.com/apache/datasketches-characterization/blob/master/src/main/resources/quantiles/ReqSketchAccuracyJob.conf): The human readable and editable configuration file that instructs the above code with the specific properties used to run the test. These configuration properties are different for each of the following plots and summarized below with each plot.
## Test Design
diff --git a/docs/Sampling/ReservoirSamplingPerformance.md b/docs/Sampling/ReservoirSamplingPerformance.md
index 7b3b1a6..466ef6e 100644
--- a/docs/Sampling/ReservoirSamplingPerformance.md
+++ b/docs/Sampling/ReservoirSamplingPerformance.md
@@ -56,7 +56,7 @@
### Comparison with Apache DataFu
-[DataFu](https://datafu.apache.org) is an Apache Incubator project that includes a set of Pig UDFs for various data processing purposes.
+[DataFu](https://fdatafu.apache.org) is an Apache project that includes a set of Pig UDFs for various data processing purposes.
Because the focus of the Sketches library is on sublinear or bounded-size algorithms, compared the performance of our reservoir
sampling Pig UDF against DataFu's basic reservoir sampling, which uses a pre-specified maximum size.
diff --git a/docs/SystemIntegrations/ApacheHiveIntegration.md b/docs/SystemIntegrations/ApacheHiveIntegration.md
index 7cee5b0..9bd2807 100644
--- a/docs/SystemIntegrations/ApacheHiveIntegration.md
+++ b/docs/SystemIntegrations/ApacheHiveIntegration.md
@@ -23,7 +23,7 @@
* [Download](https://datasketches.apache.org/docs/Community/Downloads.html)
-* Build and Install, See [Hive README](https://github.com/apache/incubator-datasketches-hive)
+* Build and Install, See [Hive README](https://github.com/apache/datasketches-hive)
* [CPC Example](https://datasketches.apache.org/docs/CPC/CpcHiveExample.html)
diff --git a/docs/SystemIntegrations/ApachePigIntegration.md b/docs/SystemIntegrations/ApachePigIntegration.md
index 38cd1eb..94549fa 100644
--- a/docs/SystemIntegrations/ApachePigIntegration.md
+++ b/docs/SystemIntegrations/ApachePigIntegration.md
@@ -23,7 +23,7 @@
* [Download](https://datasketches.apache.org/docs/Community/Downloads.html)
-* Build and Install, See [Pig README](https://github.com/apache/incubator-datasketches-pig)
+* Build and Install, See [Pig README](https://github.com/apache/datasketches-pig)
* [CPC Example](https://datasketches.apache.org/docs/CPC/CpcPigExample.html)
diff --git a/docs/SystemIntegrations/PostgreSQLIntegration.md b/docs/SystemIntegrations/PostgreSQLIntegration.md
index b8a716f..9a82f34 100644
--- a/docs/SystemIntegrations/PostgreSQLIntegration.md
+++ b/docs/SystemIntegrations/PostgreSQLIntegration.md
@@ -23,5 +23,5 @@
* [Download](https://datasketches.apache.org/docs/Community/Downloads.html)
-* Install, See [PostgreSQL README](https://github.com/apache/incubator-datasketches-postgresql)
+* Install, See [PostgreSQL README](https://github.com/apache/datasketches-postgresql)
diff --git a/docs/Theta/ThetaUpdateSpeed.md b/docs/Theta/ThetaUpdateSpeed.md
index aa2d917..fe5156d 100644
--- a/docs/Theta/ThetaUpdateSpeed.md
+++ b/docs/Theta/ThetaUpdateSpeed.md
@@ -63,7 +63,7 @@
Very few systems would actually be able to feed a single sketch at this rate so these plots represent an upper bound of performance, and not as realistic update rates in more complex systems environments. Nonetheless, this demonstrates that the sketches would consume very little of an overall system's budget for updating, if there was one, and are quite suitable for real-time streams.
The graphs on this page were generated using the utilities in the
-[Characterization Repository](https://github.com/apache/incubator-datasketches-characterization).
+[Characterization Repository](https://github.com/apache/datasketches-characterization).
There is some more documentation with the code on using these tools if you wish to re-run these characterization tests yourself.
diff --git a/docs/Tuple/TupleEngagementExample.md b/docs/Tuple/TupleEngagementExample.md
index 8e56f5d..1a49637 100644
--- a/docs/Tuple/TupleEngagementExample.md
+++ b/docs/Tuple/TupleEngagementExample.md
@@ -63,7 +63,7 @@
## The IntegerSketch and Helper classes
-To help us code our example we will leverage the [IntegerSketch Package](https://github.com/apache/incubator-datasketches-java/tree/master/src/main/java/org/apache/datasketches/tuple/aninteger) from the library. This package consists of 5 classes, the _IntegerSketch_ and 4 helper classes, all of which extend generic classes of the parent _tuple_ package. Normally, the user/developer would develop these 5 classes to solve a particular analysis problem. These 5 classes can serve as an example of how to create your own Tuple Sketch solutions and we will use them to solve our customer engagement problem.
+To help us code our example we will leverage the [IntegerSketch Package](https://github.com/apache/datasketches-java/tree/master/src/main/java/org/apache/datasketches/tuple/aninteger) from the library. This package consists of 5 classes, the _IntegerSketch_ and 4 helper classes, all of which extend generic classes of the parent _tuple_ package. Normally, the user/developer would develop these 5 classes to solve a particular analysis problem. These 5 classes can serve as an example of how to create your own Tuple Sketch solutions and we will use them to solve our customer engagement problem.
Please refer to the [Tuple Overview](/docs/Tuple/TupleOverview.html) section on this website for a quick review of how the Tuple Sketch works.
@@ -177,7 +177,7 @@
### IntegerSummaryDeserializer class
This class is only called by the underlying sketch code when deserializing a sketch and its summaries from a stored image. We will not be using this class in our example.
-## The [EngagementTest](https://github.com/apache/incubator-datasketches-java/blob/master/src/test/java/org/apache/datasketches/tuple/aninteger/EngagementTest.java) class
+## The [EngagementTest](https://github.com/apache/datasketches-java/blob/master/src/test/java/org/apache/datasketches/tuple/aninteger/EngagementTest.java) class
Note 1: the version in the GitHub master is more up-to-date than the version of this class in the 1.1.0-incubating release. This tutorial references the code in master.
Note 2: You can run the following _computeEngagementHistogram()_ method as a test, but in order to see the output you will need to un-comment the printf(...) statement at the very end of the class.
diff --git a/pom.xml b/pom.xml
index 1ad52d5..0a60653 100644
--- a/pom.xml
+++ b/pom.xml
@@ -30,7 +30,7 @@
<!-- UNIQUE FOR THIS JAVA COMPONENT -->
<artifactId>datasketches-website</artifactId>
- <version>1.0.0-incubating-SNAPSHOT</version>
+ <version>1.0.0-SNAPSHOT</version>
<description>Website source for the DataSketches library.</description>
<!-- END: UNIQUE FOR THIS JAVA COMPONENT -->
@@ -55,16 +55,16 @@
</license>
</licenses>
<scm>
- <connection>scm:git:ssh://git@github.com/apache/incubator-${project.artifactId}.git</connection>
- <developerConnection>scm:git:ssh://git@github.com/apache/incubator-${project.artifactId}.git</developerConnection>
- <url>https://github.com/apache/incubator-${project.artifactId}</url>
+ <connection>scm:git:ssh://git@github.com/apache/${project.artifactId}.git</connection>
+ <developerConnection>scm:git:ssh://git@github.com/apache/${project.artifactId}.git</developerConnection>
+ <url>https://github.com/apache/${project.artifactId}</url>
<tag>HEAD</tag>
</scm>
<issueManagement>
<!-- <system>jira</system>
<url>https://issues.apache.org/jira/browse/DATASKETCHES</url> -->
<system>GitHub</system>
- <url>https://github.com/apache/incubator-${project.artifactId}/issues</url>
+ <url>https://github.com/apache/${project.artifactId}/issues</url>
</issueManagement>
<mailingLists>
<mailingList>
@@ -473,10 +473,10 @@
</build>
</profile>
<!-- This profile is used to release signed jars to the Apache Nexus repository.
- This must be executed from a git repository set at the proper Release branch (e.g., 1.1.X-incubating)
- and at a Release Candidate tag (e.g., 1.1.0-incubating-RC1).
- The pom version in the release branch must be properly set to something like: "1.1.0-incubating".
- The pom version in the master would be set to something like: "1.2.0-incubating-SNAPSHOT".
+ This must be executed from a git repository set at the proper Release branch (e.g., 1.1.X)
+ and at a Release Candidate tag (e.g., 1.1.0-RC1).
+ The pom version in the release branch must be properly set to something like: "1.1.0".
+ The pom version in the master would be set to something like: "1.2.0SNAPSHOT".
Test Command: mvn clean verify -Pnexus-jars -DskipTests=true
Command: mvn clean deploy -Dnexus-jars
Verify Command (from terminal): gpg -v --verify $ASC $FILE # dashdashverify
