| /* |
| * Description : Fuzzy joins two datasets, DBLP and CSX, based on the similarity-jaccard function of their titles' word tokens. |
| * CSX has a keyword index on title, and we expect the join to be transformed into an indexed nested-loop join. |
| * Success : Yes |
| */ |
| |
| drop dataverse test if exists; |
| create dataverse test; |
| use dataverse test; |
| |
| create type DBLPType as closed { |
| id: int32, |
| dblpid: string, |
| title: string, |
| authors: string, |
| misc: string |
| } |
| |
| create type CSXType as closed { |
| id: int32, |
| csxid: string, |
| title: string, |
| authors: string, |
| misc: string |
| } |
| |
| create dataset DBLP(DBLPType) partitioned by key id; |
| |
| create dataset CSX(CSXType) partitioned by key id; |
| |
| create index keyword_index on CSX(title) type keyword; |
| |
| write output to nc1:"rttest/inverted-index-join_word-jaccard_02.adm"; |
| |
| for $a in dataset('DBLP') |
| for $b in dataset('CSX') |
| where similarity-jaccard(word-tokens($a.title), word-tokens($b.title)) >= 0.5f |
| and $a.id < $b.id |
| return {"arec": $a, "brec": $b } |
| |