- AuthorsMatch exploits the new matching strategy used for ORCID enhancements in #PR398: split author names in tokens, order the tokens, then check for matches of ordered full tokens or abbreviations
- Create dedup blocks from the complete queue of records matching cluster key instead of truncating the results
- Clean titles once before clustering and similarity comparisons
- Added support for filtered fields in model
- Added support for sorting List fields in model
- Added new JSONListClustering and numAuthorsTitleSuffixPrefixChain clustering functions
- Added new maxLengthMatch comparator function
- Use reduced complexity Levenshtein with threshold in levensteinTitle
- Use reduced complexity AuthorsMatch with threshold early-quit
- Use incremental Connected Component to decrease comparisons in similarity match in BlockProcessor
- Use new clusterings configuration in Dedup tests
SparkWhitelistSimRels: use left semi join for clarity and performance
SparkCreateMergeRels:
- Use new connected component algorithm that converge faster than Spark GraphX provided algorithm
- Refactored to use Windowing sorting rather than groupBy to reduce memory pressure
- Use historical pivot table to generate singleton rels, merged rels and keep continuity with dedupIds used in the past
- Comparator for pivot record selection now uses "tomorrow" as filler for missing or incorrect date instead of "2000-01-01"
- Changed generation of ids of type dedup_wf_001 to avoid collisions
DedupRecordFactory: use reduceGroups instead of mapGroups to decrease memory pressure
JsonPath cache contention fixed by using a ConcurrentHashMap
Blacklist filtering performance improvement
Minor performance improvements when evaluating similarity
Sorting in clustered elements is deterministic (by ordering and identity field, instead of ordering field only)
Giambattista Bloisi
Giambattista Bloisi
Sandro La Bruzzo
Claudio Atzori