DBScholar

Back to papers

Set Similarity Join on Probabilistic Data

Summary: Models probabilistic set data at set- and element-level uncertainty and defines probabilistic set similarity join (PS2J) under possible worlds semantics. Introduces world condensation and pruning techniques—Jaccard distance, probability upper-bound, and aggregate pruning—with indexing and synopses, validated by extensive experiments. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10304
Venue
VLDB
Year
2010
Pagerank
5.5340666e-05
Overall Rank
7,839 | 46.22%
DOI
10.14778/1920841.1920924

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{lian_vldb10,
        title = {{Set Similarity Join on Probabilistic Data}},
        author = {Lian, Xiang and Chen, Lei},
        journal = {PVLDB},
        series = {{VLDB} '10},
        volume = {3},
        number = {1},
        pages = {650--661},
        doi = {10.14778/1920841.1920924},
        url = {https://doi.org/10.14778/1920841.1920924},
        year = {2010}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
2,186 String Similarity Joins: An Experimental Evaluation 2014 VLDB 9.0001436e-05
12,103 Indexing Metric Uncertain Data for Range Queries 2015 SIGMOD 5.093636e-05
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 11 of 11 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers