Power-Law Based Estimation of Set Similarity Join Size
Summary: Power-law guided estimation of SSJoin size via compact Min-Hash signatures; exploits frequent signature patterns to count support. A novel lattice-based IE counting method yields linear complexity in lattice size, enabling light-weight mining with high accuracy and efficiency. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Hongrae Lee (University of British Columbia)
- 2. Raymond T. Ng (University of British Columbia)
- 3. Kyuseok Shim (Seoul National University)
BibTeX Citation
@article{lee_vldb09,
title = {{Power-Law Based Estimation of Set Similarity Join Size}},
author = {Lee, Hongrae and Ng, Raymond T. and Shim, Kyuseok},
journal = {PVLDB},
series = {{VLDB} '09},
doi = {10.14778/1687627.1687702},
url = {https://doi.org/10.14778/1687627.1687702},
year = {2009}
}
Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 975 | Can We Beat the Prefix Filtering? An Adaptive Framework for Similarity Join and Search | 2012 | SIGMOD | 0.00012870645 |
| 1,886 | Pass-Join: A Partition-based Method for Similarity Joins | 2012 | VLDB | 9.5358137e-05 |
| 2,186 | String Similarity Joins: An Experimental Evaluation | 2014 | VLDB | 9.0001436e-05 |
| 4,617 | Learned Cardinality Estimation for Similarity Queries | 2021 | SIGMOD | 6.604437e-05 |
| 4,857 | Similarity Join Size Estimation using Locality Sensitive Hashing | 2011 | VLDB | 6.4752373e-05 |
| 5,088 | String Similarity Measures and Joins with Synonyms | 2013 | SIGMOD | 6.3673276e-05 |
| 5,412 | Faerie: Efficient Filtering Algorithms for Approximate Dictionary-based Entity Extraction | 2011 | SIGMOD | 6.2272563e-05 |
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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.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 27 | Fast Algorithms for Mining Association Rules | 1994 | VLDB | 0.00052255472 |
| 58 | The Merge/Purge Problem for Large Databases | 1995 | SIGMOD | 0.00040116748 |
| 169 | Efficient Exact Set-Similarity Joins | 2006 | VLDB | 0.0002743469 |
| 200 | Efficient set joins on similarity predicates | 2004 | SIGMOD | 0.00025597287 |
| 432 | Mining Database Structure; Or, How to Build a Data Quality Browser | 2002 | SIGMOD | 0.00018572055 |
| 586 | ConQuer: Efficient Management of Inconsistent Databases | 2005 | SIGMOD | 0.00016124396 |
| 974 | To Search or to Crawl? Towards a Query Optimizer for Text-Centric Tasks | 2006 | SIGMOD | 0.00012870746 |
| 2,061 | Selectivity Estimation For Boolean Queries | 2000 | PODS | 9.2445049e-05 |
| 2,308 | Hashed Samples: Selectivity Estimators For Set Similarity Selection Queries | 2008 | VLDB | 8.7738996e-05 |
| 2,899 | Extending Q-Grams to Estimate Selectivity of String Matching with Low Edit Distance | 2007 | VLDB | 7.97814e-05 |
| 5,932 | Spatial Join Selectivity Using Power Laws | 2000 | SIGMOD | 6.0369275e-05 |
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| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 4,260 | Set Similarity Joins on MapReduce: An Experimental Survey | 2018 | VLDB |
| 2 | 11,447 | A Two-Level Signature Scheme for Stable Set Similarity Joins | 2023 | VLDB |
| 3 | 2,501 | An Empirical Evaluation of Set Similarity Join Techniques | 2016 | VLDB |
| 4 | 200 | Efficient set joins on similarity predicates | 2004 | SIGMOD |
| 5 | 169 | Efficient Exact Set-Similarity Joins | 2006 | VLDB |
| 6 | 3,040 | Leveraging Set Relations in Exact Set Similarity Join | 2017 | VLDB |
| 7 | 3,474 | An Efficient Partition Based Method for Exact Set Similarity Joins | 2016 | VLDB |
| 8 | 3,795 | Is Min-Wise Hashing Optimal for Summarizing Set Intersection? | 2014 | PODS |
| 9 | 3,724 | Overlap Set Similarity Joins with Theoretical Guarantees | 2018 | SIGMOD |
| 10 | 4,857 | Similarity Join Size Estimation using Locality Sensitive Hashing | 2011 | VLDB |