Customizable and Scalable Fuzzy Join for Big Data
Summary: Customizable, scalable fuzzy join for big data using LSH-based signatures to handle domain-quality issues such as synonyms and abbreviations. On Azure Databricks Spark, it delivers >50x speedup over prior scale-out methods with near-linear scalability in data size and cluster size. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zhimin Chen (Microsoft)
- 2. Yue Wang (Microsoft)
- 3. Vivek Narasayya (Microsoft)
- 4. Surajit Chaudhuri (Microsoft)
BibTeX Citation
@article{chen_vldb19,
title = {{Customizable and Scalable Fuzzy Join for Big Data}},
author = {Chen, Zhimin and Wang, Yue and Narasayya, Vivek and Chaudhuri, Surajit},
journal = {PVLDB},
series = {{VLDB} '19},
volume = {12},
number = {12},
pages = {2106--2117},
doi = {10.14778/3352063.3352128},
url = {https://doi.org/10.14778/3352063.3352128},
year = {2019}
}
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,589 | Ember: No-Code Context Enrichment via Similarity-Based Keyless Joins | 2022 | VLDB | 7.2812353e-05 |
| 7,311 | Lachesis: Automatic Partitioning for UDF-Centric Analytics | 2021 | VLDB | 5.6491618e-05 |
| 10,987 | OmniMatch: Joinability Discovery in Data Products | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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