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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)

Paper ID
12119
Venue
VLDB
Year
2019
Pagerank
5.4900832e-05
Overall Rank
8,088 | 44.51%
DOI
10.14778/3352063.3352128

Incoming Non-self Citations Over Time

Authors

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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