New Wine in an Old Bottle: Data-Aware Hash Functions for Bloom Filters
Summary: A partitioned Bloom filter uses data-derived projection hashes, avoiding learned models while exploiting key patterns. A principled analysis selects its parameters, yielding up to 100× lower FPR or 50% better compression than standard and learned variants. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Arindam Bhattacharya (Indian Institute of Technology Delhi)
- 2. Chathur Gudesa (Indian Institute of Technology Delhi)
- 3. Amitabha Bagchi (Indian Institute of Technology Delhi)
- 4. Srikanta Bedathur (Indian Institute of Technology Delhi)
BibTeX Citation
@article{bhattacharya_vldb22,
title = {{New Wine in an Old Bottle: Data-Aware Hash Functions for Bloom Filters}},
author = {Bhattacharya, Arindam and Gudesa, Chathur and Bagchi, Amitabha and Bedathur, Srikanta},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {9},
pages = {1924--1936},
doi = {10.14778/3538598.3538613},
url = {https://doi.org/10.14778/3538598.3538613},
year = {2022}
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 43 | The Case for Learned Index Structures | 2018 | SIGMOD | 0.00046060254 |
| 4,073 | Stable Learned Bloom Filters for Data Streams | 2020 | VLDB | 6.9242783e-05 |
| 4,691 | Fast Processing and Querying of 170TB of Genomics Data via a Repeated And Merged BloOm Filter (RAMBO) | 2021 | SIGMOD | 6.5609196e-05 |
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