A Partitioning Framework for Aggressive Data Skipping
Summary: Fine-grained, load-time partitioning enables aggressive block skipping. Four-step pipeline—workload analysis, per-tuple feature augmentation, reduction to feature vectors, and clustering-driven partitioning—yields up to 37x faster queries than traditional range partitioning. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Liwen Sun (University of California Berkeley)
- 2. Sanjay Krishnan (University of California Berkeley)
- 3. Reynold S. Xin (University of California Berkeley)
- 4. Michael J. Franklin (University of California Berkeley)
BibTeX Citation
@article{sun_vldb14,
title = {{A Partitioning Framework for Aggressive Data Skipping}},
author = {Sun, Liwen and Krishnan, Sanjay and Xin, Reynold S. and Franklin, Michael J.},
journal = {PVLDB},
series = {{VLDB} '14},
volume = {7},
number = {13},
pages = {1617--1620},
doi = {10.14778/2733004.2733028},
url = {https://doi.org/10.14778/2733004.2733028},
year = {2014}
}
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 165 | DB2 with BLU Acceleration: So Much More than Just a Column Store | 2013 | VLDB | 0.00027693424 |
| 425 | Shark: SQL and Rich Analytics at Scale | 2013 | SIGMOD | 0.00018704491 |
| 1,044 | Fine-grained Partitioning for Aggressive Data Skipping | 2014 | SIGMOD | 0.0001244236 |
| 1,225 | Processing a Trillion Cells per Mouse Click | 2012 | VLDB | 0.00011590013 |
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