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
- 2. Sanjay Krishnan
- 3. Reynold S. Xin
- 4. Michael J. Franklin
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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 |
|---|---|---|---|---|
| 241 | DB2 with BLU Acceleration: So Much More than Just a Column Store | 2013 | VLDB | 0.00031314629 |
| 539 | Shark: SQL and Rich Analytics at Scale | 2013 | SIGMOD | 0.00020615453 |
| 1,470 | Processing a Trillion Cells per Mouse Click | 2012 | VLDB | 0.00011827274 |
| 1,473 | Fine-grained Partitioning for Aggressive Data Skipping | 2014 | SIGMOD | 0.00011786148 |
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