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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)

Paper ID
10834
Venue
VLDB
Year
2014
Pagerank
4.1945683e-05
Overall Rank
11,993 | 16.57%
DOI
-

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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
241 DB2 with BLU Acceleration: So Much More than Just a Column Store 2013 VLDB 0.00031420034
542 Shark: SQL and Rich Analytics at Scale 2013 SIGMOD 0.00020595648
1,470 Processing a Trillion Cells per Mouse Click 2012 VLDB 0.00011833779
1,477 Fine-grained Partitioning for Aggressive Data Skipping 2014 SIGMOD 0.00011770865
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