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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
11022
Venue
VLDB
Year
2014
Pagerank
5.093636e-05
Overall Rank
12,191 | 16.36%
DOI
10.14778/2733004.2733028

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Authors

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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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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