Fine-grained Partitioning for Aggressive Data Skipping
Summary: Fine-grained blocking with per-block skip metadata enables aggressive data skipping. Tuples map to feature vectors from frequent-itemset filters; Balanced MaxSkip Partitioning is NP-hard and solved by bottom-up clustering, yielding 2–5x speedups over range-based blocking in Shark on TPC-H and real workloads. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Liwen Sun (University of California Berkeley)
- 2. Michael J. Franklin (University of California Berkeley)
- 3. Sanjay Krishnan (University of California Berkeley)
- 4. Reynold S. Xin (Databricks; University of California Berkeley)
BibTeX Citation
@inproceedings{sun_sigmod14,
title = {{Fine-grained Partitioning for Aggressive Data Skipping}},
author = {Sun, Liwen and Franklin, Michael J. and Krishnan, Sanjay and Xin, Reynold S.},
series = {{SIGMOD} '14},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/2588555.2610515},
url = {https://dl.acm.org/doi/10.1145/2588555.2610515},
year = {2014}
}
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