PIDS: Attribute Decomposition for Improved Compression and Query Performance in Columnar Storage
Summary: PIDS uses unsupervised pattern inference to decompose string attributes into sub-attrs in columnar store, enabling per-attr encoding and compression near Snappy/Gzip. Pushdown to sub-attrs reduces I/O and comparisons, yielding faster query execution. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Hao Jiang (University of Chicago)
- 2. Chunwei Liu (University of Chicago)
- 3. Qi Jin (University of Chicago)
- 4. John Paparrizos (University of Chicago)
- 5. Aaron J. Elmore (University of Chicago)
BibTeX Citation
@article{jiang_vldb20,
title = {{PIDS: Attribute Decomposition for Improved Compression and Query Performance in Columnar Storage}},
author = {Jiang, Hao and Liu, Chunwei and Jin, Qi and Paparrizos, John and Elmore, Aaron J.},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {6},
pages = {925--938},
doi = {10.14778/3380750.3380761},
url = {https://doi.org/10.14778/3380750.3380761},
year = {2020}
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