Jigsaw: A Data Storage and Query Processing Engine for Irregular Table Partitioning
Summary: Jigsaw, a storage-and-query engine, enables irregular, non-rectangular partitions to reduce I/O. A partition-at-a-time model avoids repeated reads on irregular partitions, delivering up to 4.2x speedups vs columnar and reducing data transfer to ~21% on HAP/TPC-H. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Donghe Kang (Ohio State University)
- 2. Ruochen Jiang (Ohio State University)
- 3. Spyros Blanas (Ohio State University)
BibTeX Citation
@inproceedings{kang_sigmod21,
title = {{Jigsaw: A Data Storage and Query Processing Engine for Irregular Table Partitioning}},
author = {Kang, Donghe and Jiang, Ruochen and Blanas, Spyros},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3457547},
url = {https://dl.acm.org/doi/10.1145/3448016.3457547},
year = {2021}
}
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,037 | A Deep Dive into Common Open Formats for Analytical DBMSs | 2023 | VLDB | 6.3914026e-05 |
| 6,480 | Proteus: Autonomous Adaptive Storage for Mixed Workloads | 2022 | SIGMOD | 5.8669819e-05 |
| 11,274 | Partition, Don’t Sort! Compression Boosters for Cloud Data Ingestion Pipelines | 2024 | VLDB | 5.093636e-05 |
| 11,381 | Grouping Time Series for Efficient Columnar Storage | 2023 | SIGMOD | 5.093636e-05 |
| 11,413 | SH2O: Efficient Data Access for Work-Sharing Databases | 2023 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 35 of 35 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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