Hierarchical Residual Encoding for Multiresolution Time Series Compression
Summary: Hierarchical Residual Encoding enables multiresolution time series compression from a single encoding, producing multiple fidelities. It reduces storage footprint and compression latency versus multi-resolution re-encoding, and offers faster decompression at high-accuracy demands. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Bruno Barbarioli (University of Chicago)
- 2. Gabriel Mersy (University of Chicago)
- 3. Stavros Sintos (University of Illinois Chicago)
- 4. Sanjay Krishnan (University of Chicago)
BibTeX Citation
@inproceedings{barbarioli_sigmod23,
title = {{Hierarchical Residual Encoding for Multiresolution Time Series Compression}},
author = {Barbarioli, Bruno and Mersy, Gabriel and Sintos, Stavros and Krishnan, Sanjay},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3588953},
url = {https://dl.acm.org/doi/10.1145/3588953},
year = {2023}
}
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,469 | Optimizing Collections of Bloom Filters within a Space Budget | 2024 | VLDB | 5.609743e-05 |
| 7,892 | Serf: Streaming Error-Bounded Floating-Point Compression | 2025 | SIGMOD | 5.5210739e-05 |
| 10,245 | FLEA: Frequency-based Lossless Encoding Algorithm for Periodic Time Series | 2026 | SIGMOD | 5.093636e-05 |
| 10,923 | Improving Time Series Data Compression in Apache IoTDB | 2025 | VLDB | 5.093636e-05 |
| 11,246 | Blitzcrank: Fast Semantic Compression for In-memory Online Transaction Processing | 2024 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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