Dumpy: A Compact and Adaptive Index for Large Data Series Collections
Summary: Dumpy is a compact, adaptive multi-ary index for large data-series collections, enabling fast index building and high-accuracy search. By addressing iSAX limitations—proximity-compactness trade-offs and skew—via adaptive node splitting and Dumpy-Fuzzy duplication, it achieves better efficiency, scalability, and accuracy. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zeyu Wang (Fudan University)
- 2. Qitong Wang (Universit e9 Paris Cit e9)
- 3. Peng Wang (Fudan University)
- 4. Themis Palpanas (Institut Universitaire de France; Universit e9 Paris Cit e9)
- 5. Wei Wang (Fudan University)
BibTeX Citation
@inproceedings{wang_sigmod23,
title = {{Dumpy: A Compact and Adaptive Index for Large Data Series Collections}},
author = {Wang, Zeyu and Wang, Qitong and Wang, Peng and Palpanas, Themis and Wang, Wei},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3588965},
url = {https://dl.acm.org/doi/10.1145/3588965},
year = {2023}
}
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