Return of the Lernaean Hydra: Experimental Evaluation of Data Series Approximate Similarity Search
Summary: Taxonomy reconciling data-series and multidimensional-vector similarity, with indexing changes for approximate queries with guarantees. Study on synthetic/real data, in-memory and on-disk, showing data-series methods beat disk-based vector baselines and stay competitive in memory. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Karima Echihabi (Mohammed V University)
- 2. Kostas Zoumpatianos (Harvard University)
- 3. Themis Palpanas (University of Paris)
- 4. Houda Benbrahim (Mohammed V University)
BibTeX Citation
@article{echihabi_vldb20,
title = {{Return of the Lernaean Hydra: Experimental Evaluation of Data Series Approximate Similarity Search}},
author = {Echihabi, Karima and Zoumpatianos, Kostas and Palpanas, Themis and Benbrahim, Houda},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {3},
pages = {402--419},
doi = {10.14778/3368289.3368303},
url = {https://doi.org/10.14778/3368289.3368303},
year = {2020}
}
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