The Lernaean Hydra of Data Series Similarity Search: An Experimental Evaluation of the State of the Art
Summary: Formalizes the Lernaean Hydra of data-series similarity search by defining the flavors and presenting the first systematic, empirical evaluation of state-of-the-art methods. Shows strengths/weaknesses, clarifies terminology, and guides method selection plus standardization directions. (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 (Paris Descartes University)
- 4. Houda Benbrahim (Mohammed V University)
BibTeX Citation
@article{echihabi_vldb19,
title = {{The Lernaean Hydra of Data Series Similarity Search: An Experimental Evaluation of the State of the Art}},
author = {Echihabi, Karima and Zoumpatianos, Kostas and Palpanas, Themis and Benbrahim, Houda},
journal = {PVLDB},
series = {{VLDB} '19},
volume = {12},
number = {2},
pages = {112--117},
doi = {10.14778/3282495.3282498},
url = {https://doi.org/10.14778/3282495.3282498},
year = {2019}
}
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