MS-Index: Fast Top-k Subsequence Search for Multivariate Time Series under Euclidean Distance
Summary: MS-Index: an exact index for multivariate time-series subsequence nearest-neighbor search under Euclidean distance that supports ad-hoc, at-query selection of relevant channels. Scales sublinearly with number of query channels and yields 10–100× speedups over prior work. (summarized by gpt-5-mini on Mar 13 2026)
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Authors
- 1. Jens d’Hondt (Eindhoven University of Technology)
- 2. Teun Kortekaas (Eindhoven University of Technology)
- 3. Odysseas Papapetrou (Eindhoven University of Technology)
- 4. Themis Palpanas (Institut universitaire de France; Université Paris Cité)
BibTeX Citation
@article{dhondt_vldb26,
title = {{MS-Index: Fast Top-k Subsequence Search for Multivariate Time Series under Euclidean Distance}},
author = {d’Hondt, Jens and Kortekaas, Teun and Papapetrou, Odysseas and Palpanas, Themis},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {2},
pages = {99--112},
doi = {10.14778/3773749.3773751},
url = {https://doi.org/10.14778/3773749.3773751},
year = {2026}
}
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