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Data Series Progressive Similarity Search with Probabilistic Quality Guarantees
Summary: Proposes a probabilistic learning-based method for progressive NN search on data series with probabilistic quality guarantees. Delivers initial and progressive estimates with stopping criteria, showing practical accuracy/efficiency gains.
(summarized by gpt-5-nano on Feb 09 2026)
Paper ID
6028
Venue
SIGMOD
Year
2020
Pagerank
6.8283344e-05
Overall Rank
4,212 | 71.11%
DOI
10.1145/3318464.3389751
Incoming Non-self Citations Over Time
Authors
1.
Anna Gogolou
(INRIA; National Centre for Scientific Research; University of Paris)
2.
Theophanis Tsandilas
(INRIA; National Centre for Scientific Research; University of Paris)
3.
Karima Echihabi
(IRDA Rabat IT Center; Mohammed V University; National School of Computer Science and Systems Analysis (ENSIAS))
4.
Anastasia Bezerianos
(INRIA; National Centre for Scientific Research; University of Paris)
5.
Themis Palpanas
(French University Institute; University of Paris)
BibTeX Citation
Copy BibTeX
@inproceedings{gogolou_sigmod20,
title = {{Data Series Progressive Similarity Search with Probabilistic Quality Guarantees}},
author = {Gogolou, Anna and Tsandilas, Theophanis and Echihabi, Karima and Bezerianos, Anastasia and Palpanas, Themis},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3389751},
url = {https://dl.acm.org/doi/10.1145/3318464.3389751},
year = {2020}
}
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