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Fast and Scalable Mining of Time Series Motifs with Probabilistic Guarantees

Summary: Fast, scalable top-k time-series motif mining with probabilistic guarantees via LSH and self-tuning. Correctness proofs and cost bounds; optimizations prune distance computations; CPU-scale experiments show billion-point data processed with speedups. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
13084
Venue
VLDB
Year
2022
Pagerank
6.2939621e-05
Overall Rank
5,266 | 63.88%
DOI
10.14778/3565838.3565840

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BibTeX Citation

@article{ceccarello_vldb22,
        title = {{Fast and Scalable Mining of Time Series Motifs with Probabilistic Guarantees}},
        author = {Ceccarello, Matteo and Gamper, Johann},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {13},
        pages = {3841--3853},
        doi = {10.14778/3565838.3565840},
        url = {https://doi.org/10.14778/3565838.3565840},
        year = {2022}
}

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