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Furthermore, we include several optimizations to the algorithm, reducing redundant computations and leveraging the structure of time series data to speed up LSH computations. We prove the correctness of the algorithm and provide bounds to the cost of the basic operations it performs. An experimental evaluation shows that our algorithm is able to tackle time series of one billion points on a single CPU-based machine, performing orders of magnitude faster than the GPU-based state of the art.<\/jats:p>","DOI":"10.14778\/3565838.3565840","type":"journal-article","created":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T23:09:56Z","timestamp":1674256196000},"page":"3841-3853","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Fast and Scalable Mining of Time Series Motifs with Probabilistic Guarantees"],"prefix":"10.14778","volume":"15","author":[{"given":"Matteo","family":"Ceccarello","sequence":"first","affiliation":[{"name":"Free University of Bozen\/Bolzano, Bolzano, Italy"}]},{"given":"Johann","family":"Gamper","sequence":"additional","affiliation":[{"name":"Free University of Bozen\/Bolzano, Bolzano, Italy"}]}],"member":"320","published-online":{"date-parts":[[2023,1,20]]},"reference":[{"volume-title":"Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions","author":"Andoni Alexandr","key":"e_1_2_1_1_1","unstructured":"Alexandr Andoni and Piotr Indyk . 2006. 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