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Distributed Algorithms to Find Similar Time Series

Oleksandra Levchenko 1 Boyan Kolev 1 Djamel-Edine Edine Yagoubi 1 Dennis Shasha 2 Themis Palpanas 3 Patrick Valduriez 1 Reza Akbarinia 1 Florent Masseglia 1 
1 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : As sensors improve in both bandwidth and quantity over time, the need for high performance sensor fusion increases. This requires both better (quasi-linear time if possible) algorithms and paral-lelism. This demonstration uses financial and seismic data to show how two state-of-the-art algorithms construct indexes and answer similarity queries using Spark. Demo visitors will be able to choose query time series, see how each algorithm approximates nearest neighbors and compare times in a parallel environment.
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Contributor : Reza Akbarinia Connect in order to contact the contributor
Submitted on : Monday, August 12, 2019 - 10:04:54 AM
Last modification on : Friday, August 5, 2022 - 3:03:28 PM
Long-term archiving on: : Thursday, January 9, 2020 - 11:35:55 PM


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Oleksandra Levchenko, Boyan Kolev, Djamel-Edine Edine Yagoubi, Dennis Shasha, Themis Palpanas, et al.. Distributed Algorithms to Find Similar Time Series. ECML-PKDD 2019 - European Conference on Machine Learning and Knowledge Discovery in Databases, Sep 2019, Wurtzbourg, Germany. pp.781-785, ⟨10.1007/978-3-030-46133-1_51⟩. ⟨lirmm-02265726⟩



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