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Conference Papers Year : 2020

Distributed Algorithms to Find Similar Time Series

Oleksandra Levchenko
Boyan Kolev
Djamel-Edine Edine Yagoubi
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Dennis Shasha
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Themis Palpanas
Patrick Valduriez
Reza Akbarinia
Florent Masseglia


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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lirmm-02265726 , version 1 (12-08-2019)



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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