Distributed Algorithms to Find Similar Time Series

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

doi:10.1007/978-3-030-46133-1_51

Distributed Algorithms to Find Similar Time Series

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

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 parallelism. 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.

Original language	English (US)
Title of host publication	Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings
Editors	Ulf Brefeld, Elisa Fromont, Andreas Hotho, Arno Knobbe, Marloes Maathuis, Céline Robardet
Publisher	Springer
Pages	781-785
Number of pages	5
ISBN (Print)	9783030461324
DOIs	https://doi.org/10.1007/978-3-030-46133-1_51
State	Published - 2020
Event	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019 - Wurzburg, Germany Duration: Sep 16 2019 → Sep 20 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11908 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019
Country/Territory	Germany
City	Wurzburg
Period	9/16/19 → 9/20/19

Keywords

Distributed data processing
Indexing
Similarity search
Spark
Time series

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-030-46133-1_51

Cite this

Levchenko, O., Kolev, B., Yagoubi, D. E., Shasha, D., Palpanas, T., Valduriez, P., Akbarinia, R., & Masseglia, F. (2020). Distributed Algorithms to Find Similar Time Series. In U. Brefeld, E. Fromont, A. Hotho, A. Knobbe, M. Maathuis, & C. Robardet (Eds.), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings (pp. 781-785). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11908 LNAI). Springer. https://doi.org/10.1007/978-3-030-46133-1_51

Distributed Algorithms to Find Similar Time Series. / Levchenko, Oleksandra; Kolev, Boyan; Yagoubi, Djamel Edine; Shasha, Dennis; Palpanas, Themis; Valduriez, Patrick; Akbarinia, Reza; Masseglia, Florent.

Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings. ed. / Ulf Brefeld; Elisa Fromont; Andreas Hotho; Arno Knobbe; Marloes Maathuis; Céline Robardet. Springer, 2020. p. 781-785 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11908 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Levchenko, O, Kolev, B, Yagoubi, DE, Shasha, D, Palpanas, T, Valduriez, P, Akbarinia, R & Masseglia, F 2020, Distributed Algorithms to Find Similar Time Series. in U Brefeld, E Fromont, A Hotho, A Knobbe, M Maathuis & C Robardet (eds), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11908 LNAI, Springer, pp. 781-785, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019, Wurzburg, Germany, 9/16/19. https://doi.org/10.1007/978-3-030-46133-1_51

Levchenko O, Kolev B, Yagoubi DE, Shasha D, Palpanas T, Valduriez P et al. Distributed Algorithms to Find Similar Time Series. In Brefeld U, Fromont E, Hotho A, Knobbe A, Maathuis M, Robardet C, editors, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings. Springer. 2020. p. 781-785. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-46133-1_51

Levchenko, Oleksandra ; Kolev, Boyan ; Yagoubi, Djamel Edine ; Shasha, Dennis ; Palpanas, Themis ; Valduriez, Patrick ; Akbarinia, Reza ; Masseglia, Florent. / Distributed Algorithms to Find Similar Time Series. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings. editor / Ulf Brefeld ; Elisa Fromont ; Andreas Hotho ; Arno Knobbe ; Marloes Maathuis ; Céline Robardet. Springer, 2020. pp. 781-785 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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 parallelism. 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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AU - Valduriez, Patrick

AU - Akbarinia, Reza

AU - Masseglia, Florent

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Distributed Algorithms to Find Similar Time Series

Abstract

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Keywords

ASJC Scopus subject areas

Access to Document

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