Distributed Algorithms to Find Similar Time Series - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier
Conference Papers Year : 2020

Distributed Algorithms to Find Similar Time Series

Oleksandra Levchenko
Boyan Kolev
Djamel-Edine Edine Yagoubi
  • Function : Author
  • PersonId : 1086292
Dennis Shasha
  • Function : Author
  • PersonId : 833427
Themis Palpanas
Patrick Valduriez
Reza Akbarinia
Florent Masseglia

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.
Fichier principal
Vignette du fichier
ECMLPKDD2019.pdf (660.79 Ko) Télécharger le fichier
Origin Files produced by the author(s)
Loading...

Dates and versions

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

Identifiers

Cite

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⟩
201 View
450 Download

Altmetric

Share

More