Distributed Algorithms to Find Similar Time Series

Oleksandra Levchenko 1 Boyan Kolev 1 Djamel-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.
Document type :
Conference papers
Complete list of metadatas

Cited literature [4 references]  Display  Hide  Download

https://hal-lirmm.ccsd.cnrs.fr/lirmm-02265726
Contributor : Reza Akbarinia <>
Submitted on : Monday, August 12, 2019 - 10:04:54 AM
Last modification on : Thursday, October 17, 2019 - 11:03:49 AM

File

ECMLPKDD2019.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : lirmm-02265726, version 1

Citation

Oleksandra Levchenko, Boyan Kolev, Djamel-Edine Yagoubi, Dennis Shasha, Themis Palpanas, et al.. Distributed Algorithms to Find Similar Time Series. ECML-PKDD : European Conference on Machine Learning and Knowledge Discovery in Databases, Sep 2019, Wurtzbourg, Germany. ⟨lirmm-02265726⟩

Share

Metrics

Record views

51

Files downloads

69