ML Anthology

Distributed Algorithms to Find Similar Time Series

Oleksandra Levchenko, Boyan Kolev, Djamel Edine Yagoubi, Dennis E. Shasha, Themis Palpanas, Patrick Valduriez, Reza Akbarinia, Florent Masseglia
ECML-PKDD 2019 pp. 781-785
doi:10.1007/978-3-030-46133-1_51 /ecmlpkdd/2019/levchenko2019ecmlpkdd-distributed/

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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Cite

Text

Levchenko et al. "Distributed Algorithms to Find Similar Time Series." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019. doi:10.1007/978-3-030-46133-1_51

Markdown

[Levchenko et al. "Distributed Algorithms to Find Similar Time Series." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019.](https://mlanthology.org/ecmlpkdd/2019/levchenko2019ecmlpkdd-distributed/) doi:10.1007/978-3-030-46133-1_51

BibTeX

@inproceedings{levchenko2019ecmlpkdd-distributed,
  title     = {{Distributed Algorithms to Find Similar Time Series}},
  author    = {Levchenko, Oleksandra and Kolev, Boyan and Yagoubi, Djamel Edine and Shasha, Dennis E. and Palpanas, Themis and Valduriez, Patrick and Akbarinia, Reza and Masseglia, Florent},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2019},
  pages     = {781-785},
  doi       = {10.1007/978-3-030-46133-1_51},
  url       = {https://mlanthology.org/ecmlpkdd/2019/levchenko2019ecmlpkdd-distributed/}
}