@inproceedings{69047ea561264417a9997756f31ee289,
title = "Distributed Algorithms to Find Similar Time Series",
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.",
keywords = "Distributed data processing, Indexing, Similarity search, Spark, Time series",
author = "Oleksandra Levchenko and Boyan Kolev and Yagoubi, {Djamel Edine} and Dennis Shasha and Themis Palpanas and Patrick Valduriez and Reza Akbarinia and Florent Masseglia",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2020.; European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019 ; Conference date: 16-09-2019 Through 20-09-2019",
year = "2020",
doi = "10.1007/978-3-030-46133-1_51",
language = "English (US)",
isbn = "9783030461324",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "781--785",
editor = "Ulf Brefeld and Elisa Fromont and Andreas Hotho and Arno Knobbe and Marloes Maathuis and C{\'e}line Robardet",
booktitle = "Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Proceedings",
}