Efficient Processing of Multiple DTW Queries in Time Series Databases

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperConference articleResearchpeer-review

  • Hardy Kremer, RWTH Aachen University, Germany
  • Stephan Günnemann, RWTH Aachen University, Germany
  • Anca-Maria Ivanescu, RWTH Aachen University, Germany
  • Ira Assent
  • Thomas Seidl, RWTH Aachen University, Germany
  • Department of Computer Science
Dynamic Time Warping (DTW) is a widely used distance measure for time series that has been successfully used in science and many other application domains. As DTW is computationally expensive, there is a strong need for efficient query processing algorithms. Such algorithms exist for single queries. In many of today’s applications, however, large numbers of queries arise at any given time. Existing DTW techniques do not process multiple DTW queries simultaneously, a serious limitation which slows down overall processing.
In this paper, we propose an efficient processing approach for multiple DTW queries. We base our approach on the observation that algorithms in areas such as data mining and interactive visualization incur many queries that share certain characteristics. Our solution exploits these shared characteristics by pruning database time series with respect to sets of queries, and we prove a lower-bounding property that guarantees no false dismissals. Our technique can be flexibly combined with existing DTW lower bounds or other single DTW query speed-up techniques for further runtime reduction. Our thorough experimental evaluation demonstrates substantial performance gains for multiple DTW queries.
Original languageEnglish
Book seriesLecture Notes in Computer Science
Volume6809
Pages (from-to)150-167
Number of pages18
ISSN0302-9743
DOIs
Publication statusPublished - 2011
Event23rd Scientific and Statistical Database Management Conference. SSDBM 2011 - Portland, OR, United States
Duration: 20 Jul 201122 Jul 2011

Conference

Conference23rd Scientific and Statistical Database Management Conference. SSDBM 2011
CountryUnited States
CityPortland, OR
Period20/07/201122/07/2011

Bibliographical note

Title of the vol.: Scientific and Statistical Database Management. Proceedings / Judith Bayard Cushing, James French and Shawn Bowers (eds.)
ISBN: 978-3-642-22350-1

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