Efficient Processing of Multiple DTW Queries in Time Series Databases

  • Hardy Kremer
  • Stephan Günnemann
  • Anca-Maria Ivanescu
  • Ira Assent
  • Thomas Seidl
Conference paper

DOI: 10.1007/978-3-642-22351-8_9

Part of the Lecture Notes in Computer Science book series (LNCS, volume 6809)
Cite this paper as:
Kremer H., Günnemann S., Ivanescu AM., Assent I., Seidl T. (2011) Efficient Processing of Multiple DTW Queries in Time Series Databases. In: Bayard Cushing J., French J., Bowers S. (eds) Scientific and Statistical Database Management. SSDBM 2011. Lecture Notes in Computer Science, vol 6809. Springer, Berlin, Heidelberg

Abstract

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Hardy Kremer
    • 1
  • Stephan Günnemann
    • 1
  • Anca-Maria Ivanescu
    • 1
  • Ira Assent
    • 2
  • Thomas Seidl
    • 1
  1. 1.RWTH Aachen UniversityGermany
  2. 2.Aarhus UniversityDenmark

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