Time Series Subsequence Similarity Search Under Dynamic Time Warping Distance on the Intel Many-core Accelerators

  • Aleksandr Movchan
  • Mikhail Zymbler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9371)


Subsequence similarity search is one of the most important problems of time series data mining. Nowadays there is empirical evidence that Dynamic Time Warping (DTW) is the best distance metric for many applications. However in spite of sophisticated software speedup techniques DTW still computationally expensive. There are studies devoted to acceleration of the DTW computation by means of parallel hardware (e.g. computer-cluster, multi-core, FPGA and GPU). In this paper we present an approach to acceleration of the subsequence similarity search based on DTW distance using the Intel Many Integrated Core architecture. The experimental evaluation on synthetic and real data sets confirms the efficiency of the approach.


Dynamic Time Warping Queue Size Central Processor Unit Serial Algorithm Intel Xeon Processor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.South Ural State UniversityChelyabinskRussia

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