Best-Match Time Series Subsequence Search on the Intel Many Integrated Core Architecture

  • Mikhail Zymbler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9282)


Subsequence similarity search is one of the basic problems of time series data mining. Nowadays Dynamic Time Warping (DTW) is considedered as the best similarity measure. However despite various existing software speedup techniques DTW is still computationally expensive. There are approaches to speed up DTW computation by means of parallel hardware (e.g. GPU and FPGA) but accelerators based on the Intel Many Integrated Core architecture have not been payed attention. The paper presents a parallel algorithm for best-match time series subsequence search based on DTW distance for the Intel Xeon Phi coprocessor. The experimental results on synthetic and real data sets confirm the efficiency of the algorithm.



This work was financially supported by the Ministry of education and science of the Russian Federation (“Research and development on priority directions of scientific-technological complex of Russia for 2014–2020” Federal Program, contract No. 14.574.21.0035).


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.South Ural State UniversityChelyabinskRussia

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