International Conference on Similarity Search and Applications

Similarity Search and Applications pp 295-306 | Cite as

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

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9371)

Abstract

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abdullaev, S., Lenskaya, O., Gayazova, A., Sobolev, D., Noskov, A., Ivanova, O., Radchenko, G.: Short-range forecasting algorithms using radar data: Translation estimate and life-cycle composite display. Bull. of South Ural State University. Series: Comput. Math. and Soft. Eng. 3(1), 17–32 (2014)Google Scholar
  2. 2.
    Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: Fayyad, U.M., Uthurusamy, R. (eds.) KDD Workshop, pp. 359–370. AAAI Press (1994)Google Scholar
  3. 3.
    Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., Keogh, E.J.: Querying and mining of time series data: experimental comparison of representations and distance measures. PVLDB 1(2), 1542–1552 (2008)Google Scholar
  4. 4.
    Duran, A., Klemm, M.: The intel many integrated core architecture. In: Smari, W.W., Zeljkovic,V. (eds.) HPCS, pp. 365–366. IEEE (2012)Google Scholar
  5. 5.
    Dyshaev, M., Sokolinskaya, I.: Representation of trading signals based on Kaufman adaptive moving average as a system of linear inequalities. Bull. of South Ural State University. Series: Comput. Math. and Soft. Eng. 2(4), 103–108 (2013)Google Scholar
  6. 6.
    Epishev, V., Isaev, A., Miniakhmetov, R., Movchan, A., Smirnov, A., Sokolinsky, L., Zymbler, M., Ehrlich, V.: Physiological data mining system for elite sports. Bull. of South Ural State University. Series: Comput. Math. and Soft. Eng. 2(1), 44–54 (2013)Google Scholar
  7. 7.
    Fu, A.W.-C., Keogh, E.J., Lau, L.Y.H., Ratanamahatana, C.A.: Scaling and time warping in time series querying. In: Böhm, K., Jensen, C.S., Haas, L.M., Kersten, M.L., Larson, P., Ooi, B.C. (eds.) Proceedings of the 31st International Conference on Very Large Data Bases, Trondheim, Norway, August 30–September 2, 2005, pp. 649–660. ACM (2005)Google Scholar
  8. 8.
    Keogh, E.J., Lau, L.Y.H., Ratanamahatana, C.A., Wong, R.C.-W.: Scaling and time warping in time series querying. VLDB J. 17(4), 899–921 (2008)CrossRefGoogle Scholar
  9. 9.
    Keogh, E.J., Wei, L., Xi, X., Vlachos, M., Lee, S.-H., Protopapas, P.: Supporting exact indexing of arbitrarily rotated shapes and periodic time series under Euclidean and warping distance measures. VLDB J. 18(3), 611–630 (2009)CrossRefGoogle Scholar
  10. 10.
    Kim, S.-W., Park, S., Chu, W.W.: An index-based approach for similarity search supporting time warping in large sequence databases. In: Georgakopoulos, D., Buchmann, A. (eds.) Proceedings of the 17th International Conference on Data Engineering, Heidelberg, Germany, April 2–6, 2001, pp. 607–614. IEEE Computer Society (2001)Google Scholar
  11. 11.
    Lim, S.-H., Park, H.-J., Kim, S.-W.: Using multiple indexes for efficient subsequence matching in time-series databases. In: Li Lee, M., Tan, K.-L., Wuwongse, V. (eds.) DASFAA 2006. LNCS, vol. 3882, pp. 65–79. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  12. 12.
    Rakthanmanon, T., Campana, B.J.L., Mueen, A., Gustavo, B., Westover, B., Zhu, Q., Zakaria, J., Keogh, E.J.: Searching and mining trillions of time series subsequences under dynamic time warping. In: Yang, Q., Agarwal, D., Pei, J. (eds.) KDD, pp. 262–270. ACM (2012)Google Scholar
  13. 13.
    Sakurai, Y., Faloutsos, C., Yamamuro, M.: Stream monitoring under the time warping distance. In: Chirkova, R., Dogac, A., Özsu, M.T., Sellis, T.K. (eds.) Proceedings of the 23rd International Conference on Data Engineering, ICDE 2007, The Marmara Hotel, Istanbul, Turkey, April 15–20, 2007, pp. 1046–1055. IEEE (2007)Google Scholar
  14. 14.
    Sart, D., Mueen, A., Najjar, W.A., Keogh, E.J., Niennattrakul, V.: Accelerating dynamic time warping subsequence search with GPUs and FPGAs. In: Webb, G.I., Liu, B., Zhang, C., Gunopulos, D., Wu, X. (eds.) ICDM, pp. 1001–1006. IEEE Computer Society (2010)Google Scholar
  15. 15.
    Sharanyan, S., Arvind, K., Rajeev, G.: Implementing the dynamic time warping algorithm in multithreaded environments for real time and unsupervised pattern discovery. In: Department of Computer Science and Motial Nehru National Institute of Technology Engineering, ICCCT, pp. 394–398. IEEE Computer Society (2011)Google Scholar
  16. 16.
    Takahashi, N., Yoshihisa, T., Sakurai, Y., Kanazawa, M.: A parallelized data stream processing system using dynamic time warping distance. In: Barolli, L., Xhafa, F., Hsu, H.-H. (eds.) 2009 International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2009, Fukuoka, Japan, March 16–19, 2009, pp. 1100–1105. IEEE Computer Society (2009)Google Scholar
  17. 17.
    Wang, Z., Huang, S., Wang, L., Li, H., Wang, Y., Yang, H.: Accelerating subsequence similarity search based on dynamic time warping distance with FPGA. In: Hutchings, B.L., Betz, V. (eds.) The 2013 ACM/SIGDA International Symposium on Field Programmable Gate Arrays, FPGA 2013, Monterey, CA, USA, February 11–13, 2013, pp. 53–62. ACM (2013)Google Scholar
  18. 18.
    Zhang, Y., Adl, K., Glass, J.R.: Fast spoken query detection using lower-bound dynamic time warping on graphical processing units. In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2012, Kyoto, Japan, March 25–30, 2012, pp. 5173–5176. IEEE (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.South Ural State UniversityChelyabinskRussia

Personalised recommendations