East European Conference on Advances in Databases and Information Systems

ADBIS 2015: Advances in Databases and Information Systems pp 275-286 | Cite as

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

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

Abstract

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.

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. S. Ural State Univ. Ser. Comput. Math. 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. S. Ural State Univ. Ser.: Comput. Math. 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. S. Ural State Univ. Ser.: Comput. Math. 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, pp. 649–660. ACM, Trondheim, Norway, 30 August – 2 September 2005 (2005)Google Scholar
  8. 8.
    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
  9. 9.
    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
  10. 10.
    Pearson, K.: The problem of the random walk. Nat. 72(1865), 294 (1905)CrossRefMATHGoogle Scholar
  11. 11.
    Rakthanmanon, T., Campana, B.J.L., Mueen, A., Batista, G.E.A.P.A., Westover, M.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
  12. 12.
    Sakurai, Y., Faloutsos, C., Yamamuro, M.: Stream monitoring under the time warping distance. In: Chirkova, R., Dogac, A., Tamer Özsu, M., Sellis, T.K. (eds.) Proceedings of the 23rd International Conference on Data Engineering, ICDE 2007, pp. 1046–1055. IEEE, The Marmara Hotel, Istanbul, Turkey, 15–20 April 2007 (2007)Google Scholar
  13. 13.
    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
  14. 14.
    Srikanthan, S., Kumar, A., Gupta, R.: 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
  15. 15.
    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.) CISIS, pp. 1100–1105. IEEE Computer Society (2009)Google Scholar
  16. 16.
    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, pp. 53–62. ACM, Monterey, 11–13 February 2013 (2013)Google Scholar
  17. 17.
    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, pp. 5173–5176. IEEE, Kyoto, Japan, 25–30 March 2012 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.South Ural State UniversityChelyabinskRussia

Personalised recommendations