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Matching Consecutive Subpatterns over Streaming Time Series

  • Rong Kang
  • Chen Wang
  • Peng Wang
  • Yuting Ding
  • Jianmin WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10988)

Abstract

Pattern matching of streaming time series with lower latency under limited computing resource comes to a critical problem, especially as the growth of Industry 4.0 and Industry Internet of Things. However, against traditional single pattern matching model, a pattern may contain multiple subpatterns representing different physical meanings in the real world. Hence, we formulate a new problem, called “consecutive subpatterns matching”, which allows users to specify a pattern containing several consecutive subpatterns with various specified thresholds. We propose a novel representation Equal-Length Block (ELB) together with two efficient implementations, which work very well under all \(L_p\)-Norms without false dismissals. Extensive experiments are performed on synthetic and real-world datasets to illustrate that our approach outperforms the brute-force method and MSM, a multi-step filter mechanism over the multi-scaled representation by orders of magnitude.

Keywords

Pattern matching Stream Time series 

References

  1. 1.
    Agrawal, R., Faloutsos, C., Swami, A.: Efficient similarity search in sequence databases. In: Lomet, D.B. (ed.) FODO 1993. LNCS, vol. 730, pp. 69–84. Springer, Heidelberg (1993).  https://doi.org/10.1007/3-540-57301-1_5CrossRefGoogle Scholar
  2. 2.
    Begum, N., Keogh, E.: Rare time series motif discovery from unbounded streams. PVLDB 8(2), 149–160 (2014)Google Scholar
  3. 3.
    Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: KDD Workshop, vol. 10, pp. 359–370 (1994)Google Scholar
  4. 4.
    Branlard, E.: Wind energy: on the statistics of gusts and their propagation through a wind farm. In: ECN-Wind-Memo 2009, vol. 5 (2009)Google Scholar
  5. 5.
    Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases. In: SIGMOD, pp. 419–429. ACM (1994)CrossRefGoogle Scholar
  6. 6.
    Jensen, S.K., Pedersen, T.B., Thomsen, C.: Time series management systems: a survey. TKDE PP(99), 1 (2017)Google Scholar
  7. 7.
    Keogh, E.: Welcome to the UCR Time Series Classification/Clustering Page. www.cs.ucr.edu/~eamonn/time_series_data
  8. 8.
    Keogh, E.: Exact indexing of dynamic time warping. In: PVLDB, Hong Kong, China, pp. 406–417 (2002)CrossRefGoogle Scholar
  9. 9.
    Kotsifakos, A., Papapetrou, P., Hollmén, J., Gunopulos, D.: A subsequence matching with gaps-range-tolerances framework: a query-by-humming application. PVLDB 4(11), 761–771 (2011)Google Scholar
  10. 10.
    Lian, X., Chen, L., Yu, J.X., Han, J., Ma, J.: Multiscale representations for fast pattern matching in stream time series. TKDE 21(4), 568–581 (2009)Google Scholar
  11. 11.
    Lian, X., Chen, L., Yu, J.X., Wang, G., Yu, G.: Similarity match over high speed time-series streams. In: ICDE, pp. 1086–1095. IEEE, April 2007Google Scholar
  12. 12.
    Lim, H.-S., Whang, K.-Y., Moon, Y.-S.: Similar sequence matching supporting variable-length and variable-tolerance continuous queries on time-series data stream. Inf. Sci. 178(6), 1461–1478 (2008)MathSciNetCrossRefGoogle Scholar
  13. 13.
    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).  https://doi.org/10.1007/11733836_7CrossRefGoogle Scholar
  14. 14.
    Loh, W.-K., Kim, S.-W., Whang, K.-Y.: A subsequence matching algorithm that supports normalization transform in time-series databases. DMKD 9(1), 5–28 (2004)MathSciNetGoogle Scholar
  15. 15.
    Luo, G., Yi, K., Cheng, S.W., Li, Z., Fan, W., He, C., Mu, Y.: Piecewise linear approximation of streaming time series data with max-error guarantees. In: 2015 IEEE 31st International Conference on Data Engineering, pp. 173–184, April 2015Google Scholar
  16. 16.
    Moon, Y.-S., Whang, K.-Y., Han, W.-S.: General match: a subsequence matching method in time-series databases based on generalized windows. In: SIGMOD, pp. 382–393. ACM (2002)Google Scholar
  17. 17.
    Pace, A., Johnson, K., Wright, A.: LIDAR-based extreme event control to prevent wind turbine overspeed. In: 51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition, p. 315 (2012)Google Scholar
  18. 18.
    Sun, H., Deng, K., Meng, F., Liu, J.: Matching stream patterns of various lengths and tolerances. In: CIKM, pp. 1477–1480. ACM (2009)Google Scholar
  19. 19.
    Vlachos, M., Kollios, G., Gunopulos, D.: Discovering similar multidimensional trajectories. In: ICDE, pp. 673–684. IEEE (2002)Google Scholar
  20. 20.
    Wang, Y., Wang, P., Pei, J., Wang, W., Huang, S.: A data-adaptive and dynamic segmentation index for whole matching on time series. PVLDB 6(10), 793–804 (2013)Google Scholar
  21. 21.
    Wei, L., Keogh, E., Van Herle, H., Mafra-Neto, A.: Atomic wedgie: efficient query filtering for streaming time series. In: ICDM, p. 8-pp. IEEE (2005)Google Scholar
  22. 22.
    Wu, H., Salzberg, B., Zhang, D.: Online event-driven subsequence matching over financial data streams. In: SIGMOD, pp. 23–34. ACM (2004)Google Scholar
  23. 23.
    Yi, B.-K., Faloutsos, C.: Fast time sequence indexing for arbitrary Lp norms. In: PVLDB, pp. 385–394. Morgan Kaufmann Publishers Inc. (2000)Google Scholar
  24. 24.
    Zhao, J., Liu, K., Wang, W., Liu, Y.: Adaptive fuzzy clustering based anomaly data detection in energy system of steel industry. Inf. Sci. 259(Suppl C), 335–345 (2014)CrossRefGoogle Scholar
  25. 25.
    Zhu, Y., Shasha, D.: Efficient elastic burst detection in data streams. In: SIGKDD, pp. 336–345. ACM (2003)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Rong Kang
    • 1
    • 2
  • Chen Wang
    • 1
    • 2
  • Peng Wang
    • 3
  • Yuting Ding
    • 1
    • 2
  • Jianmin Wang
    • 1
    • 2
    Email author
  1. 1.School of SoftwareTsinghua UniversityBeijingChina
  2. 2.National Engineering Laboratory for Big Data SoftwareTsinghua UniversityBeijingChina
  3. 3.School of Computer ScienceFudan UniversityShanghaiChina

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