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Normalized Cross-Match: Pattern Discovery Algorithm from Biofeedback Signals

  • Xueyuan GongEmail author
  • Simon Fong
  • Yain-Whar Si
  • Robert P. Biuk-Aghai
  • Raymond K. Wong
  • Athanasios V. Vasilakos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9794)

Abstract

Biofeedback signals are important elements in critical care applications, such as monitoring ECG data of a patient, discovering patterns from large amount of ECG data sets, detecting outliers from ECG data, etc. Because the signal data update continuously and the sampling rates may be different, time-series data stream is harder to be dealt with compared to traditional historical time-series data. For the pattern discovery problem on time-series streams, Toyoda proposed the CrossMatch (CM) approach to discover the patterns between two time-series data streams (sequences), which requires only O(n) time per data update, where n is the length of one sequence. CM, however, does not support normalization, which is required for some kinds of sequences (e.g. EEG data, ECG data). Therefore, we propose a normalized-CrossMatch approach (NCM) that extends CM to enforce normalization while maintaining the same performance capabilities.

Keywords

Pattern discovery CrossMatch NCM Time-series streams 

Notes

Acknowledgments

The authors are thankful for the financial supports by the Macao Science and Technology Development Fund under the research project (No. 126/2014/A3), titled A Scalable Data Stream Mining Methodology: Stream-based Holistic Analytics and Reasoning in Parallel, by the University of Macau and the Macau SAR government.

References

  1. 1.
    Aach, J., Church, G.M.: Aligning gene expression time series with time warping algorithms. Bioinformatics 17, 495–508 (2001)CrossRefGoogle Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    Angiulli, F., Fassetti, F.: Detecting distance-based outliers in streams of data. In: Proceedings of the 16th Conference on Information and Knowledge Management (CIKM), pp. 811–820 (2007)Google Scholar
  4. 4.
    Bu, Y., Chen, L., Fu, A.W.-C., Liu, D.: Efficient anomaly monitoring over moving object trajectory streams. In: Proceedings of the 15th International Conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 159–168 (2009)Google Scholar
  5. 5.
    Chiu, B., Keogh, E., Lonardi, S.: Probabilistic discovery of time series motifs. In: Proceedings of the 9th International Conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 493–498 (2003)Google Scholar
  6. 6.
    Gong, X., Si, Y.-W., Fong, S., Mohammed, S.: NSPRING: normalization-supported SPRING for subsequence matching on time series streams. In: IEEE 15th International Symposium on Computational Intelligence and Informatics (CINTI), pp. 373–378 (2014)Google Scholar
  7. 7.
    Itakura, F.: Minimum prediction residual principle applied to speech recognition. IEEE Trans. Acoust. Speech Signal Process. 23, 67–72 (1975)CrossRefGoogle Scholar
  8. 8.
    Keogh, E., Kasetty, S.: On the need for time series data mining benchmarks: a survey and empirical demonstration. Data Min. Knowl. Discov. 7, 349–371 (2003)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Keogh, E., Ratanamahatana, C.A.: Exact indexing of dynamic time warping. Knowl. Inf. Syst. 7, 358–386 (2005)CrossRefGoogle Scholar
  10. 10.
    Keogh, E., 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. Int. J. Very Large Data Bases 18, 611–630 (2009)CrossRefGoogle Scholar
  11. 11.
    Li, Y., Leong Hou, U., Yiu, M.L., Gong, Z.: Quick-motif: an efficient and scalable framework for exact motif discovery. In: Proceedings of the International Conference on Data Engineering (ICDE) (2014)Google Scholar
  12. 12.
    Mueen, A.: Enumeration of time series motifs of all lengths. In: IEEE 13th International Conference on Data Mining (ICDM), pp. 547–556 (2013)Google Scholar
  13. 13.
    Mueen, A., Keogh, E.J., Zhu, Q., Cash, S., Westover, M.B.: Exact discovery of time series motifs. In: SDM, pp. 473–484 (2009)Google Scholar
  14. 14.
    Rakthanmanon, T., Campana, B., Mueen, A., Batista, G., Westover, B., Zhu, Q., Zakaria, J., Keogh, E.: Searching and mining trillions of time series subsequences under dynamic time warping. In: Proceedings of the 18th International Conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 262–270 (2012)Google Scholar
  15. 15.
    Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26, 43–49 (1978)CrossRefzbMATHGoogle Scholar
  16. 16.
    Sakurai, Y., Faloutsos, C., Yamamuro, M.: Stream monitoring under the time warping distance. In: IEEE 23rd International Conference on Data Engineering (ICDE), pp. 1046–1055 (2007)Google Scholar
  17. 17.
    Toyoda, M., Sakurai, Y.: Discovery of cross-similarity in data streams. In: IEEE 26th International Conference on Data Engineering (ICDE), pp. 101–104 (2010)Google Scholar
  18. 18.
    Toyoda, M., Sakurai, Y., Ichikawa, T.: Identifying similar subsequences in data streams. In: Bhowmick, S.S., Küng, J., Wagner, R. (eds.) DEXA 2008. LNCS, vol. 5181, pp. 210–224. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  19. 19.
    Toyoda, M., Sakurai, Y., Ishikawa, Y.: Pattern discovery in data streams under the time warping distance. VLDB J. 22, 295–318 (2013)CrossRefGoogle Scholar
  20. 20.
    Yi, B.-K., Jagadish, H., Faloutsos, C.: Efficient retrieval of similar time sequences under time warping. In: Proceedings of the 14th International Conference on Data Engineering (ICDE), pp. 201–208 (1998)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Xueyuan Gong
    • 1
    Email author
  • Simon Fong
    • 1
  • Yain-Whar Si
    • 1
  • Robert P. Biuk-Aghai
    • 1
  • Raymond K. Wong
    • 2
  • Athanasios V. Vasilakos
    • 3
  1. 1.Department of Computer and Information ScienceUniversity of MacauMacauChina
  2. 2.School of Computer Science and EngineeringUniversity of New South WalesSydneyAustralia
  3. 3.Department of Computer Science, Electrical and Space EngineeringLulea University of TechnologyLuleaSweden

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