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Discovering Multiple Time Lags of Temporal Dependencies from Fluctuating Events

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Web and Big Data (APWeb-WAIM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10988))

Abstract

As one of the key features of temporal dependency, time lag plays an important role in analyzing sequential data and predicting the developing trend. Huge number of temporal mining approaches have been successfully applied in many applications, like finance, environmental science and health-care. However, these approaches cannot effectively deal with a more realistic scenario, where more than one types of time lags are existed in sequences and all of them are fluctuating due to the inevitable noise. In this paper, we study the problem of discovering multiple time lags of temporal dependencies from event sequences considering the randomness property of the hidden time lags. We first present a parametric model as well as an EM-based solution for solving this problem. Then two approximate approaches are proposed for efficiently finding diverse types of time lags without significant loss of accuracy. Extensive empirical studies on both synthetic and real datasets demonstrate the efficiency and effectiveness of our proposed approaches.

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References

  1. Aggarwal, C.C.: Data Streams: Models and Algorithms, vol. 31. Springer, Heidelberg (2007). https://doi.org/10.1007/978-0-387-47534-9

    Book  MATH  Google Scholar 

  2. Ailamaki, A., Faloutos, C., Fischbeck, P.S., Small, M.J., VanBriesen, J.: An environmental sensor network to determine drinking water quality and security. ACM SIGMOD Rec. 32(4), 47–52 (2003)

    Article  Google Scholar 

  3. Arnold, A., Liu, Y., Abe, N.: Temporal causal modeling with graphical granger methods. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 66–75. ACM (2007)

    Google Scholar 

  4. Bahadori, M.T., Liu, Y.: An examination of practical granger causality inference. In: Proceedings of the 2013 SIAM International Conference on Data Mining, pp. 467–475. SIAM (2013)

    Chapter  Google Scholar 

  5. Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)

    Google Scholar 

  6. Bouandas, K., Osmani, A.: Mining association rules in temporal sequences. In: IEEE Symposium on Computational Intelligence and Data Mining CIDM 2007, pp. 610–615. IEEE (2007)

    Google Scholar 

  7. Dhurandhar, A.: Learning maximum lag for grouped graphical granger models. In: 2010 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 217–224. IEEE (2010)

    Google Scholar 

  8. Du, X., Jin, R., Ding, L., Lee, V.E., Thornton Jr, J.H.: Migration motif: a spatial-temporal pattern mining approach for financial markets. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135–1144. ACM (2009)

    Google Scholar 

  9. Golab, L., Karloff, H., Korn, F., Saha, A., Srivastava, D.: Sequential dependencies. Proc. VLDB Endow. 2(1), 574–585 (2009)

    Article  Google Scholar 

  10. Han, J., Pei, J., Mortazavi-Asl, B., Chen, Q., Dayal, U., Hsu, M.C.: FreeSpan: frequent pattern-projected sequential pattern mining. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 355–359. ACM (2000)

    Google Scholar 

  11. Hellerstein, J.L., Ma, S., Perng, C.S.: Discovering actionable patterns in event data. IBM Syst. J. 41(3), 475–493 (2002)

    Article  Google Scholar 

  12. IBM Tivoli Monitoring. http://www-01.ibm.com/software/tivoli/

  13. Jansen, R., Yu, H., Greenbaum, D., Kluger, Y., Krogan, N.J., Chung, S., Emili, A., Snyder, M., Greenblatt, J.F., Gerstein, M.: A bayesian networks approach for predicting protein-protein interactions from genomic data. Science 302(5644), 449–453 (2003)

    Article  Google Scholar 

  14. Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22(1), 79–86 (1951)

    Article  MathSciNet  Google Scholar 

  15. Laxman, S., Sastry, P.S.: A survey of temporal data mining. Sadhana 31(2), 173–198 (2006)

    Article  MathSciNet  Google Scholar 

  16. Li, T.: Event Mining. Chapman and Hall/CRC, Boca Raton (2015)

    Google Scholar 

  17. Li, T., Liang, F., Ma, S., Peng, W.: An integrated framework on mining logs files for computing system management. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 776–781. ACM (2005)

    Google Scholar 

  18. Li, T., Ma, S.: Mining temporal patterns without predefined time windows. In: Fourth IEEE International Conference on Data Mining ICDM 2004, pp. 451–454. IEEE (2004)

    Google Scholar 

  19. Li, T., Peng, W., Perng, C., Ma, S., Wang, H.: An integrated data-driven framework for computing system management. IEEE Trans. Syst. Man Cybern.-Part A: Syst. Hum. 40(1), 90–99 (2010)

    Article  Google Scholar 

  20. Lozano, A.C., Abe, N., Liu, Y., Rosset, S.: Grouped graphical granger modeling methods for temporal causal modeling. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 577–586. ACM (2009)

    Google Scholar 

  21. Ma, S., Hellerstein, J.L.: Mining partially periodic event patterns with unknown periods. In: Proceedings 17th International Conference on Data Engineering 2001, pp. 205–214. IEEE (2001)

    Google Scholar 

  22. Pei, J., Han, J., Mortazavi-Asl, B., Pinto, H., Chen, Q., Dayal, U., Hsu, M.: Prefixspan: mining sequential patterns by prefix-projected growth. In: Proceedings of the 17th International Conference on Data Engineering, pp. 215–224. IEEE Computer Society, Washington, DC, USA (2001)

    Google Scholar 

  23. Peng, W., Perng, C., Li, T., Wang, H.: Event summarization for system management. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1028–1032. ACM (2007)

    Google Scholar 

  24. Schroeder, D.J.: Astronomical Optics. Academic press, Cambridge (1999)

    Google Scholar 

  25. Song, L., Kolar, M., Xing, E.P.: Time-varying dynamic Bayesian networks. In: Advances in Neural Information Processing Systems, pp. 1732–1740 (2009)

    Google Scholar 

  26. Srikant, R., Agrawal, R.: Mining sequential patterns: generalizations and performance improvements. In: Apers, P., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 1–17. Springer, Heidelberg (1996). https://doi.org/10.1007/BFb0014140

    Chapter  Google Scholar 

  27. Tan, P.N., et al.: Introduction to Data Mining. Pearson Education India, London (2006)

    Google Scholar 

  28. Tang, L., Li, T., Shwartz, L.: Discovering lag intervals for temporal dependencies. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 633–641. ACM (2012)

    Google Scholar 

  29. Zeng, C., Wang, Q., Wang, W., Li, T., Shwartz, L.: Online inference for time-varying temporal dependency discovery from time series. In: 2016 IEEE International Conference on Big Data (Big Data), pp. 1281–1290 (2016)

    Google Scholar 

  30. Zeng, C., Tang, L., Li, T., Shwartz, L., Grabarnik, G.Y.: Mining temporal lag from fluctuating events for correlation and root cause analysis. In: 2014 10th International Conference on Network and Service Management (CNSM), pp. 19–27. IEEE (2014)

    Google Scholar 

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Authors and Affiliations

  1. School of Computing and Information Sciences, Florida International University, Miami, FL, 33199, USA

    Wentao Wang, Chunqiu Zeng & Tao Li

Authors
  1. Wentao Wang
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  2. Chunqiu Zeng
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  3. Tao Li
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Corresponding author

Correspondence to Wentao Wang .

Editor information

Editors and Affiliations

  1. South China University of Technology, Guangzhou, China

    Yi Cai

  2. Nagoya University, Nagoya, Japan

    Yoshiharu Ishikawa

  3. Hong Kong Baptist University, Kowloon Tong, Hong Kong, China

    Jianliang Xu

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Wang, W., Zeng, C., Li, T. (2018). Discovering Multiple Time Lags of Temporal Dependencies from Fluctuating Events. In: Cai, Y., Ishikawa, Y., Xu, J. (eds) Web and Big Data. APWeb-WAIM 2018. Lecture Notes in Computer Science(), vol 10988. Springer, Cham. https://doi.org/10.1007/978-3-319-96893-3_10

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  • DOI: https://doi.org/10.1007/978-3-319-96893-3_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-96892-6

  • Online ISBN: 978-3-319-96893-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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