Advertisement

GrammarViz 2.0: A Tool for Grammar-Based Pattern Discovery in Time Series

  • Pavel Senin
  • Jessica Lin
  • Xing Wang
  • Tim Oates
  • Sunil Gandhi
  • Arnold P. Boedihardjo
  • Crystal Chen
  • Susan Frankenstein
  • Manfred Lerner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8726)

Abstract

The problem of frequent and anomalous patterns discovery in time series has received a lot of attention in the past decade. Addressing the common limitation of existing techniques, which require a pattern length to be known in advance, we recently proposed grammar-based algorithms for efficient discovery of variable length frequent and rare patterns. In this paper we present GrammarViz 2.0, an interactive tool that, based on our previous work, implements algorithms for grammar-driven mining and visualization of variable length time series patterns1.

Keywords

Context Free Grammar Pattern Discovery Grammar Rule Input Time Series Merging Operator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lin, J., Keogh, E., Patel, P., Lonardi, S.: Finding Motifs in Time Series. In: The 2nd Workshop on Temporal Data Mining, the 8th ACM Int’l Conference on KDD, pp. 53–68 (2002)Google Scholar
  2. 2.
    Keogh, E., Lin, J., Fu, A.: HOT SAX: Efficiently Finding the Most Unusual Time Series Subsequence. In: Proc. ICDM, pp. 226–233 (2005)Google Scholar
  3. 3.
    Patel, P., Keogh, E., Lin, J., Lonardi, S.: Mining Motifs in Massive Time Series Databases. In: Proc. ICDM (2002)Google Scholar
  4. 4.
    Chandola, V., Cheboli, D., Kumar, V.: Detecting Anomalies in a Time Series Database. CS Technical Report 09–004 (2009)Google Scholar
  5. 5.
    Li, Y., Lin, J., Oates, T.: Visualizing variable-length time series motifs. In: Proc. of the 2012 SIAM International Conference on Data Mining, pp. 895–906 (2012)Google Scholar
  6. 6.
    Senin, P., Lin, J., Wang, X., Oates, T., Boedihardjo, A.P., Chen, C., Frankenstein, S., Gandhi, S.: Grammar-driven anomaly discovery in time series. CSDL Techreport 14-05 (2014)Google Scholar
  7. 7.
    Nevill-Manning, C., Witten, I.: Identifying Hierarchical Structure in Sequences: A linear-time algorithm. Journal of Artificial Intelligence Research 7, 67–82 (1997)zbMATHGoogle Scholar
  8. 8.
    Paper authors. Supporting webpage: https://code.google.com/p/jmotif/
  9. 9.
    Lin, J., Keogh, E., Lonardi, S., Lankford, J., Nystrom, D.: Visually mining and monitoring massive time series. In: Proc. 10th ACM SIGKDD Intl. Conf. on KDD, pp. 460–469 (2004)Google Scholar
  10. 10.
    Hao, M., Marwah, M., Janetzko, H., Dayal, U., Keim, D., Patnaik, D., Ramakrishnan, N., Sharma, R.K.: Visual Exploration of Frequent Patterns in Multivariate Time Series. Information Visualization 11(1), 71–83 (2012)CrossRefGoogle Scholar
  11. 11.
    Oates, T., Boedihardjo, A., Lin, J., Chen, C., Frankenstein, S., Gandhi, S.: Motif discovery in spatial trajectories using grammar inference. In: Proc. of ACM Intl. Conf. on Information and Knowledge Management, CIKM (2013)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Pavel Senin
    • 1
  • Jessica Lin
    • 2
  • Xing Wang
    • 2
  • Tim Oates
    • 3
  • Sunil Gandhi
    • 3
  • Arnold P. Boedihardjo
    • 4
  • Crystal Chen
    • 4
  • Susan Frankenstein
    • 4
  • Manfred Lerner
    • 5
  1. 1.ICS Dept., CSDLUniversity of Hawaii,ManoaUSA
  2. 2.Dept. of Computer ScienceGeorge Mason UniversityUSA
  3. 3.Dept. of Computer ScienceUniversity of Maryland, Baltimore CountyUSA
  4. 4.Engineer Research and Development CenterU.S. Army Corps of EngineersUSA
  5. 5.SAPGermany

Personalised recommendations