ARTEMIS: Assessing the Similarity of Event-Interval Sequences

  • Orestis Kostakis
  • Panagiotis Papapetrou
  • Jaakko Hollmén
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6912)

Abstract

In several application domains, such as sign language, medicine, and sensor networks, events are not necessarily instantaneous but they can have a time duration. Sequences of interval-based events may contain useful domain knowledge; thus, searching, indexing, and mining such sequences is crucial. We introduce two distance measures for comparing sequences of interval-based events which can be used for several data mining tasks such as classification and clustering. The first measure maps each sequence of interval-based events to a set of vectors that hold information about all concurrent events. These sets are then compared using an existing dynamic programming method. The second method, called Artemis, finds correspondence between intervals by mapping the two sequences into a bipartite graph. Similarity is inferred by employing the Hungarian algorithm. In addition, we present a linear-time lower-bound for Artemis. The performance of both measures is tested on data from three domains: sign language, medicine, and sensor networks. Experiments show the superiority of Artemis in terms of robustness to high levels of artificially introduced noise.

Keywords

Event-interval sequence distance measure Dynamic Time Warping Hungarian algorithm 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abraham, T., Roddick, J.F.: Incremental meta-mining from large temporal data sets. In: Proceedings of the Workshops on Data Warehousing and Data Mining, pp. 41–54 (1999)Google Scholar
  2. 2.
    Ale, J.M., Rossi, G.H.: An approach to discovering temporal association rules. In: Proc. of the SAC, pp. 294–300 (2000)Google Scholar
  3. 3.
    Allen, J.F.: Maintaining knowledge about temporal intervals. Communications of the ACM 26(11), 832–843 (1983)CrossRefMATHGoogle Scholar
  4. 4.
    Berendt, B.: Explaining preferred mental models in Allen inferences with a metrical model of imagery. In: Proceedings of the Annual Conference of the Cognitive Science Society, pp. 489–494 (1996)Google Scholar
  5. 5.
    Bergen, B., Chang, N.: Embodied construction grammar in simulation-based language understanding. In: Construction grammars: Cognitive grounding and theoretical extensions, pp. 147–190 (2005)Google Scholar
  6. 6.
    Chen, X., Petrounias, I.: Mining temporal features in association rules. In: Żytkow, J.M., Rauch, J. (eds.) PKDD 1999. LNCS (LNAI), vol. 1704, pp. 295–300. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  7. 7.
    Giannotti, F., Nanni, M., Pedreschi, D.: Efficient mining of temporally annotated sequences. In: SDM, vol. 6, pp. 346–357 (2006)Google Scholar
  8. 8.
    Höppner, F.: Discovery of temporal patterns. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 192–203. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  9. 9.
    Höppner, F., Klawonn, F.: Finding informative rules in interval sequences. In: Hoffmann, F., Adams, N., Fisher, D., Guimarães, G., Hand, D.J. (eds.) IDA 2001. LNCS, vol. 2189, pp. 123–132. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  10. 10.
    Hwang, S.-Y., Wei, C.-P., Yang, W.-S.: Discovery of temporal patterns from process instances. Computers in Industry 53(3), 345–364 (2004)CrossRefGoogle Scholar
  11. 11.
    Kam, P., Fu, A.W.: Discovering temporal patterns for interval-based events. In: Kambayashi, Y., Mohania, M., Tjoa, A.M. (eds.) DaWaK 2000. LNCS, vol. 1874, pp. 317–326. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  12. 12.
    Kosara, R., Miksch, S.: Visualizing complex notions of time. Studies in Health Technology and Informatics, 211–215 (2001)Google Scholar
  13. 13.
    Kostakis, O., Papapetrou, P., Hollmén, J.: Distance measure for querying arrangements of temporal intervals. In: Proc. of ACM Pervasive Technologies Related to Assistive Environments, PETRA (2011)Google Scholar
  14. 14.
    Kruskal, J.B., Liberman, M.: The symmetric time warping algorithm: From continuous to discrete. In: Time Warps. Addison-Wesley, Reading (1983)Google Scholar
  15. 15.
    Laxman, S., Sastry, P., Unnikrishnan, K.: Discovering frequent generalized episodes when events persist for different durations. IEEE Transactions on Knowledge and Data Engineering 19(9), 1188–1201 (2007)CrossRefGoogle Scholar
  16. 16.
    Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions, and reversals. Soviet Physics 10(8), 707–710 (1966)MathSciNetMATHGoogle Scholar
  17. 17.
    Lin, J.-L.: Mining maximal frequent intervals. In: Proc. of SAC, pp. 624–629 (2003)Google Scholar
  18. 18.
    Lu, H., Han, J., Feng, L.: Stock movement prediction and n-dimensional inter-transaction association rules. In: Proc. of the ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 12:1–12:7 (1998)Google Scholar
  19. 19.
    Mooney, C., Roddick, J.F.: Mining relationships between interacting episodes. In: Proc. of SDM (2004)Google Scholar
  20. 20.
    Mörchen, F.: Unsupervised pattern mining from symbolic temporal data. SIGKDD Explor. Newsl. 9, 41–55 (2007)CrossRefGoogle Scholar
  21. 21.
    Mörchen, F.: Temporal pattern mining in symbolic time point and time interval data. In: Proc. of ACM SIGKDD (2010)Google Scholar
  22. 22.
    Mörchen, F., Fradkin, D.: Robust mining of time intervals with semi-interval partial order patterns. In: SDM, pp. 315–326 (2010)Google Scholar
  23. 23.
    Munkres, J.: Algorithms for the assignment and transportation problems. Journal of the Society for Industrial and Applied Mathematics 5(1), 32–38 (1957)MathSciNetCrossRefMATHGoogle Scholar
  24. 24.
    Pachet, F., Ramalho, G., Carrive, J.: Representing temporal musical objects and reasoning in the MusES system. Journal of new music research 25(3), 252–275 (1996)CrossRefGoogle Scholar
  25. 25.
    Papapetrou, P., Benson, G., Kollios, G.: Discovering frequent poly-regions in dna sequences. In: Proc. of the IEEE ICDM Workshop on Data Mining in Bioinformatics, pp. 94–98 (2006)Google Scholar
  26. 26.
    Papapetrou, P., Kollios, G., Sclaroff, S., Gunopulos, D.: Discovering frequent arrangements of temporal intervals. In: Proc. of IEEE ICDM, pp. 354–361 (2005)Google Scholar
  27. 27.
    Papapetrou, P., Kollios, G., Sclaroff, S., Gunopulos, D.: Mining frequent arrangements of temporal intervals. In: Knowledge and Information Systems (KAIS), vol. 21, pp. 133–171 (2009)Google Scholar
  28. 28.
    Patel, D., Hsu, W., Lee, M.: Mining relationships among interval-based events for classification. In: Proc. of ACM SIGMOD, pp. 393–404 (2008)Google Scholar
  29. 29.
    Pissinou, N., Radev, I., Makki, K.: Spatio-temporal modeling in video and multimedia geographic information systems. GeoInformatica 5(4), 375–409 (2001)CrossRefMATHGoogle Scholar
  30. 30.
    Villafane, R., Hua, K.A., Tran, D., Maulik, B.: Knowledge discovery from series of interval events. Intelligent Information Systems 15(1), 71–89 (2000)CrossRefGoogle Scholar
  31. 31.
    Vlachos, M., Hadjieleftheriou, M., Gunopulos, D., Keogh, E.: Indexing multidimensional time-series. The VLDB Journal 15, 1–20 (2006)CrossRefGoogle Scholar
  32. 32.
    Winarko, E., Roddick, J.F.: Armada - an algorithm for discovering richer relative temporal association rules from interval-based data. Data & Knowledge Engineering 63(1), 76–90 (2007)CrossRefGoogle Scholar
  33. 33.
    Wu, S.-Y., Chen, Y.-L.: Mining nonambiguous temporal patterns for interval-based events. IEEE Transactions on Knowledge and Data Engineering 19(6), 742–758 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Orestis Kostakis
    • 1
    • 2
  • Panagiotis Papapetrou
    • 1
    • 2
  • Jaakko Hollmén
    • 1
    • 2
  1. 1.Department of Information and Computer ScienceAalto UniversityFinland
  2. 2.Helsinki Institute for Information TechnologyFinland

Personalised recommendations