Interactive Visual Transformation for Symbolic Representation of Time-Oriented Data

  • Tim Lammarsch
  • Wolfgang Aigner
  • Alessio Bertone
  • Markus Bögl
  • Theresia Gschwandtner
  • Silvia Miksch
  • Alexander Rind
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7947)

Abstract

Data Mining on time-oriented data has many real-world applications, like optimizing shift plans for shops or hospitals, or analyzing traffic or climate. As those data are often very large and multi-variate, several methods for symbolic representation of time-series have been proposed. Some of them are statistically robust, have a lower-bound distance measure, and are easy to configure, but do not consider temporal structures and domain knowledge of users. Other approaches, proposed as basis for Apriori pattern finding and similar algorithms, are strongly configurable, but the parametrization is hard to perform, resulting in ad-hoc decisions. Our contribution combines the strengths of both approaches: an interactive visual interface that helps defining event classes by applying statistical computations and domain knowledge at the same time. We are not focused on a particular application domain, but intend to make our approach useful for any kind of time-oriented data.

Keywords

Data Mining KDD Data Simplification Visual Analytics 

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References

  1. 1.
    Piateski, G., Frawley, W.: Knowledge discovery in databases. MIT Press (1991)Google Scholar
  2. 2.
    Hand, D., Mannila, H., Smyth, P.: Principles of data mining. MIT Press (2001)Google Scholar
  3. 3.
    Mannila, H., Toivonen, H., Inkeri Verkamo, A.: Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery 1(3), 259–289 (1997)CrossRefGoogle Scholar
  4. 4.
    Laxman, S., Sastry, P.: A Survey of Temporal Data Mining. Sadhana 31(2), 173–198 (2006)MathSciNetMATHCrossRefGoogle Scholar
  5. 5.
    Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. ACM SIGMOD Record 22(2), 207–216 (1993)CrossRefGoogle Scholar
  6. 6.
    Srikant, R., Agrawal, R.: Mining sequential patterns: Generalizations and performance improvements. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 1–17. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  7. 7.
    Magnusson, M.: Discovering hidden time patterns in behavior: T-patterns and their detection. Behavior Research Methods 32(1), 93–110 (2000)CrossRefGoogle Scholar
  8. 8.
    Chen, Y., Chiang, M., Ko, M.: Discovering time-interval sequential patterns in sequence databases. Expert Systems with Applications 25(3), 343–354 (2003)CrossRefGoogle Scholar
  9. 9.
    Hu, Y., Huang, T., Yang, H., Chen, Y.: On mining multi-time-interval sequential patterns. Data & Knowledge Engineering 68(10), 1112–1127 (2009)CrossRefGoogle Scholar
  10. 10.
    Bertone, A., Lammarsch, T., Turic, T., Aigner, W., Miksch, S., Gaertner, J.: MuTIny: a multi-time interval pattern discovery approach to preserve the temporal information in between. In: Proc. of ECDM 2010, pp. 101–106 (2010)Google Scholar
  11. 11.
    Bertone, A., Lammarsch, T., Turic, T., Aigner, W., Miksch, S.: Does Jason Bourne need Visual Analytics to catch the Jackal? In: Kohlhammer, J., Keim, D. (eds.) Proc. First International Symposium on Visual Analytics Science and Technology held in Europe (EuroVAST 2010). Eurographics, pp. 61–67 (2010)Google Scholar
  12. 12.
    Lin, J., Keogh, E., Wei, L., Lonardi, S.: Experiencing sax: a novel symbolic representation of time series. Data Mining and Knowledge Discovery 15(2), 107–144 (2007)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases. ACM SIGMOD Record 23(2), 419–429 (1994)CrossRefGoogle Scholar
  14. 14.
    Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A symbolic representation of time series, with implications for streaming algorithms. In: Proc. 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 2–11. ACM (2003)Google Scholar
  15. 15.
    Aigner, W., Miksch, S., Schumann, H., Tominski, C.: Visualization of Time-Oriented Data. Springer (2011)Google Scholar
  16. 16.
    Andrienko, N., Andrienko, G.: Exploratory analysis of spatial and temporal data: a systematic approach. Springer (2006)Google Scholar
  17. 17.
    Smuc, M., Mayr, E., Lammarsch, T., Bertone, A., Aigner, W., Risku, H., Miksch, S.: Visualizations at First Sight: Do Insights Require Training? In: Holzinger, A. (ed.) USAB 2008. LNCS, vol. 5298, pp. 261–280. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  18. 18.
    Smuc, M., Mayr, E., Lammarsch, T., Aigner, W., Miksch, S., Gärtner, J.: To Score or Not to Score? Tripling Insights for Participatory Design. IEEE Computer Graphics and Applications 29(3), 29–38 (2009)CrossRefGoogle Scholar
  19. 19.
    Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality reduction for fast similarity search in large time series databases. Knowledge and information Systems 3(3), 263–286 (2001)MATHCrossRefGoogle Scholar
  20. 20.
    Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Locally adaptive dimensionality reduction for indexing large time series databases. ACM SIGMOD Record 30(2), 151–162 (2001)CrossRefGoogle Scholar
  21. 21.
    Yule, G.: On a method of investigating periodicities in disturbed series, with special reference to wolfer’s sunspot numbers. Philosophical Transactions of the Royal Society of London. Series A, Containing Papers of a Mathematical or Physical Character 226, 267–298 (1927)MATHCrossRefGoogle Scholar
  22. 22.
    Marx, M., Larsen, R.: Introduction to mathematical statistics and its applications. Pearson/Prentice Hall (2006)Google Scholar
  23. 23.
    Apostolico, A., Bock, M., Lonardi, S.: Monotony of surprise and large-scale quest for unusual words. Journal of Computational Biology 10(3-4), 283–311 (2003)CrossRefGoogle Scholar
  24. 24.
    Lonardi, S.: Global detectors of unusual words: design, implementation, and applications to pattern discovery in biosequences. PhD thesis, Purdue University (2001)Google Scholar
  25. 25.
    Nguyen, T.T., Skowron, A.: Rough set approach to domain knowledge approximation. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS (LNAI), vol. 2639, pp. 221–228. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  26. 26.
    Chen, H., Lv, S.: Study on ontology model based on rough set. In: Int. Symp. on Intelligent Information Technology and Security Informatics, pp. 105–108. IEEE (2010)Google Scholar
  27. 27.
    Tominski, C.: Event-based concepts for user-driven visualization. Information Visualization 10(1), 65–81 (2011)Google Scholar
  28. 28.
    Klimov, D., Shahar, Y., Taieb-Maimon, M.: Intelligent selection and retrieval of multiple time-oriented records. Journal of Intelligent Information Systems 35(2), 261–300 (2010)CrossRefGoogle Scholar
  29. 29.
    Klimov, D., Shahar, Y., Taieb-Maimon, M.: Intelligent visualization and exploration of time-oriented data of multiple patients. Artificial Intelligence in Medicine 49(1), 11–31 (2010)CrossRefGoogle Scholar
  30. 30.
    Wang, T., Plaisant, C., Shneiderman, B., Spring, N., Roseman, D., Marchand, G., Mukherjee, V., Smith, M.: Temporal summaries: Supporting temporal categorical searching, aggregation and comparison. IEEE Trans. Visualization and Computer Graphics 15(6), 1049–1056 (2009)CrossRefGoogle Scholar
  31. 31.
    Vrotsou, K., Johansson, J., Cooper, M.: Activitree: interactive visual exploration of sequences in event-based data using graph similarity. IEEE Trans. Visualization and Computer Graphics 15, 945–952 (2009)CrossRefGoogle Scholar
  32. 32.
    Funkhouser, H.: A note on a tenth century graph. Osiris, 260–262 (1936)Google Scholar
  33. 33.
    Inselberg, A., Dimsdale, B.: Parallel coordinates: A tool for visualizing multi-dimensional geometry. In: Proc. 1st Conf. Visualization 1990, pp. 361–378. IEEE (1990)Google Scholar
  34. 34.
    Lammarsch, T., Aigner, W., Bertone, A., Gärtner, J., Mayr, E., Miksch, S., Smuc, M.: Hierarchical Temporal Patterns and Interactive Aggregated Views for Pixel-based Visualizations. In: Proc. of IV 2009, pp. 44–49. IEEE (2009)Google Scholar
  35. 35.
    Keim, D., Kriegel, H.P., Ankerst, M.: Recursive pattern: A technique for visualizing very large amounts of data. In: Proc. IEEE Visualization (Vis 1995), pp. 279–286 (1995)Google Scholar
  36. 36.
    Tufte, E.R.: The Visual Display of Quantitative Information. Graphics Press, Cheshire (1983)Google Scholar
  37. 37.
    Pearson, K.: Contributions to the mathematical theory of evolution. ii. skew variation in homogeneous material. Philosophical Transactions of the Royal Society of London. A 186, 343–414 (1895)CrossRefGoogle Scholar
  38. 38.
    Bade, R., Schlechtweg, S., Miksch, S.: Connecting time-oriented data and information to a coherent interactive visualization. In: Proc. SIGCHI Conf. Human Factors in Computing Systems, pp. 105–112. ACM, Vienna (2004)Google Scholar
  39. 39.
    Aigner, W., Rind, A., Hoffmann, S.: Comparative evaluation of an interactive time-series visualization that combines quantitative data with qualitative abstractions. Computer Graphics Forum 31(3), 995–1004 (2012)CrossRefGoogle Scholar
  40. 40.
    Vlachos, M., Kollios, G., Gunopulos, D.: Discovering similar multidimensional trajectories. In: Proc. 18th Int. Conf. Data Engineering, pp. 673–684. IEEE (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tim Lammarsch
    • 1
  • Wolfgang Aigner
    • 1
  • Alessio Bertone
    • 2
  • Markus Bögl
    • 1
  • Theresia Gschwandtner
    • 1
  • Silvia Miksch
    • 1
  • Alexander Rind
    • 1
  1. 1.Institute of Software Technology and Interactive SystemsVienna University of TechnologyAustria
  2. 2.Institute of CartographyDresden University of TechnologyGermany

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