Skip to main content
SpringerLink
Log in
Book cover

International Workshop on Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data

HCI-KDD 2013: Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data pp 400–419Cite as

Interactive Visual Transformation for Symbolic Representation of Time-Oriented Data

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNISA,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.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Piateski, G., Frawley, W.: Knowledge discovery in databases. MIT Press (1991)

    Google Scholar 

  2. Hand, D., Mannila, H., Smyth, P.: Principles of data mining. MIT Press (2001)

    Google Scholar 

  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)

    Article  Google Scholar 

  4. Laxman, S., Sastry, P.: A Survey of Temporal Data Mining. Sadhana 31(2), 173–198 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  7. Magnusson, M.: Discovering hidden time patterns in behavior: T-patterns and their detection. Behavior Research Methods 32(1), 93–110 (2000)

    Article  Google Scholar 

  8. Chen, Y., Chiang, M., Ko, M.: Discovering time-interval sequential patterns in sequence databases. Expert Systems with Applications 25(3), 343–354 (2003)

    Article  Google Scholar 

  9. Hu, Y., Huang, T., Yang, H., Chen, Y.: On mining multi-time-interval sequential patterns. Data & Knowledge Engineering 68(10), 1112–1127 (2009)

    Article  Google Scholar 

  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. 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. 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)

    Article  MathSciNet  Google Scholar 

  13. Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases. ACM SIGMOD Record 23(2), 419–429 (1994)

    Article  Google Scholar 

  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. Aigner, W., Miksch, S., Schumann, H., Tominski, C.: Visualization of Time-Oriented Data. Springer (2011)

    Google Scholar 

  16. Andrienko, N., Andrienko, G.: Exploratory analysis of spatial and temporal data: a systematic approach. Springer (2006)

    Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MATH  Google Scholar 

  22. Marx, M., Larsen, R.: Introduction to mathematical statistics and its applications. Pearson/Prentice Hall (2006)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  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. Tominski, C.: Event-based concepts for user-driven visualization. Information Visualization 10(1), 65–81 (2011)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  32. Funkhouser, H.: A note on a tenth century graph. Osiris, 260–262 (1936)

    Google Scholar 

  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. 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. 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. Tufte, E.R.: The Visual Display of Quantitative Information. Graphics Press, Cheshire (1983)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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 

Download references

Author information

Authors and Affiliations

  1. Institute of Software Technology and Interactive Systems, Vienna University of Technology, Austria

    Tim Lammarsch, Wolfgang Aigner, Markus Bögl, Theresia Gschwandtner, Silvia Miksch & Alexander Rind

  2. Institute of Cartography, Dresden University of Technology, Germany

    Alessio Bertone

Authors
  1. Tim Lammarsch
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wolfgang Aigner
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alessio Bertone
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Markus Bögl
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Theresia Gschwandtner
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Silvia Miksch
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Alexander Rind
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute for Medical Informatics, Statistics and Documentation (IMI), Medical University Graz, Auenbruggerplatz 2/V, 8036, Graz, Austria

    Andreas Holzinger

  2. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Viale Sarca 336, 20126, Milano, Italy

    Gabriella Pasi

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lammarsch, T. et al. (2013). Interactive Visual Transformation for Symbolic Representation of Time-Oriented Data. In: Holzinger, A., Pasi, G. (eds) Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data. HCI-KDD 2013. Lecture Notes in Computer Science, vol 7947. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39146-0_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-39146-0_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39145-3

  • Online ISBN: 978-3-642-39146-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation