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)


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.


Data Mining KDD Data Simplification Visual Analytics 


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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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