Representing Unevenly-Spaced Time Series Data for Visualization and Interactive Exploration

  • Aleks Aris
  • Ben Shneiderman
  • Catherine Plaisant
  • Galit Shmueli
  • Wolfgang Jank
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3585)


Visualizing time series is useful to support discovery of relations and patterns in financial, genomic, medical and other applications. Often, measurements are equally spaced over time. We discuss the challenges of unevenly-spaced time series and present fourrepresentationmethods: sampled events, aggregated sampled events, event index and interleaved event index. We developed these methods while studying eBay auction data with TimeSearcher. We describe the advantages, disadvantages, choices for algorithms and parameters, and compare the different methods for different tasks. Interaction issues such as screen resolution, response time for dynamic queries, and learnability are governed by these decisions.


Time Series Event Index Aggregation Level Online Auction Information Visualization 
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.


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Copyright information

© IFIP International Federation for Information Processing 2005

Authors and Affiliations

  • Aleks Aris
    • 1
  • Ben Shneiderman
    • 1
  • Catherine Plaisant
    • 1
  • Galit Shmueli
    • 2
  • Wolfgang Jank
    • 2
  1. 1.Human-Computer Interaction LaboratoryUniversity of Maryland Institute for Advanced Computer StudiesUSA
  2. 2.Department of Decision and Information Technologies, Robert H. Smith School of BusinessUniversity of MarylandCollege ParkUSA

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