Dynamic Aggregation to Support Pattern Discovery: A Case Study with Web Logs

  • Lida Tang
  • Ben Shneiderman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2226)


Rapid growth of digital data collections is overwhelming the capabilities of humans to comprehend them without aid. The extraction of useful data from large raw data sets is something that humans do poorly. Aggregation is a technique that extracts important aspect from groups of data thus reducing the amount that the user has to deal with at one time, thereby enabling them to discover patterns, outliers, gaps, and clusters. Previous mechanisms for interactive exploration with aggregated data were either too complex to use or too limited in scope. This paper proposes a new technique for dynamic aggregation that can combine with dynamic queries to support most of the tasks involved in data manipulation.


Access Pattern Information Visualization Dynamic Aggregation Automatic Aggregation Interactive Exploration 
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

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Lida Tang
    • 1
  • Ben Shneiderman
    • 1
  1. 1.Department of Computer ScienceUniversity of MarylandCollege Park

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