Research

Using interactive visualizations of WWW log data to characterize access patterns and inform site design

  1. Harry Hochheiser1,
  2. Ben Shneiderman2

Article first published online: 5 JAN 2001

DOI: 10.1002/1532-2890(2000)9999:9999<::AID-ASI1066>3.0.CO;2-Y

Issue

Journal of the American Society for Information Science and Technology

Journal of the American Society for Information Science and Technology

Volume 52, Issue 4, pages 331–343, 2001

Additional Information

How to Cite

Hochheiser, H. and Shneiderman, B. (2001), Using interactive visualizations of WWW log data to characterize access patterns and inform site design. Journal of the American Society for Information Science and Technology, 52: 331–343. doi: 10.1002/1532-2890(2000)9999:9999<::AID-ASI1066>3.0.CO;2-Y

Author Information

  1. 1

    Human-Computer Interaction Lab, Department of Computer Science, University of Maryland, College Park, MD 20742

  2. 2

    Human-Computer Interaction Lab, Department of Computer Science, Institute for Systems Research and Institute for Advanced Computer Studies, University of Maryland, College Park, MD 20742

Publication History

  1. Issue published online: 12 FEB 2001
  2. Article first published online: 5 JAN 2001
  3. Manuscript Accepted: 12 JUL 2000
  4. Manuscript Revised: 29 JUN 2000
  5. Manuscript Received: 18 NOV 1999

Funded by

  • IBM's University Partnership Program

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Abstract

HTTP server log files provide Web site operators with substantial detail regarding the visitors to their sites. Interest in interpreting this data has spawned an active market for software packages that summarize and analyze this data, providing histograms, pie graphs, and other charts summarizing usage patterns. Although useful, these summaries obscure useful information and restrict users to passive interpretation of static displays. Interactive visualizations can be used to provide users with greater abilities to interpret and explore Web log data. By combining two-dimensional displays of thousands of individual access requests, color, and size coding for additional attributes, and facilities for zooming and filtering, these visualizations provide capabilities for examining data that exceed those of traditional Web log analysis tools. We introduce a series of interactive visualizations that can be used to explore server data across various dimensions. Possible uses of these visualizations are discussed, and difficulties of data collection, presentation, and interpretation are explored.

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