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Using Interactive Visualizations of WWW Log Data to Characterize Access Patterns and Inform Site Design

Personenreihenfolge alphabetisch und evtl. nicht korrekt Harry Hochheiser, Ben Shneiderman
Erstpublikation in: Journal of the American Society for Information Science and Technology, Vol. 52, No. 4, pp. 331-343
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Ben ShneidermanHTTP 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. While 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.
Von Harry Hochheiser, Ben Shneiderman Personenreihenfolge alphabetisch und evtl. nicht korrekt im Text Using Interactive Visualizations of WWW Log Data to Characterize Access Patterns and Inform Site Design (2001)
Matthias DreierWeb server log files provide valuable information for web site designers and operators. There are numerous software packages to visualise log file data. Hochheiser and Shneiderman introduce Spotfire, a software application that allows interactive visualisations of such data.
The authors argue that most software packages only provide static visualisations of log file data. Due to the sheer quantity and complexity of these data insights through static visualisations are limited. The user must be able to filter and zoom the examined data interactively in order to gain more insights.
Hochheiser and Shneiderman begin with a brief overview of current efforts in log file visualisations. Modern software packages feature aggregation, filtering, and even analysis of individual user “trails". Unfortunately, these features often require a pre-processing step. Spotfire, however, allows the user to select the filtering criteria interactively using sliders. The main window of Spotfire provides a two-dimensional plot. Points in the plane can display more values by using size and colour coding. The authors then show six examples of plots that enable web site designers and operators answer relevant questions. The colour coding is extremely helpful to detect errors like broken links or accidentally deleted documents. The two-dimensional plot provides insights at a glance. For example, a URL vs. time plot shows which documents are retrieved at what time. Vertical clusters indicate a crawling/spidering activity. Each of the six plots is examined in detail and various effects are explained.
There are limitations that appear in most log file analysis software: It is very difficult to identify individual web users. For example, some internet service providers prevent host name resolution, others use proxy servers. Sometimes several persons use the same internet terminal, e. g. in a library or an internet café. Another problem is referrer spam or malicious crawling/spidering of web sites. When trying to answer web design questions with respect to usability aspects one has to distinguish between human users and web robots, and individual users must be identifiable. Unfortunately, Spotfire does not provide a mechanism to support either of these differentiations.
The tool presented by Hochheiser and Shneiderman is a very interesting piece of software because it allows user to explore log file data interactively. Some questions come only to the users’ mind when “playing around" with different parameters. An important aspect of log file analysis is the level of granularity. Unlike pre-processing tools Spotfire allows to adjust this level for each parameter interactively. The article closes with potential feature for future implementations. For example real-time monitoring of log files might prove useful for web site operators. The article presents many ideas of interpreting log file data. It is worth reading for web site designers and operators. Even if you are not considering using Spotfire the article provides you with insights that might change the way you look at log files.
[Source: http://www.elearning-reviews.org/]
Von Matthias Dreier, erfasst im Biblionetz am 28.07.2005

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