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Data mining of search engine logs

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  • Martin Whittle
  • Barry Eaglestone
  • Nigel Ford
  • Valerie J. Gillet
  • Andrew Madden

Abstract

This article reports on the development of a novel method for the analysis of Web logs. The method uses techniques that look for similarities between queries and identify sequences of “query transformation”. It allows sequences of query transformations to be represented as graphical networks, thereby giving a richer view of search behavior than is possible with the usual sequential descriptions. We also perform a basic analysis to study the correlations between observed transformation codes, with results that appear to show evidence of behavior habits. The method was developed using transaction logs from the Excite search engine to provide a tool for an ongoing research project that is endeavoring to develop a greater understanding of Web‐based searching by the general public.

Suggested Citation

  • Martin Whittle & Barry Eaglestone & Nigel Ford & Valerie J. Gillet & Andrew Madden, 2007. "Data mining of search engine logs," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(14), pages 2382-2400, December.
    • Handle: RePEc:bla:jamist:v:58:y:2007:i:14:p:2382-2400
    DOI: 10.1002/asi.20733
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