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.
%0 Journal Article
%1 citeulike:1704454
%A Whittle, Martin
%A Eaglestone, Barry
%A Ford, Nigel
%A Gillet, Valerie J.
%A Madden, Andrew
%C Department of Information Studies, University of Sheffield, UK
%D 2007
%J Journal of the American Society for Information Science and Technology
%K log, mining, query, transformation
%N 9999
%P NA+
%R 10.1002/asi.20733
%T Data mining of search engine logs
%U http://dx.doi.org/10.1002/asi.20733
%V 9999
%X 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.
@article{citeulike:1704454,
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.},
added-at = {2008-06-17T16:01:02.000+0200},
address = {Department of Information Studies, University of Sheffield, UK},
author = {Whittle, Martin and Eaglestone, Barry and Ford, Nigel and Gillet, Valerie J. and Madden, Andrew},
biburl = {https://www.bibsonomy.org/bibtex/22498b2d6fd1e85bda16138915a7337e6/pprett},
citeulike-article-id = {1704454},
doi = {10.1002/asi.20733},
interhash = {452909a859438766b194cbbd4590edb8},
intrahash = {2498b2d6fd1e85bda16138915a7337e6},
journal = {Journal of the American Society for Information Science and Technology},
keywords = {log, mining, query, transformation},
number = 9999,
pages = {NA+},
posted-at = {2007-12-06 17:45:49},
priority = {4},
timestamp = {2008-06-17T16:01:38.000+0200},
title = {Data mining of search engine logs},
url = {http://dx.doi.org/10.1002/asi.20733},
volume = 9999,
year = 2007
}