The World Wide Web is continuously growing, being today one of the biggest repositories ever built. As a consequence, there is an increasing need of making Web sites adaptive, i.e. capable of providing different content/structure to users according to their needs and preferences. Analyzing the visitor behavior is a fundamental part in the design of an adaptive Web site. A common source of information about the visitor browsing behavior is represented by Web server log files. Processing these huge files of raw web log data in order to retrieve significant information about the navigational behavior of users is not a trivial task. In this paper, we present LODAP, a web LOg DAta Preprocessor which is able to identify actions (and hence preferences) of individual users during navigation through a Web site. To derive a model of the browsing behavior, LODAP processes a log file by activating several modules in sequence. Firstly, the data cleaning module cleans the log file from all irrelevant entries corresponding to resources not explicitly requested by the user. Then, the data structuration module groups relevant requests into user sessions, by using a time-based method. Once user sessions have been identified, LODAP uses time information about resources accessed in each session to evaluate the degree of interest for each resource requested by the user during navigation through the Web site. Finally, the data filtering module reduces the size of data concerning the extracted user sessions by removing very low support requests, i.e. requests to those resources which appear in a low number of sessions. Tests on the log files of a specific Web site are reported to show how the proposed log data preprocessor can condense raw log data into a collection of significant user sessions from which a behavior model can be easily derived.
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|Titolo:||Log data preprocessing for mining Web browsing patterns|
|Data di pubblicazione:||2007|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|