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Online Analytical Mining for Web Access Patterns
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Author(s): Joseph Fong (City University of Hong Kong, Hong Kong), Hing K. Wong (City University of Hong Kong, Hong Kong)and Anthony Fong (City University of Hong Kong, Hong Kong)
Copyright: 2004
Pages: 33
Source title:
Advanced Topics in Database Research, Volume 3
Source Author(s)/Editor(s): Keng Siau (City University of Hong Kong, Hong Kong SAR)
DOI: 10.4018/978-1-59140-255-8.ch015
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Abstract
The WWW and its associated distributed information services provide rich world-wide online information services, where objects are linked together to facilitate interactive access. Users seeking information from the Internet traverse from one object via links to another. It is important to analyze user access patterns, which helps improve web page design by providing an efficient access between highly correlated objects, and also assists in better marketing decisions by placing advertisements in frequently visited documents. We need to study the user surfing behavior through examining the web access log, browsing frequency of web pages and computing the average duration of visitors. This chapter offers an architecture to store the derived web user access paths in a data warehouse, and facilitates its view maintainability by use of metadata. The system will update the user access paths pattern with the data warehouse by the data operation functions in the metadata. Whenever a new user access path occurs, the view maintainability is triggered by a constraint class in the metadata. The data warehouse can be analyzed on the frequent pattern tree of user access paths on the web site within a period and duration. The result is an online analytical mining path traversal pattern. Performance studies have been done to demonstrate the effectiveness and efficiency of the system with the following contributions: an architecture of online analytical mining using frame model metadata, a methodology of implementing the online analytical mining, and the resultant cluster of web pages frequently visited by users for marketing use.
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