Database Reference
In-Depth Information
Chapter XV
Online Analytical Mining
for Web Access Patterns
Joseph Fong, City University of Hong Kong, Hong Kong
Hing Kwok Wong, City University of Hong Kong, Hong Kong
Anthony Fong, City University of Hong Kong, Hong Kong
ABSTRACT
The WWW and its associated distributed information services provide rich world-wide on-
line 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 effi cient 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 surfi ng behavior through examining the web access log, browsing frequency
of web pages and computing the average duration of visitors. This chapter offers an ar-
chitecture 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 analyti-
cal mining path traversal pattern. Performance studies have been done to demonstrate the
effectiveness and effi ciency 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.
