Middleware 2003

ACM/IFIP/USENIX International Middleware Conference

Rio Othon Palace Hotel

Rio de Janeiro, Brazil

16-20 June 2003

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Full Paper Abstracts

Prefetching based on Web Usage Mining

Daby Sow, David Olshefski, Mandis Beigi and Guruduth Banavar (IBM)

This paper introduces a new technique for prefetching web content by learning the access patterns
of individual users.  The learning algorithm, called Fuzzy-LZ, mines the history of user access and
identifies patterns of recurring accesses. This algorithm is evaluated analytically via a new metric
called learnability and validated experimentally by correlating learnability with prediction accuracy.
A content prefetching system that incorporates Fuzzy-LZ is described and evaluated. Our
 experiments demonstrate that Fuzzy-LZ prefetching provides a gain of 41.5 % in cache hit rate over
pure caching. This gain is highest for those users who are neither highly predictable nor highly
random, which turns out to be the vast majority of users in our workload. The overhead of our
prefetching technique for a typical user is 2.4 prefetched pages per user request.

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