Web log data warehousing and mining for intelligent web caching
We introduce intelligent web caching algorithms that employ predictive models of web requests; the general idea is to extend the least recently used (LRU) policy of web and proxy servers by making it sensitive to web access models extracted from web log data using data mining techniques. Two approaches have been studied in particular, frequent patterns and decision trees. The experimental results of the new algorithms show substantial improvement over existing LRU-based caching techniques, in terms of hit rate. We designed and developed a prototypical system, which supports data warehousing of web log data, extraction of data mining models and simulation of the web caching algorithms.
log data warehousing web caching data mining frequent patterns decision trees association rules