Data mining for intelligent Web caching

Presents a vertical application of data warehousing and data mining technology: intelligent Web caching. We introduce several ways to construct intelligent Web caching algorithms that employ predictive models of Web requests; the general idea is to extend the LRU (least recently used) policy of Web and proxy servers by making it sensible to Web access models extracted from Web log data using data mining techniques. Two approaches have been studied, in particular one based on association rules and another based on decision trees. The experimental results of the new algorithms show substantial improvements over existing LRU-based caching techniques in terms of the hit rate, i.e. the fraction of Web documents directly retrieved in the cache. 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, around an architecture that integrates the various phases in the knowledge discovery process. The system supports a systematic evaluation and benchmarking of the proposed algorithms with respect to existing caching strategies.