We propose a data mining model that captures the user navigation behaviour patterns. The user navigation sessions are modelled as ahypertext probabilistic grammar whose higher probability strings correspond to the user's preferred trails. An algorithm to efficiently mine suchtrailsisgiven. Wemake use of the Ngram model which assumes that the last N pages browsed affect the probability of the next page to be visited. The model is based on the theory of probabilistic grammars providing it with a sound theoretical foundation for future enhancements. Moreover, we propose the use of entropy as an estimator of the grammar's statistical properties. Extensive experiments were conducted and the results show that the algorithm runs in linear time, the grammar's entropy is a good estimator of the number of mined trails and the real data rules confirm the effectiveness of the model.

This paper presents a study of web content adaptation to improve server overload performance, as well as an implementation of a web content adaptation software prototype. When the request rate on a web server increases beyond server capacity, the server becomes overloaded and unresponsive. The TCP listen queue of the server's socket overflows exhibiting a drop-tail behavior. As a result, clients experience service outages. Since clients typically issue multiple requests over the duration of a session with the server, and since requests are dropped indiscriminately, all clients connecting to the server at overload are likely to experience connection failures, even though there may be enough capacity on the server to deliver all responses properly for a subset of clients. In this paper, we propose to resolve the overload problem by adapting delivered content to load conditions to alleviate overload. The premise is that successful delivery of a less resource intensive content under overlo...

Recent proposals have suggested Web usage mining as an enabling mechanism to overcome the problems associated with more traditional Web personalization techniques such as collaborative or contentbased filtering. These problems include lack of scalability, reliance on subjective user ratings or static profiles, and the inability to capture a richer set of semantic relationships among objects (in content-based systems). Yet, usage-based personalization can be problematic when little usage data is available pertaining to some objects or when the site contentchanges regularly.For more effective personalization, both usage and content attributes of a site must be integrated into a Web mining framework and used by the recommendation engine in a uniform manner. In this

Three common approaches to server-directed automatic Web personalization have been collaborative filtering, content-based filtering, and manual decision rule systems. Recent proposals have suggested Web usage mining as an enabling mechanism to overcome the problems associated with these more traditional techniques. These problems include, for example, the reliance on subjective user ratings or static profiles, and the inability of techniques based on content similarity to capture a richer set of semantic relationships among objects (i.e., pages or products). Yet, usagebased personalization can be problematic when little usage data is available pertaining to some objects or when the site content changes regularly. For more effective personalization, both usage and content attributes of a site must be integrated into a Web mining framework and used by the recommendation engine in a uniform manner. In this paper we present such a framework, distinguishing between the offline t...