Off-line and integrated data mining for intelligent systems

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The widespread and ease of use of the World-Wide Web have helped make web-based applications a regular practice in software industry. This practice is even more pronounced when it comes to distributed applications such as electronic commerce and distance learning. With these applications, there is an important need to better understand the user on-line behaviour in order to improve the application (i.e. increase profit in the case of e-commerce, and improve the learning experience in the case of e-learning). Web-based applications track on-line resource accesses in a web access log, which typically becomes very large in size. What distinguish the data that result from web-based activities in general and e-learning in particular are the sheer complexity and the vast size of data, and the fact that simple information extraction is not possible. Relevant information about user behaviour must be deduced by interactive data mining and associated data visualization techniques. This research focuses on devising data mining techniques specific to the context of web-based information systems and in particular with application in e-learning. The research will address issues for (1) off-line data mining (i.e. data mining performed after the collection of data), and (2) integrated data mining (i.e. data mining performed real-time on data streams). The objectives for off-line data mining are to devise new algorithms (such as in clustering, classification, association analysis, sequence analysis, etc.) and new visualization techniques for visualizing website path traversals and resources access and usage to help web designers and educators in the case of e-learning assess analytically the on-line activities and eventually evaluate the users (i.e. learners). The objectives for integrated data mining are to design intelligent software agents that monitor on-line learners activities and dynamically adjust and restructure the online resources accordingly, as well as automatically recommend learning activities to learners based on previous successful learning behaviour of similar learners in the community.