A Belief-Driven Discovery Framework Based on Data Monitoring and Triggering

A new knowledge-discovery framework, called Data Monitoring and Discovery Triggering (DMDT),is defined, where the user specifies monitors that acirc; watchquot; for significant changes to the dataand changes to the user-defined system of beliefs. Once these changes are detected, knowledgediscovery processes, in the form of data mining queries, are triggered. The proposed frameworkis the result of an observation, made in the previous work of the authors, that when changes tothe user-defined beliefs occur, this means that, there are interesting patterns in the data. In thispaper, we present an approach for finding these interesting patterns using data monitoring andbelief-driven discovery techniques. Our approach is especially useful in those applications wheredata changes rapidly with time, as in some of the On-Line Transaction Processing (OLTP) systems. The proposed approach integrates active databases, data mining queries and subjectivemeasures of interestingness based on user-defined systems of beliefs in a novel and synergeticway to yield a new type of data mining systems