Automated construction of fuzzy event sets and its application to active databases

Fuzzy sets and fuzzy logic research aims to bridge the gap between the cris p world of math and the real world. Fuzzy set theory was applied to many di fferent areas, from control to databases. Sometimes the number of events in an event-driven system may become very high and unmanageable. Therefore, i t is very useful to organize the events into fuzzy event sets also introduc ing the benefits of the fuzzy set theory, All the events that have occurred in a system can be stored in event histories which contain precious hidden information, In this paper, we propose a method for automated construction of fuzzy event sets out of event histories via data mining techniques. The useful information hidden in the event history is extracted into a matrix called sequential proximity matrix. This matrix shows the proximities of ev ents and it is used for fuzzy rule execution via similarity based event det ection and construction of fuzzy event sets. Our application platform is ac tive databases. We describe how fuzzy event sets can be exploited for simil arity based event detection and fuzzy rule execution in active database sys tems.