To effectively perform data mining, however, we cannot naively consider all candidate instantiations, since the number of such instantiations is exponential in the number of variables. We provide algorithms and heuristics that ex- ploit the granularity system and the given constraints to reduce the hypothesis space for the pattern matching task. The global approach offers an effective procedure to dis- cover patterns of events that occur frequently in a sequence satisfying specific temporal relationships. We consider our algorithms and heuristics as part of a general data mining system which should include, among other subsystems, a user interface. Data mining requests are issued through the user interface and processed by the data mining algorithms
