Evaluating triggers using decision trees

Abstract

This paper presents an algorithm for impkmenting rule filtering in active and trigger enabled databases. The algorithm generates one or more decision trees that determine what rules or triggers might be enabled by an individual database element, reducing the number of rules or triggers that must be evaluated. The algorithm operates by symbolically representing tbe space of database elements and subdividing the space based on mle predicates. Regions of the state space represent particular combinations of enabled rules. Decision trees are then generated based on the subdivided state space. The trees have the important property that no individual test is repeated. The ordered binary decision diagram (BDD) data structure is used to represent and manipulate the state space.