A framework for join pattern indexing in intelligent database systems

In intelligent database systems, knowledge directed inference often derives large amounts of data, and the efficiency of query processing in these systems depends upon how the derived data is maintained. This paper focuses on situations where the rule is conditional on a join of multiple data objects (relations) and the rule-derived data are materialized to reduce the overall query processing costs. We develop an indexing technique based on a unique construct called join pattern relation. Several pattern redundancy reduction methods are also introduced to minimize the overhead cost of join indexing.