Artificial Intelligence
Programming expert systems in OPS5: an introduction to rule-based programming
Programming expert systems in OPS5: an introduction to rule-based programming
Query Optimization in Database Systems
ACM Computing Surveys (CSUR)
Parallel Rule Firing in Production Systems
IEEE Transactions on Knowledge and Data Engineering
Efficient Matching Algorithms for the SOARIOPSS Production System
Efficient Matching Algorithms for the SOARIOPSS Production System
A Meta-Level Control Architecture for Production Systems
IEEE Transactions on Knowledge and Data Engineering
Optimization of Rule-Based Systems Using State Space Graphs
IEEE Transactions on Knowledge and Data Engineering
Trigger Condition Testing and View Maintenance Using Optimized Discrimination Networks
IEEE Transactions on Knowledge and Data Engineering
Optimizing Real-Time Equational Rule-Based Systems
IEEE Transactions on Software Engineering
Shortening Matching Time in OPS5 Production Systems
IEEE Transactions on Software Engineering
COROR: a composable rule-entailment owl reasoner for resource-constrained devices
RuleML'2011 Proceedings of the 5th international conference on Rule-based reasoning, programming, and applications
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As the scale of rule-based expert systems increases, the efficiency of production systems becomes a pressing concern. Recently developed production systems thus enable users to specify an appropriate ordering or clustering of join operations. Various efficiency heuristics have been introduced to optimize production rules manually. However, since the heuristics often conflict With each other, users have to proceed by trial and error. The problem addressed in this paper is how to automatically determine efficient join structures for production system programs. Our algorithm does not directly apply efficiency heuristics to programs, but rather enumerates possible join structures under various constraints and selects the best one. For this purpose, the cost model for production systems is introduced to estimate the run-time cost of join operations. Evaluation results demonstrate that the proposed algorithm can generate programs that are as efficient as those obtained by manual optimization, and thus can reduce the burden of manual optimization.