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Summary Many distributed computing environments are based on the paradigms of peer-to-peer networks and grid-based computing. These environments tend to be structurally dynamic but with volatile resource and service availability. Such environments have rules, constraints, and guidelines that govern how tasks are performed. The rules describe how re...
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... (dormant). Formulation occurs when a "higher power" (i.e. user) creates new rules. New rules can then be activated. An activated rule may become enforced, i.e. applied to tasks, or become deactivated (inactive, retired, expired) or dormant. A dormant rule can be re-activated at any time. The rule execution life-cycle can be represented ( Fig. 1) as a state transition diagram with the four possible states: new, active, inactive and enforced. The first three states are final while the last is not. Rule execution is deterministic and can be expressed as a deterministic finite automaton, R by the 5-tuple: R= (Q, Σ, q 0 , F, δ) where Q is a finite set of states, Σ is a finite set ...
Citations
Production systems based on knowledge rules have been widely used for reasoning both in industry and academia. However, rule matching in production system is time-consuming too much and it always incur the system crash when the massive knowledge exceeds the limitations of memory and computing capacity of one single computer. The advent of cloud computing-a new on-demand computing model brings us an inspiring perspective to address this problem. In this paper, a MapReduce-based rule matching method was proposed. It decomposes the task of rule matching and maps subtasks to different computers in a distributed and parallel computing environment, and gets the final matching result after reduce phase. An experimental evaluation shows the high efficiency of the method.
