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<p>We have developed a problem-solving framework, called ConClass, that is capable of classifying continuous real-time problems dynamically and concurrently on a distributed system. ConClass provides an efficient development environment for describing and decomposing a classification problem and synthesizing solutions. In ConClass, decomposed concurrent subproblems specified by the application developer effectively correspond to the actual distributed hardware elements. This scheme is useful for designing and implementing efficient distributed processing, making it easier to anticipate and evaluate system behavior. The ConClass system provides an object replication feature that prevents any particular object from being overloaded. In order to deal with an indeterminate amount of problem data, ConClass dynamically creates object networks that justify hypothesized solutions, and thus achieves a dynamic load distribution. A number of efficient execution mechanisms that manage a variety of asynchronous aspects of distributed processing have been implemented without using schedulers or synchronization schemes that are liable to develop bottlenecks. We have confirmed the efficiency of parallel distributed processing and load balancing of ConClass with an experimental application.</p>
real-time systems; distributed processing; knowledge based systems; problem solving; synchronisation; classification; resource allocation; ConClass; real-time distributed knowledge-based processing; problem decomposition; continuous real-time problem classification; development environment; solution synthesis; decomposed concurrent subproblems; distributed hardware elements; system behavior evaluation; object replication; object networks; hypothesized solution justification; dynamic load distribution; execution mechanisms; signal interpretation; efficiency; parallel distributed processing; load balancing; asynchronous message passing; classification problem solving framework; concurrent programming; information fusion

H. Maegawa, "ConClass: A Framework for Real-Time Distributed Knowledge-Based Processing," in IEEE Transactions on Knowledge & Data Engineering, vol. 6, no. , pp. 909-919, 1994.
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