Reliable Distributed Computing for Decision Support Systems

Reliable Distributed Computing for Decision Support Systems

Real-time decision support typically requires that several tasks such as on-line monitoring, simulation, prediction, state estimation, and real-time control are run concurrently. This is implemented most effectively in a true distributed system rather than in a time-shared uni-processor environment. Moreover, the physical size of many decision-support systems such as water distribution networks...

Computing Agents for Decision Support Systems

In decision support systems, it is essential to get a candidate solution fast, even if it means resorting to an approximation. This constraint introduces a scalability requirement with regard to the kind of heuristics which can be used in such systems. As execution time is bounded, these algorithms need to give better results and scale up with additional computing resources instead of additiona...

Integrating Visualization Support into Distributed Computing Systems

Visualization andanimation tools may become extremely important aids in the understanding, verification, and performance tuning of parallel computations. Presently, however, the use of visualization has had only a limited use for enhancing parallel computation. We hypothesize that one of the primary reasons for the limited use of visualization tools in parallel program development is the diffic...

Reliable Probabilistic Prediction for Medical Decision Support

A major drawback of most existing medical decision support systems is that they do not provide any indication about the uncertainty of each of their predictions. This paper addresses this problem with the use of a new machine learning framework for producing valid probabilistic predictions, called Venn Prediction (VP). More specifically, VP is combined with Neural Networks (NNs), which is one o...

Data mining and soft computing algorithms for decision support systems