Abstract
Decision support systems (DSS) typically contain data and models to facilitate decision making. DSS users, in response to a particular decision-making situation, often execute a sequence of models, in which inputs to a model in the sequence are obtained from outputs of other models upstream in the sequence and from database retrievals. The problem of generating a sequence of models from the set of available models is known as the model composition problem. In this paper, we propose a new construct called filter spaces to support model composition. We show how filter spaces can significantly facilitate automation of model composition and execution process, and provide effective means to integrate partial solutions from multiple composite models and databases.
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