Model composition in multi-dimensional data spaces

Authors:
Haihong Yu;Jigui Sun;Xia Wu;Zehai Li
Affiliations:
College of Computer Science and Technology, Jilin University, Changchun, China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Changchun, China;College of Computer Science and Technology, Jilin University, Changchun, China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Changchun, China;College of Computer Science and Technology, Jilin University, Changchun, China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Changchun, China;College of Computer Science and Technology, Jilin University, Changchun, China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Changchun, China
Venue:
RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
Year:
2007

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Abstract

Model composition is an important problem in model management. In this paper, we propose a new method to support model composition in multi-dimensional data spaces. We define a model as a 6- tuple with input interface and output interface. An algorithm for model composition and execution is given. Moreover, the method has been applied into a practical project. The running statistics showed that there had been 105 instances of model composition, and 89 decision problems had been effectively solved.