Model Composition in Multi-dimensional Data Spaces

  • Haihong Yu
  • Jigui Sun
  • Xia Wu
  • Zehai Li
Conference paper

DOI: 10.1007/978-3-540-72458-2_55

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4481)
Cite this paper as:
Yu H., Sun J., Wu X., Li Z. (2007) Model Composition in Multi-dimensional Data Spaces. In: Yao J., Lingras P., Wu WZ., Szczuka M., Cercone N.J., Ślȩzak D. (eds) Rough Sets and Knowledge Technology. RSKT 2007. Lecture Notes in Computer Science, vol 4481. Springer, Berlin, Heidelberg

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.

Keywords

Decision Support System Model Management Model Composition Multi-dimension Data 

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Haihong Yu
    • 1
    • 2
  • Jigui Sun
    • 1
    • 2
  • Xia Wu
    • 1
    • 2
  • Zehai Li
    • 1
    • 2
  1. 1.College of Computer Science and Technology, Jilin University, 130012 ChangchunChina
  2. 2.Key Laboratory of Symbolic Computation and Knowledge Engineering, of Ministry of Education, 130012 ChangchunChina

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