An adaptive approach to schema classification for data warehouse modeling

  • Authors:

  • Hong-Ding Wang;Yun-Hai Tong;Shao-Hua Tan;Shi-Wei Tang;Dong-Qing Yang;Guo-Hui Sun

  • Affiliations:

  • School of Electronics Engineering and Computer Science, Peking University, Beijing, China and National Laboratory on Machine Perception;School of Electronics Engineering and Computer Science, Peking University, Beijing, China and National Laboratory on Machine Perception;School of Electronics Engineering and Computer Science, Peking University, Beijing, China and National Laboratory on Machine Perception;School of Electronics Engineering and Computer Science, Peking University, Beijing, China and National Laboratory on Machine Perception;School of Electronics Engineering and Computer Science, Peking University, Beijing, China;Microsoft Co., Ltd, Beijing, China

  • Venue:
  • Journal of Computer Science and Technology
  • Year:
  • 2007

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Abstract

Data warehouse (DW) modeling is a complicated task, involving both knowledge of business processes and familiarity with operational information systems structure and behavior. Existing DW modeling techniques suffer from the following major drawbacks -- data-driven approach requires high levels of expertise and neglects the requirements of end users, while demand-driven approach lacks enterprise-wide vision and is regardless of existing models of underlying operational systems. In order to make up for those shortcomings, a method of classification of schema elements for DW modeling is proposed in this paper. We first put forward the vector space models for subjects and schema elements, then present an adaptive approach with self-tuning theory to construct context vectors of subjects, and finally classify the source schema elements into different subjects of the DW automatically. Benefited from the result of the schema elements classification, designers can model and construct a DW more easily.