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 ...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.展开更多
基金Supported by the National Natural Science Foundation of China under Grant No. 60403041, the Project of National ＂10th Five-Year Plan＂ of China under Grant No. 2001BA102A01, and the National Grand Fundamental Research 973 Program of China under Grant No. 1999032705. Acknowledgements The first author is very thankful to Jian Liu, Bo Yu, Peng Zhang and Ling-Fu Li for their valuable suggestions on this paper. The authors are also grateful to anonymous reviewers for their insightful comments on this paper, which have helped to improve the manuscript.
文摘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.
