An Adaptive Approach to Schema Classification for Data Warehouse Modeling

Data warehouse (DW) modeling is a complicated task,involving both knowledge of business processes and familiarity withoperational information systems structure and behavior. Existing DWmodeling techniques suffer from the following majordrawbacks --- data-driven approach requires high levels of expertise andneglects the requirements of end users, while demand-driven approachlacks enterprise-wide vision and is regardless of existing models ofunderlying operational systems. In order to make up for thoseshortcomings, a method of classification of schema elements for DWmodeling is proposed in this paper. We first put forward the vectorspace models for subjects and schema elements, then present an adaptiveapproach with self-tuning theory to construct context vectors ofsubjects, and finally classify the source schema elements intodifferent subjects of the DW automatically. Benefited from the resultof the schema elements classification, designers can model andconstruct a DW more easily.