A Schema Transformation Technique for Temporal Data Warehouses

V.T.N. Chau and S. Chittayasothorn (Thailand)


Temporal Data Warehouse, Structure Version, Transformation Function, Schema Transformation, Clustering


In recent years, temporal data warehousing has become one of the popular topics in the database area. The nature of data warehouse is related to time along with data spanning over periods of time. It is possible that during a long time interval, the underlying structure of a data warehouse may be changed to conform itself to the latest environmental conditions. If different structures of a temporal data warehouse are allowed to be seen by users, it is a challenge to present the time varying data that comply with the particular schema chosen by the users. Making the most of a well known field - data mining, this paper focuses on the development of schema transformation in temporal data warehouses using probability together with the K-means clustering method. The result from our technique is found to be more accurate than one from existing techniques.

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