An Application of an Intelligent Data Warehouse for Modelling Spatiotemporal Objects

Abstract

Data warehouse (DW) systems provide the best solution for intelligent data analysis and decision-making. Changes applied to data gradually in real life have to be projected to the DW. Slowly changing dimension (SCD) refers to the potential volatility of DW dimension members. The treatment of SCDs has a significant impact over the quality of data analysis. A new SCD type, Type N, is proposed in this research paper, which encapsulates volatile data into historical clusters. Type N preserves complete history of changes, additional tables, columns, and rows are not required, extra join operations are omitted, and surrogate keys are avoided. Type N is implemented and compared to other SCD types. Good candidates for practicing SCDs are spatiotemporal objects (i.e., objects whose shape or geometry evolves slowly over time). The case study used and implemented in this paper concerns shape-shifting constructions (i.e., buildings that respond to changing weather conditions or the way people use them). The results demonstrate the correctness and effectiveness of the proposed SCD Type N.