An Application of an Intelligent Data Warehouse for Modelling Spatiotemporal Objects

An Application of an Intelligent Data Warehouse for Modelling Spatiotemporal Objects

Georgia Garani, Nunziato Cassavia, Ilias K. Savvas
Copyright: © 2020 |Pages: 22
DOI: 10.4018/IJBDIA.2020010103
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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.
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Background

Kimball initially proposed three types for solving the problem of SCDs, Type 1, Type 2 and Type 3 (Kimball, 1996). The principal idea was to support changes in dimensions by maintaining the referential integrity between facts and dimension members. New types were introduced later for improving the previous original types by combining some of their features (Kimball & Ross, 2013). On the contrary, fast changing dimensions are dimensions which keep changing rapidly. Support for fast changing dimensions by combing different SCD types and using mini-dimensions is also given in Kimball & Ross (2010).

A new methodology of managing SCDs is proposed in Griffin et al. (2005). A dimension template is provided for each dimension table for maintaining and using a table appropriately. This is based on assigning behavior to the dimension attributes, which are distinguished into seven different behavior types, surrogate key, business key, effective date, end date, last update date, current indicator and normal according to their approach.

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