A Transformation-oriented Methodology to Knowledge-based Conceptual Data Warehouse Design

Abstract

Applications of artificial intelligence (AI) technology in the form of knowledge-based systems within the context of database design have been extensively researched particularly to provide support within the conceptual design phase. However, a similar approach to the task of data warehouse design has yet to be seriously initiated. In this paper, we proposed a design methodology for conceptual data warehouse design called the transformation-oriented methodology, which transforms an Entity-Relationship (ER) model into a multidimensional model based on a series of transformation and analysis rules. The transformation-oriented methodology translates the ER model into a specification language model and transformed it into an initial problem domain model. A set of synthesis and diagnosis rules will then gradually transform the problem domain model into the multidimensional model. A prototype KB tool called the DWDesigner has been developed to implement the aforementioned methodology. The multidimensional model produces by the DWDesigner as output is presented in a graphical form for better visualization. Testing has been conducted to a number of design problems, such as university, business and hospital domains and consistent results have been achieved

Fuzzy-Rule Based Adaptive Data Warehouse

“…However, a similar approach to the task of data warehouse design has yet to be seriously initiated. A design methodology is proposed by Sitompul and Noah (2006) for conceptual data warehouse design called the transformation-oriented methodology, which transforms an Entity-Relationship (ER) model into a multidimensional model based on a series of transformation and analysis rules.…”