A data analysis model for business intelligence. (1st January 2003) - Segev, Arie; Shao, Shin-Chung; Zhao, J. Leon
Business intelligence (BI) has become an integral part of e-business activities of companies. The success of such effort is dependent on integration and management of terabytes of transactional data in a large-scale data warehouse to support various analyses. In the last two years, web-oriented BI and outsourcing have led to more challenging problems of integrating these new types of data with the internal transactional data. Furthermore, recent trends in enabling the real-time company have placed new performance demands on BI software. Significant research has been carried out in optimising multidimensional aggregation in data warehousing that serves as the base data for BI analysis software. However, virtually all of this work has focused on either simple aggregates or on specialised analysis such as in human genome and other scientific applications. In this paper, we focus on statistical analysis models and techniques to reduce the volume of base data and enable real-time BI analysis. We introduce a new data aggregation model, referred to as the multivariate and multidimensional aggregated data model (M²AD), for supporting statistical computing in a data warehouse environment.