Abstract

Data warehousing is the foundation of DSS (decision support system), of which the goal is to enable decision makers to make better business decisions based on analysis of historic data related to the business operation. Data warehousing has become a major business trend, both for product and service sectors and for application to daily business in all industries. In this thesis, we compare and discuss the two data models and two methods of OLAP (MOLAP and ROLAP). The two data models and two OLAPs are not mutually exclusive in any project. An example of the hybrid design is presented in a case study. In this project, we are able to apply two data models at different stages of the data warehouse. By combining ER model, Star Schema and aggregate table in one architecture, we are able to maximize on both storage capacity and query performance. In addition to the selection of data models, the choice between MOLAP and ROLAP is discussed. When the objectives of the client are clearly defined, the functional requirements of the data warehouse are determined, and the amount of data is relatively small, MOLAP would likely be a better choice because of its superior performance and intuitive querying mechanism. ROLAP becomes a more suitable candidate for OLAP when the amount of data is exceedingly large, and the client does not want to leave out anything. Again, it is possible, even desirable, for both OLAP methods to be applied in one project to compliment each other.