A Component-Based Architecture for Preparing Data in Data Warehousing
Won Bae Kim, Do-Heon Lee, Kyung Chang Kim, Eui Kyeong Hong · Joop-journal of Object-oriented Programming · 2000
A data warehouse (DW) stores data from various data sources to efficiently implement decision support or OLAP queries. Selecting data to load into a DW is one of the most important decisions in designing a DW. Most of the DW research activities in the last few years has been to select data in data sources that would provide the best performance for a given query pattern or workload. However, it is difficult to predict a query pattern or a workload due to the dynamic nature of decision support analysis. In this article we describe a component-based architecture for determining and preparing the source data to load into a DW. The architecture consists of 12 components that extract metadata from data sources, capture business rules, cleanse and profile source data, and map the source schemas to business rules. A methodology is presented that utilizes the components in correct order to select data in data sources to load into a DW. Third party tools used to carry out the tasks of some of the components are also discussed.