Efficient Aggregation Algorithms for Compressed Data Warehouses
IEEE Transactions on Knowledge and Data Engineering
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
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Data Warehousing and On-Line Analytical Processing are essential elements of knowledge worker (executive, manager, and analyst) to make better and faster decisions. Therefore, it becomes a focus of the database industry. There are many application areas of data warehousing technologies, such as manufacturing, financial services, telecommunication and healthcare. The main objective of our work is to use and understand the data warehousing and OLAP technologies. First this paper proposes a system for data warehouse. Then the detailed of ETL processes are followed. The data extracted from different sources are stored in data staging area, where the data is cleaned and transformed using cube aggregation. The cleaned data from staging area are loaded and organized as Matriculation Warehouse for the direct querying for the end users. Specifically, this paper emphasizes on how to conduct the ETL process with the use of concept hierarchy-based cube aggregation. The prototyped system is implemented based on Basic Education High School (B.E.H.S) student data for accessing the speed up OLAP operations because of cube aggregation.