Research on Materialized Views Technology in Data Warehouse

• Published in: 2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop pp. 1030 - 1035
• Main Authors: Lijuan Zhou, Min Xu, Qian Shi, Zhongxiao Hao
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2008
• Subjects: algorithm; Computer science; Cost function; Data engineering; data warehouse; Data warehouses; Delay; Educational institutions; Material storage; materialized view; Materials science and technology; Space technology
• ISBN: 1424435307; 9781424435302
• DOI: 10.1109/KAMW.2008.4810668

With the needs of decision-support information of enterprise and the fast development of computer technologies data warehouse technology come out. The data warehouse is a repository of information collected from multiple, possibly heterogeneous, autonomous, distributed databases. The information stored at the data warehouse is in form of views referred to as materialized views. The design of data warehouse is one of the core research problems in studying and evolution of data warehouse. One of the most important decisions in design of data warehouse is the data warehouse selection. Selecting views to materialize impacts on the efficiency as well as the total cost of establishing and running a data warehouse. So, we develop algorithms to select a set of views to materialize in data warehouse in order to minimize the total view maintenance cost under the constraint of a given query response time. We call it query cost view selection problem (QC_VSP). In this paper, First, we propose query cost view selection problem model. Second, we give three algorithms for QC_VSP; we give view_node_matrix in order to solve it. Third, experiment simulation is adopted. The results show that our algorithm works better in practical cases. We implemented our algorithms and a performance study of the algorithms shows that the proposed algorithm delivers an optimal solution. Finally, we discuss the observed behavior of the algorithms. We also identify some important issues for future investigations.