Query answering-based view selection
T. V. Vijay Kumar, Mohammad Rafiqul Haider · International Journal of Business Information Systems · 2015
A data warehouse is designed for answering decision making queries. These queries are usually long, complex and exploratory in nature and involve aggregates over a large number of dimensions. As a result, the processing time for such queries, against a continuously growing data warehouse, is high. This problem can be addressed by materialising views over a data warehouse. This paper presents a query answering view selection algorithm (QAVSA) that considers the size and query answering capability of views to select the top-K views for materialisation from a multi-dimensional lattice. The views selected using QAVSA are likely to be beneficial both with respect to their size and their ability to answer decision making queries. Further, experimental results show that QAVSA, in comparison to the well known greedy algorithm HRUA, is able to efficiently select views that can provide answers to greater number of queries. This in turn would facilitate decision making.