Materialized View Selection using Improvement based Bee Colony Optimization

In the present information age, data and information are vital not just for the survival of any corporate entity, but also to provide it with an edge over its competitors. Data warehouses have become the foundational databases of almost every corporation. However, extracting new information from these data warehouses takes hours, and even days, which is practically unacceptable. Materialized views have been popularly used to facilitate fast information extraction. However, the selection of appropriate views, which significantly accelerate information synthesis is an NP-Complete problem. The aim of this paper is to select near optimal sets of views for materialization using the improvement bee colony optimization algorithm. The experimental results indicate that the improvement bee colony optimization algorithm performs better than the constructive bee colony optimization algorithm and the fundamental view selection algorithm HRUA. The views thus selected would significantly minimize the response time of analytical queries, when materialized, resulting in efficient strategic decision making.

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