Materialized view selection using HBMO

Strategic business decision making has become far more complex, challenging and consequential in today’s modern and highly competitive economy. So, managers have been using decision support systems to assist them in making accurate, efficient and effective decisions. These systems takes hours and days to process massive data sets in order to find relevant information for answering analytical queries. As a result the query response times are high. This response time can be reduced substantially by selecting and materializing pre-computed views that can provide answers to analytical queries. In this paper, an attempt has been made to select optimal sets of views, which would significantly reduce response time of analytical queries. In this regard, honey bee mating optimization based view selection algorithm (HBMOVSA) is proposed that selects Top-K views, from amongst all possible views, in a multidimensional lattice. Experimental results show that HBMOVSA is able to select comparatively better quality of views when compared with those selected by the most fundamental view selection algorithm HRUA.

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