Design and Analysis of Stochastic Query Optimizer for Biobank Databases

The enduring revolution in the field of life sciences is producing biological data at phenomenal rate. The large volume of biological data is meaningful only when it is accessed and analyzed by different researchers. A flood of biological databases is creating a problem in finding an efficient query execution plan for the complex query as posed by bio-researchers. In this research paper, an effort has been made to effectively process the biological data collected in biobanks. The major objective of this research work is to find an optimal query execution plan for biobank database queries using the proposed Restricted Exhaustive Enumeration Approach (REA) and Entropy based Restricted Genetic Approach (ERGA). The result of different query optimization approaches viz. Exhaustive Enumeration, Restricted Exhaustive Enumeration, Simple Genetic Approach and Entropy Based Restricted Genetic Approach are compared with each other on the basis of usage of system resources. The results of Entropy Based Restricted Genetic Algorithm in finding query execution plan for Burbank queries are better than EA and SGAby 2-20% and 7-15% respectively. Furthermore, experimental results reveal that use of Inter-site parallel environment further optimized the results of ERGA by 0-4%.