Database Querying Optimization via Genetic Algorithm for Biomedical Research

Thanks to the skyscraping development of hardware and software technologies, the data solutions have become an urgent trend to deal with vast amount of data, especially in biomedical research, human genome and healthcare systems. The healthcare research has always demanded close association with biomedical data to produce personalized medicine and deliver suitable cure and treatments. Nevertheless, coping with huge amount of information from biomedical data requires bulky solutions. In the light of data science, the solution for this issue can change from a theoretical approach to a data-driven approach. Database stores a huge amount of information and particular sets of data can be accessed via queries which are written in specific interface language. In order to manage this amount of data, database optimization is implemented to maximize the speed and efficiency with data retrieval or reduce database system response time. Query optimization is one of the major functionalities in database management systems. The purpose of the query optimization is to determine the most efficient and effective way to execute a particular query by considering several query plans. In this article, genetic algorithm (GA) strategy is utilized for biomedical database systems to execute the query plan. Genetic algorithms are extensively using to solve constrained and unconstrained optimization problems. Based on three main types of rules of GA such as selection, crossover and mutation, the querying can be optimized for solving database problem.