Bio-inspired algorithms for query optimization in biological databases

Abstract

Despite of the numerous research efforts on distributed query processing, the complexity of the problem has been little solved which paves way for the exploration on the solution of the problem. However, due to its inherent difficulty, the complexity of the majority of problems on distributed query optimization remains unknown. In this paper, we analyze and present the problems identified and the possible algorithms used for solving them. The higher probability of problem solving nature of the Bio-inspired Algorithms has been projected with a Comparative Study on literature proof and as a result a proposal for Query Optimization on Biological Databases is introduced.

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

“…The benefits resulting from this synergistic relationship are exposed by new Big Data infrastructures, tools and technologies that have adopted bio-inspired algorithms to reach a higher level of efficiency in their tasks. Some few examples of technologies that take advantage of the capabilities of bio-inspired algorithms are, among many others, NoSQL databases [13][14][15], load planners/schedulers [16], or tools assisting analytical tasks such as feature selection [17], dimensionality reduction [18] or data fusion [19]. On the other hand, through bioinspired computation perspective, Big Data provides the possibility of great volumes and varieties of data and the efficient implementation of solvers through new technologies, which offer parallel, distributable and scalable workloads.…”