Chapter 1 - Bio-Inspired Algorithms for Big Data Analytics: A Survey, Taxonomy, and Open Challenges

Abstract

Presently, various governments and organizations are focusing on digitization of technical and academic documents, which overloads digital libraries. It is challenging to manage a massive amount of data (big data) with the current data processing techniques. In literature, bio-inspired algorithm-based models and architectures have been introduced by numerous industry and academic institutions to facilitate data analytics for big data. This chapter presents a systematic review of bio-inspired algorithms for big data analytics. The current status of bio-inspired algorithms is categorized into three categories: ecological, swarm-based, and evolutionary. This chapter compares the existing models and architectures, explores the current trends, and recognizes the existing open issues in the development of big data analytical techniques. This research work will also help to choose the most appropriate bio-inspired algorithm for big data analytics in a specific type of data along with promising directions for future research.