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

Abstract

This overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.

COVID-19 Pandemic Waves: 4IR Technology Utilisation in Multi-Sector Economy

“…This presents an opportunity to make data available to a large number of persons, making it easy to monitor the movement of persons and determine if preventive measures are followed religiously. The Internet of Things (IoT) and cloud computing are "standard-bearers of the current digitization process" experienced in different sectors such as education, health, agriculture and many more, as they support the "connectivity and management of devices" which are used for gathering data, delivery, processing and computation under several architectural underpinnings [23].…”

“…The core concept of big data is the derivation of alternative and efficient computing means to ingest, retrieve, process and visualize large amounts of data [23]. Big data interventions for COVID-19 pandemic waves have been enormous.…”

Application of Bio and Nature-Inspired Algorithms in Agricultural Engineering

“…Despite the early and dominant influence of BIAs in agriculture, they were disregarded in the following decades; it was not until the last decade that there has been renewed interest in the adoption of BIAs in big data-related applications [12] and commercial agriculture [13][14][15]. The renewed interest in BIAs can be partly attributed to the tremendous surge in the number of neurons, layers, and complexity of the deep learning techniques [8,16]. The review article synthesized the published studies on BIAs, with an emphasis on the four major groups (ecological, swarm-intelligence-based, ecology-based, and multi-objective) and their applications [6,[17][18][19].…”

“…The review article synthesized the published studies on BIAs, with an emphasis on the four major groups (ecological, swarm-intelligence-based, ecology-based, and multi-objective) and their applications [6,[17][18][19]. The review builds on Torre-Bastida et al [16] and Huang's [8] research on applying BIAs innovations suitable for agriculture [8]. The outcomes drawn from the review would positively impact future research and the development of advanced algorithms for agriculture.…”

“…On average, it takes about 1-5 min for algorithms to compute and provide comprehensive information [21]. The timeframe is dependent on the computation system capabilities and the tasks to be executed [16,[22][23][24]. The performance of hybrid algorithms in terms of the computation speed is presented in Fig.…”