Torre-Bastida Ana I, Díaz-de-Arcaya Josu, Osaba Eneko, Muhammad Khan, Camacho David, Del Ser Javier
TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio, Spain.
Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab), Department of Software, Sejong University, Seoul, 143-747 Republic of Korea.
Neural Comput Appl. 2021 Aug 3:1-31. doi: 10.1007/s00521-021-06332-9.
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.
本综述聚焦于大数据技术与生物启发式计算相结合所产生的最新研究成果。这两种范式之间能产生有益协同效应的原因有很多，其根源都在于生物启发式原理能够为旨在高效管理、检索、融合和处理大数据的技术提供适应性、智能性和稳健性。我们首先深入分析现有文献，重点关注过去几年所报道的进展，以此深入研究这一领域。对现有文献的分析辅以对大数据中尚未解决的新趋势和开放性挑战的识别，而生物启发式算法能够有效应对这些挑战。作为第二项贡献，本文阐述了生物启发式算法如何需要进行调整以用于大数据环境，在这种环境中，数据融合作为允许处理和挖掘多个且可能异构数据源的前置步骤变得至关重要。这种分析有助于探索和比较不同问题和领域中现有方法的范围和效率，目的是识别新的潜在应用和研究领域。最后，本综述突出了这一研究方向中至今仍未解决的开放性问题，并给出了未来研究的建议。
