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An overview on the roles of fuzzy set techniques in big data processing: Trends, challenges and opportunities

机译：模糊集技术在大数据处理中的作用概述：趋势，挑战和机遇

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摘要

In the era of big data, we are facing with an immense volume and high velocity of data with complex structures. Data can be produced by online and offline transactions, social networks, sensors and through our daily life activities. A proper processing of big data can result in informative, intelligent and relevant decision making completed in various areas, such as medical and healthcare, business, management and government. To handle big data more efficiently, new research paradigm has been engaged but the ways of thinking about big data call for further long-term innovative pursuits. Fuzzy sets have been employed for big data processing due to their abilities to represent and quantify aspects of uncertainty. Several innovative approaches within the framework of Granular Computing have been proposed. To summarize the current contributions and present an outlook of further developments, this overview addresses three aspects: (1) We review the recent studies from two distinct views. The first point of view focuses on What types of fuzzy set techniques have been adopted. It identifies clear trends as to the usage of fuzzy sets in big data processing. Another viewpoint focuses on the explanation of the benefits of fuzzy sets in big data problems. We analyze when and why fuzzy sets work in these problems. (2) We present a critical review of the existing problems and discuss the current challenges of big data, which could be potentially and partially solved in the framework of fuzzy sets. (3) Based on some principles, we infer the possible trends of using fuzzy sets in big data processing. We stress that some more sophisticated augmentations of fuzzy sets and their integrations with other tools could offer a novel promising processing environment. (C) 2016 Elsevier B.V. All rights reserved.
机译：在大数据时代，我们面临着庞大的数据量和高速度的复杂结构数据。数据可以通过在线和离线交易，社交网络，传感器以及我们的日常生活活动产生。正确处理大数据可以在医疗，保健，商业，管理和政府等各个领域完成信息丰富，智能且相关的决策。为了更有效地处理大数据，已经采用了新的研究范式，但是思考大数据的方法要求进一步的长期创新追求。由于模糊集具有表示和量化不确定性方面的能力，因此已将模糊集用于大数据处理。已经提出了粒度计算框架内的几种创新方法。为了总结当前的贡献并提出进一步的发展前景，本概述从三个方面着手：（1）我们从两种不同的观点回顾了最近的研究。第一种观点集中在采用了哪种类型的模糊集技术上。它确定了模糊集在大数据处理中的使用趋势。另一个观点集中在解释模糊集在大数据问题中的好处。我们分析模糊集何时以及为什么在这些问题中起作用。 （2）我们对现有问题进行了批判性审查，并讨论了大数据当前面临的挑战，这些挑战可以在模糊集的框架中潜在地和部分地解决。 （3）基于一些原理，我们推断在大数据处理中使用模糊集的可能趋势。我们强调，模糊集的一些更复杂的扩充及其与其他工具的集成可以提供一种新颖的有希望的处理环境。 （C）2016 Elsevier B.V.保留所有权利。

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  • 来源
    《Knowledge-Based Systems》 |2017年第15期|15-30|共16页
  • 作者

    Wang Hai; Xu Zeshui; Pedrycz Witold;

  • 作者单位

    Southeast Univ, Sch Econ & Management, Nanjing 211189, Jiangsu, Peoples R China;

    Southeast Univ, Sch Econ & Management, Nanjing 211189, Jiangsu, Peoples R China|Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Jiangsu, Peoples R China;

    Univ Alberta, Dept Elect & Comp Engn, Edmonton, AB T6R 2V4, Canada|King Abdulaziz Univ, Dept Elect & Comp Engn, Fac Engn, Jeddah 21589, Saudi Arabia|Polish Acad Sci, Syst Res Inst, Warsaw, Poland;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Big data; Data-intensive science; Fuzzy sets; Fuzzy logic; Granular computing;

    机译：大数据;数据密集型科学;模糊集;模糊逻辑;粒度计算;

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