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A Grey Theory Based Approach to Big Data Risk Management Using FMEA

机译：基于灰色理论的FMEA大数据风险管理方法

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摘要

Big data is the term used to denote enormous sets of data that differ from other classic databases in four main ways: (huge) volume, (high) velocity, (much greater) variety, and (big) value. In general, data are stored in a distributed fashion and on computing nodes as a result of which big data may be more susceptible to attacks by hackers. This paper presents a risk model for big data, which comprises Failure Mode and Effects Analysis (FMEA) and Grey Theory, more precisely grey relational analysis. This approach has several advantages: it provides a structured approach in order to incorporate the impact of big data risk factors; it facilitates the assessment of risk by breaking down the overall risk to big data; and finally its efficient evaluation criteria can help enterprises reduce the risks associated with big data. In order to illustrate the applicability of our proposal in practice, a numerical example, with realistic data based on expert knowledge, was developed. The numerical example analyzes four dimensions, that is, managing identification and access, registering the device and application, managing the infrastructure, and data governance, and 20 failure modes concerning the vulnerabilities of big data. The results show that the most important aspect of risk to big data relates to data governance.
机译：大数据是用于表示与其他经典数据库在以下四个主要方面不同的海量数据的术语：（巨大）数量，（高）速度，（大得多）种类和（大）价值。通常，数据以分布式方式存储在计算节点上，因此大数据可能更容易受到黑客的攻击。本文提出了一种大数据风险模型，其中包括故障模式和影响分析（FMEA）和灰色理论，更确切地说是灰色关联分析。这种方法具有几个优点：它提供了一种结构化方法，以便合并大数据风险因素的影响；通过将整体风险分解为大数据来促进风险评估；最后，其有效的评估标准可以帮助企业降低与大数据相关的风险。为了说明我们的建议在实践中的适用性，开发了一个基于专家知识的具有实际数据的数值示例。数值示例分析了四个方面，即管理标识和访问，注册设备和应用程序，管理基础结构和数据治理以及20种有关大数据漏洞的故障模式。结果表明，大数据风险的最重要方面与数据治理有关。

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  • 来源
    《Mathematical Problems in Engineering》 |2016年第9期|9175418.1-9175418.15|共15页
  • 作者

    Silva Maisa Mendonca; Poleto Thiago; Camara e Silva Lucio; Henriques de Gusmao Ana Paula; Cabral Seixas Costa Ana Paula;

  • 作者单位

    Univ Fed Pernambuco, Dept Engn Management, Technol Ctr, Rodovia BR 104,Km 62, BR-55002960 Caruaru, PE, Brazil;

    Univ Fed Pernambuco, Dept Engn Management, Sch Engn, Ctr Technol & Geosci, Caixa Postal 5125, BR-52070970 Recife, PE, Brazil;

    Univ Fed Pernambuco, Dept Engn Management, Technol Ctr, Rodovia BR 104,Km 62, BR-55002960 Caruaru, PE, Brazil;

    Univ Fed Pernambuco, Dept Engn Management, Sch Engn, Ctr Technol & Geosci, Caixa Postal 5125, BR-52070970 Recife, PE, Brazil;

    Univ Fed Pernambuco, Dept Engn Management, Sch Engn, Ctr Technol & Geosci, Caixa Postal 5125, BR-52070970 Recife, PE, Brazil;

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