Big data analytics for security and criminal investigations

Applications of various data analytics technologies to security and criminal investigation during the past three decades have demonstrated the inception, growth, and maturation of criminal analytics. We first identify five cutting-edge data mining technologies such as link analysis, intelligent agents, text mining, neural networks, and machine learning. Then, we explore their recent applications to the criminal analytics domain, and discuss the challenges arising from these innovative applications. We also extend our study to big data analytics which provides some state-of-the-art technologies to reshape criminal investigations. In this paper, we review the recent literature, and examine the potentials of big data analytics for security intelligence under a criminal analytics framework. We examine some common data sources, analytics methods, and applications related to two important aspects of social network analysis namely, structural analysis and positional analysis that lay the foundation of criminal analytics. Another contribution of this paper is that we also advocate a novel criminal analytics methodology that is underpinned by big data analytics. We discuss the merits and challenges of applying big data analytics to the criminal analytics domain. Finally, we highlight the future research directions of big data analytics enhanced criminal investigations.

Author(s): M.I. Pramanik, Raymond Y.K. Lau, Wei T. Yue, Yunming Ye, Chunping Li
Organization(s): City University of Hong Kong, Shenzhen Graduate School/Harbin Institute of Technology, Tsinghua University
Source: WIREs Data Mining Knowledge Discovery
Year: 2017

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