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Socio-cyber network: The potential of cyber-physical system to define human behaviors using big data analytics

机译：社会网络：网络物理系统使用大数据分析定义人类行为的潜力

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The growing gap between users and the big data analytics requires innovative tools that address the challenges faced by big data volume, variety, and velocity. Therefore, it becomes computationally inefficient to analyze such massive volume of data. Moreover, advancements in the field of big data application and data science leads toward a new paradigm of human behavior, where various smart devices integrate with each other and establish a relationship. However, majority of the systems are either memoryless or computational inefficient, which are unable to define or predict human behavior. Therefore, keeping in view the aforementioned needs, there is a requirement for a system that can efficiently analyze a stream of big data within their requirements. Hence, this paper presents a system architecture that integrates social network with the technical network. We derive a novel notion of‘Socio-Cyber Network’,where a friendship is made based on the geo-location information of the user, where trust index is used based on graphs theory. The proposed graph theory provides a better understanding of extraction knowledge from the data and finding relationship between different users. To check the efficiency of the proposed algorithms exploited in the proposed system architecture, we have implemented our proposed system using Hadoop and MapReduce. MapReduce for cyber-physical system (CPS) is supported by a parallel algorithm that efficiently process a huge volume of data sets. The system is implemented using Spark GraphX tool at the top of the Hadoop parallel nodes to generate and process graphs with near real-time. Moreover, the system is evaluated in terms of efficiency by considering the system throughput and processing time. The results show that the proposed system is more scalable and efficient.
机译：用户和大数据分析之间的差距不断扩大，需要创新的工具来应对大数据量，种类和速度所面临的挑战。因此，分析如此大量的数据在计算上效率低下。此外，大数据应用和数据科学领域的进步导致了人类行为的新范例，其中各种智能设备相互集成并建立了联系。但是，大多数系统要么无记忆，要么计算效率低下，无法定义或预测人类行为。因此，考虑到前述需求，需要一种能够在其需求内有效地分析大数据流的系统。因此，本文提出了一种将社交网络与技术网络相集成的系统架构。我们推导了一个新颖的“社会网络网络”概念，该概念是根据用户的地理位置信息建立友谊的，而信任关系则是基于图论的。提出的图论可以更好地理解从数据中提取知识并发现不同用户之间的关系。为了检查在所提出的系统体系结构中利用的所提出算法的效率，我们使用Hadoop和MapReduce实现了所提出的系统。并行算法支持用于网络物理系统（CPS）的MapReduce，该算法可有效处理大量数据集。该系统使用Hadoop并行节点顶部的Spark GraphX工具实现，以近乎实时地生成和处理图形。此外，通过考虑系统吞吐量和处理时间来评估系统的效率。结果表明，所提出的系统具有更高的可扩展性和效率。

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