ABSTRACT
Cyber-Physical Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .152 6.6 Case Study: Big Data Driven Vehicular Cyber-Physical Systems Design . . . . . . . . . . . . . . . . . . .154 6.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .164 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .164 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .165
Big data as a phase have been among the biggest trends of the four years, giving rise to an upsurge of research and development, as well as industry and government applications. Data are deemed a powerful and useful raw material that can impact multidisciplinary research endeavors as well as government, industry, and business performance. Everyone is talking about big data, and it is believed that science, business, industry, government, society, and so forth will undergo a thorough change with the influence of big data [1]. In fact, we now live in the era of big data as huge and complex data sets, which are being produced and collected for diverse purposes through all kinds of technologies or approaches including mobile devices, remote sensing technologies, software logs, wireless sensor networks, and social media. The big data sets tend to be more unstructured, distributed, and complex than ever before [2]. Thus, big data are defined as huge amount of data that require new technologies and architectures so that it becomes possible to extract value from them by
capturing and analysis process. Due to such large size of data, it becomes very difficult to perform effective analysis using the existing traditional techniques. Big data due to their various properties like volume, velocity, variety, variability, value, and complexity put forward many challenges [3]. Under the great increase in global data, the term big data is constantly used to describe huge data sets. Compared with traditional data sets, big data typically compose of masses of unstructured data, sensor data, and stream data that need more real-time processing. Big data are characterized by three aspects: (1) data are numerous, (2) data cannot be categorized into regular relational databases, and (3) data are generated, captured, and processed rapidly. Moreover, big data are transforming health care, transportation, aerospace, science, engineering, finance, business, and eventually, the society [4]. In addition, big data also give rise to new opportunities for discovering new values, make us gain an in-depth understanding of the hidden values, and also bring new challenges, for example, how to effectively specify, model, capture, transfer, organize, and manage such data sets [5]. Big data aim to handle high-volume, high-velocity, high-variety data to extract intended data value and ensure high veracity of original data and obtained information that demand cost-effective, innovative forms of data and information processing for enhanced insight, decision making, and process control; all of these demand new data models and new infrastructure services and tools that allow also obtaining from a variety of sources and delivering data in a variety of forms to different data and information consumers and devices [6].
