Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000. Digital Object Identifier 10.1109/ACCESS.2017.DOI Video Big Data Analytics in the Cloud: A Reference Architecture, Survey, Opportunities, and Open Research Issues AFTAB ALAM, IRFAN ULLAH, AND YOUNG-KOO LEE Department of Computer Science and Engineering, Kyung Hee University (Global Campus), Yongin 1732, South Korea Corresponding author: Young-Koo Lee (e-mail:
[email protected]) This work was supported by the Institute for Information and Communications Technology Promotion Grant through the Korea Government (MSIT) under Grant R7120-17-1007 (SIAT CCTV Cloud Platform). ABSTRACT The proliferation of multimedia devices over the Internet of Things (IoT) generates an unprecedented amount of data. Consequently, the world has stepped into the era of big data. Recently, on the rise of distributed computing technologies, video big data analytics in the cloud has attracted the attention of researchers and practitioners. The current technology and market trends demand an efficient framework for video big data analytics. However, the current work is too limited to provide a complete survey of recent research work on video big data analytics in the cloud, including the management and analysis of a large amount of video data, the challenges, opportunities, and promising research directions. To serve this purpose, we present this study, which conducts a broad overview of the state-of-the-art literature on video big data analytics in the cloud. It also aims to bridge the gap among large-scale video analytics challenges, big data solutions, and cloud computing. In this study, we clarify the basic nomenclatures that govern the video analytics domain and the characteristics of video big data while establishing its relationship with cloud computing.