A survey on Big Data Analytics

Abstract

The concept of electrocardiogram is explained. Then, a problem statement based on manufacturing scenario is
presented. Subsequently, the architecture of proposed algorithm called integrated deep denoising auto-encoder (IDDA)
and algorithm workflow are provided. Moreover, DECG is compared with traditional factory information system, and the
feasibility and effectiveness of proposed algorithm are validated experimentally. The proposed concept and algorithm
combine typical industrial scenario and advance artificial intelligence, which has great potential to accelerate the
implementation. In the context of Industry 4.0, industrial robotics such as automated guided vehicles have drawn
increased attention due to their automation capabilities and low cost. With the support of cognitive technologies for
industrial Internet of Things (IoT), production processes can be significantly optimized and more intelligent
manufacturing can be implemented for smart factories. The explosive growth in the number of devices connected to
the Internet of Things (IoT) and the exponential increase in data consumption only reflect how the growth of big
data perfectly overlaps with that of IoT. The management of big data in a continuously expanding network gives rise to
non-trivial concerns regarding data collection efficiency, data processing, analytics, and security. The advances in
wireless communication technologies, vehicular networks and cloud computing boost a growing interest in the design,
development and deployment of Vehicular Cyber-Physical Systems (VCPS) for some emerging applications, which leads
to an increasing demand on connecting Mobile Cloud Computing (MCC) users to VCPS for accessing the richer
applications and services. Fault diagnosis is an important topic both in practice and research. There is intense pressure on
industrial systems to continue reducing unscheduled downtime, performance degradation, and safety hazards, which
requires detecting and recovering from potential faults as early as possible.