  In most process control systems nowadays, process measurements are
periodically collected and archived in historians. Analytics applications
process the data, and provide results offline or in a time period that is
considerably slow in comparison to the performance of the manufacturing
process. Along with the proliferation of Internet-of-Things (IoT) and the
introduction of "pervasive sensors" technology in process industries,
increasing number of sensors and actuators are installed in process plants for
pervasive sensing and control, and the volume of produced process data is
growing exponentially. To digest these data and meet the ever-growing
requirements to increase production efficiency and improve product quality,
there needs to be a way to both improve the performance of the analytics system
and scale the system to closely monitor a much larger set of plant resources.
In this paper, we present a real-time data analytics platform, called RT-DAP,
to support large-scale continuous data analytics in process industries. RT-DAP
is designed to be able to stream, store, process and visualize a large volume
of realtime data flows collected from heterogeneous plant resources, and
feedback to the control system and operators in a realtime manner. A prototype
of the platform is implemented on Microsoft Azure. Our extensive experiments
validate the design methodologies of RT-DAP and demonstrate its efficiency in
both component and system levels.
