TY - GEN
T1 - RT-DAP
T2 - 1st IEEE International Conference on Industrial Internet, ICII 2018
AU - Han, Song
AU - Gong, Tao
AU - Nixon, Mark
AU - Rotvold, Eric
AU - Lam, Kam-Yiu
AU - Ramamritham, Krithi
N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).
PY - 2018/10
Y1 - 2018/10
N2 - 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 many manufacturing processes. 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 a way to both improve the performance of the analytic 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, referred to as 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 real-time data flows collected from heterogeneous plant resources, and feedback to the control system and operators in a real-time 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.
AB - 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 many manufacturing processes. 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 a way to both improve the performance of the analytic 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, referred to as 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 real-time data flows collected from heterogeneous plant resources, and feedback to the control system and operators in a real-time 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.
KW - industrial process control
KW - large scale
KW - real time data analytics
UR - http://www.scopus.com/inward/record.url?scp=85059865205&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85059865205&origin=recordpage
U2 - 10.1109/ICII.2018.00015
DO - 10.1109/ICII.2018.00015
M3 - RGC 32 - Refereed conference paper (with host publication)
T3 - Proceedings - 2018 IEEE International Conference on Industrial Internet, ICII 2018
SP - 59
EP - 68
BT - Proceedings - 2018 IEEE International Conference on Industrial Internet, ICII 2018
PB - IEEE
Y2 - 21 October 2018 through 23 October 2018
ER -
