TY - GEN
T1 - An OPC UA-based industrial Big Data architecture
AU - Hirsch, E.
AU - Hoher, S.
AU - Huber, S.
N1 - Conference code: 192026
Export Date: 14 December 2023
Correspondence Address: Hirsch, E.; Salzburg University of Applied Sciences, Austria; email: [email protected]
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Springer, 202, Seville, Spain, May 13-15; Abdulsalam, Y.S., Hedabou, M., Security and privacy in cloud computing: Technical review (2022) Future Internet, 14 (1), p. 11; Hou, T., Wang, V., Industrial espionage-a systematic literature review (slr) (2020) computers & security, 98, p. 102019; Ettredge, M., Guo, F., Li, Y., Trade secrets and cyber security breaches (2018) Journal of Accounting and Public Policy, 37 (6), pp. 564-585; Bali, M.S., Khurana, S., Effect of latency on network and end user domains in cloud computing (2013) 2013 International Conference on Green Computing, Communication and Conservation of Energy (ICGCE). IEEE, pp. 777-782; Laney, D., (2001) 3D data management: Controlling data volume, velocity, and variety, , http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3DData-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf, META Group, Tech. 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Springer, 2023, Las Palmas de Gran Canaria, Spain, February 20-25
PY - 2023
Y1 - 2023
N2 - Industry 4.0 factories are complex and data-driven. Data is yielded from many sources, including sensors, PLCs, and other devices, but also from IT, like ERP or CRM systems. We ask how to collect and process this data in a way, such that it includes metadata and can be used for industrial analytics or to derive intelligent support systems. This paper describes a new, query model based approach, which uses a big data architecture to capture data from various sources using OPC UA as a foundation. It buffers and preprocesses the information for the purpose of harmonizing and providing a holistic state space of a factory, as well as mappings to the current state of a production site. That information can be made available to multiple processing sinks, decoupled from the data sources, which enables them to work with the information without interfering with devices of the production, disturbing the network devices they are working in, or influencing the production process negatively. Metadata and connected semantic information is kept throughout the process, allowing to feed algorithms with meaningful data, so that it can be accessed in its entirety to perform time series analysis, machine learning or similar evaluations as well as replaying the data from the buffer for repeatable simulations. © 2023 IEEE.
AB - Industry 4.0 factories are complex and data-driven. Data is yielded from many sources, including sensors, PLCs, and other devices, but also from IT, like ERP or CRM systems. We ask how to collect and process this data in a way, such that it includes metadata and can be used for industrial analytics or to derive intelligent support systems. This paper describes a new, query model based approach, which uses a big data architecture to capture data from various sources using OPC UA as a foundation. It buffers and preprocesses the information for the purpose of harmonizing and providing a holistic state space of a factory, as well as mappings to the current state of a production site. That information can be made available to multiple processing sinks, decoupled from the data sources, which enables them to work with the information without interfering with devices of the production, disturbing the network devices they are working in, or influencing the production process negatively. Metadata and connected semantic information is kept throughout the process, allowing to feed algorithms with meaningful data, so that it can be accessed in its entirety to perform time series analysis, machine learning or similar evaluations as well as replaying the data from the buffer for repeatable simulations. © 2023 IEEE.
KW - big data
KW - data retrieval
KW - device decoupling
KW - information model
KW - IT/OT integration
KW - OPC UA
KW - query model
KW - Machine learning
KW - Metadata
KW - Network architecture
KW - Search engines
KW - Semantics
KW - Time series analysis
KW - Data architectures
KW - Data driven
KW - Data retrieval
KW - Decouplings
KW - Device decoupling
KW - ERP system
KW - Information Modeling
KW - Query model
KW - Big data
U2 - 10.1109/INDIN51400.2023.10217899
DO - 10.1109/INDIN51400.2023.10217899
M3 - Conference contribution
SN - 978-1-6654-9314-7
BT - 2023 IEEE 21st International Conference on Industrial Informatics (INDIN)
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 21st IEEE International Conference on Industrial Informatics, INDIN 2023
Y2 - 18 July 2023 through 20 July 2023
ER -