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Cross-Sectorial Semantic Model for Support of Data Analytics in Process Industries

Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Technical University Kosice, Letna 9, 040 01 Kosice, Slovakia
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Processes 2019, 7(5), 281; https://doi.org/10.3390/pr7050281
Received: 21 March 2019 / Revised: 23 April 2019 / Accepted: 6 May 2019 / Published: 13 May 2019
(This article belongs to the Section Advanced Digital and Other Processes)
The process industries rely on various software systems and use a wide range of technologies. Predictive modeling techniques are often applied to data obtained from these systems to build the predictive functions used to optimize the production processes. Therefore, there is a need to provide a proper representation of knowledge and data and to improve the communication between the data scientists who develop the predictive functions and domain experts who possess the expert knowledge of the domain. This can be achieved by developing a semantic model that focuses on cross-sectorial aspects rather than concepts for specific industries, and that specifies the meta-classes for the formal description of these specific concepts. This model should cover the most important areas including modeling the production processes, data analysis methods, and evaluation using the performance indicators. In this paper, our primary objective was to introduce the specifications of the Cross-sectorial domain model and to present a set of tools that support data analysts and domain experts in the creation of process models and predictive functions. The model and the tools were used to design a knowledge base that could support the development of predictive functions in the green anode production in the aluminum production domain. View Full-Text
Keywords: semantic model; data analytics; semantic annotation; process industry semantic model; data analytics; semantic annotation; process industry
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Sarnovsky, M.; Bednar, P.; Smatana, M. Cross-Sectorial Semantic Model for Support of Data Analytics in Process Industries. Processes 2019, 7, 281.

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