Abstract
Industry 4.0 initiatives have fostered the definition of different standards, e.g., AutomationML or OPC UA, allowing for the specification of industrial objects and for machine-to-machine communication in Smart Factories. Albeit facilitating interoperability at different steps of the production life-cycle, the information models generated from these standards are not semantically defined, making the semantic data integration a challenging problem. We tackle the problems of integrating data from documents specified either using the same or different Industry 4.0 standards, and propose a rule-based framework that combines deductive databases and Semantic Web technologies to effectively solve these problems. As a proof-of-concept, we have developed a Datalog-based representation for AutomationML documents, and a set of rules for identifying semantic heterogeneity problems among these documents. We have empirically evaluated our proposed framework against several benchmarks and the initial results suggest that exploiting deductive and Semantic Web techniques allows for increasing scalability, efficiency, and coherence of models for Industry 4.0 manufacturing environments.
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References
Biffl, S., Kovalenko, O., Lüder, A., Schmidt, N., Rosendahl, R.: Semantic mapping support in AutomationML. In: ETFA, pp. 1–4. IEEE (2014)
Ceri, S., Gottlob, G., Tanca, L.: What you always wanted to know about datalog (and never dared to ask). IEEE Trans. Knowl. Data Eng. 1(1), 146–166 (1989)
eClass e.V.: eCl@ss standardized material and service classification (2016)
Enste, U., Mahnke, W.: OPC unified architecture. Automatisierungstechnik 59(7), 397–405 (2011)
e.V. AutomationML, OPC Foundation: OPC UA information model for AutomationML. Status report (2016)
Grangel-González, I., Collarana, D., Halilaj, L., Lohmann, S., Lange, C., Vidal, M.-E., Auer, S.: Alligator: a deductive approach for the integration of Industry 4.0 standards. In: Blomqvist, E., Ciancarini, P., Poggi, F., Vitali, F. (eds.) EKAW 2016. LNCS (LNAI), vol. 10024, pp. 272–287. Springer, Cham (2016). doi:10.1007/978-3-319-49004-5_18
Henßen, R., Schleipen, M.: Interoperability between OPC UA and AutomationML. In: Procedia CIRP 8th International Conference on Digital Enterprise Technology DET, vol. 25 (2014)
Himmler, F.: Function based engineering with automationml - towards better standardization and seamless process integration in plant engineering. In: 12th International Conference on Tagung Wirtschaftsinformatik, WI (2015)
Kovalenko, O., Euzenat, J.: Semantic matching of engineering data structures. In: Biffl, S., Sabou, M. (eds.) Semantic Web for Intelligent Engineering Applications. Springer, Cham (2016)
Kovalenko, O., Wimmer, M., Sabou, M., Lüder, A., Ekaputra, F.J., Biffl, S.: Modeling AutomationML: semantic web technologies vs. model-driven engineering. In: 20th IEEE Conference on Emerging Technologies & Factory Automation, ETFA, pp. 1–4 (2015)
Lange, C.: Krextor - an extensible XML\(\rightarrow \)RDF extraction framework. In: Scripting and Development for the Semantic Web, SFSW, vol. 449. CEUR Workshop Proceedings, Aachen, May 2009
Panetto, H., Zdravkovic, M., Jardim-Gonçalves, R., Romero, D., Cecil, J., Mezgár, I.: New perspectives for the future interoperable enterprise systems. Comput. Ind. 79, 47–63 (2016)
Persson, J., Gallois, A., Björkelund, A., Hafdell, L., Haage, M., Malec, J., Nilsson, K., Nugues, P.: A knowledge integration framework for robotics. In: 41st International Symposium on Robotics and ROBOTIK 2010 (2010)
Sabou, M., Ekaputra, F., Kovalenko, O., Biffl, S.: Supporting the engineering of cyber-physical production systems with the AutomationML analyzer. In: 1st International Workshop on Cyber-Physical Production Systems, CPPS, pp. 1–8. IEEE (2016)
Schleipen, M., Damm, M., Henßen, R., Lüder, A., Schmidt, N., Sauer, O., Hoppe, S.: OPC UA and AutomationML-collaboration partners for one common goal: Industry 4.0. (2014)
Schleipen, M., Gutting, D., Sauerwein, F.: Domain dependant matching of MES knowledge and domain independent mapping of AutomationML models. In: 2012 IEEE 17th Conference on Emerging Technologies & Factory Automation, ETFA, pp. 1–7. IEEE (2012)
Schleipen, M., Okon, M.: The CAEX tool suite - user assistance for the use of standardized plant engineering data exchange. In: 15th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA (2010)
Schmidt, N., Lüder, A., Rosendahl, R., Ryashentseva, D., Foehr, M., Vollmar, J.: Surveying integration approaches for relevance in cyber physical production systems. In: ETFA, pp. 1–8. IEEE (2015)
Acknowledgments
The author would like to thank to Sören Auer and Maria-Esther Vidal for their guidance and fruitful discussions during the development of this doctoral work.
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Grangel-González, I. (2017). Semantic Data Integration for Industry 4.0 Standards. In: Ciancarini, P., et al. Knowledge Engineering and Knowledge Management. EKAW 2016. Lecture Notes in Computer Science(), vol 10180. Springer, Cham. https://doi.org/10.1007/978-3-319-58694-6_36
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DOI: https://doi.org/10.1007/978-3-319-58694-6_36
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