Abstract
Large-scale Internet-of-Things (IoT) environments such as Intelligent Transportation Systems are facing tremendous challenges wrt. monitoring their operational technology (OT) not least due to its inherent heterogeneous and evolutionary nature. This situation is often aggravated by the lack of machine-interpretable information about the interdependencies between OT objects in terms of “semantic relationships”, thus considerably impeding the detection of root causes of cross-system errors or interrelated impacts. Therefore, we propose a novel hybrid approach for identifying semantic relationships based on both, mined functional correlations between OT objects based on log files and domain knowledge in terms of an IoT meta model. For this, we firstly contribute a systematic discussion of associated challenges faced in large-scale IoT environments, secondly, we put forward an IoT meta model based on both, industry standards and academic proposals, and finally, we employ this meta model as guidance and target template for the automatic population of semantic relationships into an OT ontology.
This work is supported by: the Austrian Research Promotion Agency (FFG) under grant FFG Forschungspartnerschaften 874490 and by Erasmus+ under grant agreement No 2021-1-SI01-KA220-HED-000032218, project ID KA220-HED-15/21.
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Notes
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Please note that the notation sticks to UML class and object diagrams, whereas the red lines between objects represent a functional correlation and yellow arrows represent the impact one object has on another object in real-world.
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Graf, D., Retschitzegger, W., Schwinger, W., Kapsammer, E., Baumgartner, N. (2022). Bridging Signals and Human Intelligence. In: Memmi, G., Yang, B., Kong, L., Zhang, T., Qiu, M. (eds) Knowledge Science, Engineering and Management. KSEM 2022. Lecture Notes in Computer Science(), vol 13369. Springer, Cham. https://doi.org/10.1007/978-3-031-10986-7_46
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