Abstract
In the context of Industry 4.0 and smart production, industrial large-scale enterprise data is applied for enabling data-driven analysis and modeling methods. However, the majority of the currently applied approaches consider the data in isolated fashion such that data from different sources, e.g., from large data warehouses are only considered independently. Furthermore, connections and relations between those data, i.e., relating to semantic dependencies are typically not considered, while these would open up integrated semantic approaches for effective data mining methods. This paper tackles these issues and demonstrates approaches and experiences in the context of a real-world case study in the industrial logistics domain: We propose knowledge-based data analysis applying subgroup discovery for identifying exceptional patterns in a semantic approach using appropriately constructed knowledge graphs.
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Sternberg, E., Atzmueller, M. (2018). Knowledge-Based Mining of Exceptional Patterns in Logistics Data: Approaches and Experiences in an Industry 4.0 Context. In: Ceci, M., Japkowicz, N., Liu, J., Papadopoulos, G., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2018. Lecture Notes in Computer Science(), vol 11177. Springer, Cham. https://doi.org/10.1007/978-3-030-01851-1_7
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