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Complex Event Processing as an Approach for real-time Analytics in industrial Environments

: Lamberti, Robin; Stojanovic, Ljiljana


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Industrial Electronics Society -IES-:
IEEE 17th International Conference on Industrial Informatics, INDIN 2019. Proceedings : Industrial Applications of Artificial Intelligence, 22-25 July 2019, Helsinki-Espoo, Finland
Piscataway, NJ: IEEE, 2019
ISBN: 978-1-7281-2927-3
ISBN: 978-1-7281-2928-0
International Conference on Industrial Informatics (INDIN) <17, 2019, Helsinki-Espoo>
Conference Paper
Fraunhofer IOSB ()
manufacturing; IoT; data analytics; complex event processing; edge computing

Real-time analysis of Internet of Things sensor data is crucial for players in the industrial sector for staying competitive. That is, why highly capable and easy to integrate solutions are needed for this, which can be deployed close to the data sources. In this paper, we argue that Complex Event Processing (CEP), which is a model-driven data analytics approach, is such a technique. CEP is able to achieve high throughput of data without the need of the computing power available in modern cloud infrastructures, while producing semantically higher value data in real time. Our here presented solution using CEP is easily integrated, scalable and capable of processing big amounts of data while giving semantic assurances through meta data modeling. Users of our solution do not need to learn any languages to model patterns, but can do that with an intuitive, graphical approach running on mobile devices, which makes it a good fit for domain experts working in industrial environments today. Solutions like the one presented in this paper can be a key-enabler for new business models in the industrial sector and smart factories.