Abstract
The recent advancements in Internet of Things (IoT) technology and the increasing amount of sensing devices that collect and/or generate massive sensor data streams enhances the use of streaming analytics for providing timely and meaningful insights. The current paper proposes a framework for supporting streaming analytics in edge-cloud computational environment for logistics operations in order to maximize the potential value of IoT technology. The proposed framework is demonstrated in a real-life scenario of a large transportation asset in the aviation sector.
Keywords
- Data analytics
- Machine learning
- Predictive maintenance
- Aviation
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Acknowledgements
This work has been funded by the European Commission project H2020 UPTIME “Unified Predictive Maintenance System” (768634).
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von Stietencron, M. et al. (2020). Streaming Analytics in Edge-Cloud Environment for Logistics Processes. In: Lalic, B., Majstorovic, V., Marjanovic, U., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Towards Smart and Digital Manufacturing. APMS 2020. IFIP Advances in Information and Communication Technology, vol 592. Springer, Cham. https://doi.org/10.1007/978-3-030-57997-5_29
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DOI: https://doi.org/10.1007/978-3-030-57997-5_29
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