ContinuumConductor : Decentralized Process Mining on the Edge-Cloud Continuum
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866918236767911936 |
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| author | Reiter, Hendrik Edinger, Janick Kabierski, Martin Koschmider, Agnes Landsiedel, Olaf Lepsien, Arvid Lu, Xixi Marrella, Andrea Serral, Estefania Schulte, Stefan Tschorsch, Florian Weidlich, Matthias Hasselbring, Wilhelm |
| author_facet | Reiter, Hendrik Edinger, Janick Kabierski, Martin Koschmider, Agnes Landsiedel, Olaf Lepsien, Arvid Lu, Xixi Marrella, Andrea Serral, Estefania Schulte, Stefan Tschorsch, Florian Weidlich, Matthias Hasselbring, Wilhelm |
| contents | Process mining traditionally assumes centralized event data collection and analysis. However, modern Industrial Internet of Things systems increasingly operate over distributed, resource-constrained edge-cloud infrastructures. This paper proposes a structured approach for decentralizing process mining by enabling event data to be mined directly within the IoT systems edge-cloud continuum. We introduce ContinuumConductor a layered decision framework that guides when to perform process mining tasks such as preprocessing, correlation, and discovery centrally or decentrally. Thus, enabling privacy, responsive and resource-efficient process mining. For each step in the process mining pipeline, we analyze the trade-offs of decentralization versus centralization across these layers and propose decision criteria. We demonstrate ContinuumConductor at a real-world use-case of process optimazition in inland ports. Our contributions lay the foundation for computing-aware process mining in cyber-physical and IIoT systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_07280 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ContinuumConductor : Decentralized Process Mining on the Edge-Cloud Continuum Reiter, Hendrik Edinger, Janick Kabierski, Martin Koschmider, Agnes Landsiedel, Olaf Lepsien, Arvid Lu, Xixi Marrella, Andrea Serral, Estefania Schulte, Stefan Tschorsch, Florian Weidlich, Matthias Hasselbring, Wilhelm Distributed, Parallel, and Cluster Computing Process mining traditionally assumes centralized event data collection and analysis. However, modern Industrial Internet of Things systems increasingly operate over distributed, resource-constrained edge-cloud infrastructures. This paper proposes a structured approach for decentralizing process mining by enabling event data to be mined directly within the IoT systems edge-cloud continuum. We introduce ContinuumConductor a layered decision framework that guides when to perform process mining tasks such as preprocessing, correlation, and discovery centrally or decentrally. Thus, enabling privacy, responsive and resource-efficient process mining. For each step in the process mining pipeline, we analyze the trade-offs of decentralization versus centralization across these layers and propose decision criteria. We demonstrate ContinuumConductor at a real-world use-case of process optimazition in inland ports. Our contributions lay the foundation for computing-aware process mining in cyber-physical and IIoT systems. |
| title | ContinuumConductor : Decentralized Process Mining on the Edge-Cloud Continuum |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2512.07280 |