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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2506.08597 |
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| _version_ | 1866916787936821248 |
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| author | Omidi, H. Sacco, L. Hutter, V. Irsiegler, G. Claus, M. Schobben, M. Jacob, A. Schramm, M. Fiore, S. |
| author_facet | Omidi, H. Sacco, L. Hutter, V. Irsiegler, G. Claus, M. Schobben, M. Jacob, A. Schramm, M. Fiore, S. |
| contents | Capturing the history of operations and activities during a computational workflow is significantly important for Earth Observation (EO). The data provenance helps to collect the metadata that records the lineage of data products, providing information about how data are generated, transferred, manipulated, by whom all these operations are performed and through which processes, parameters, and datasets. This paper presents an approach to improve those aspects, by integrating the data provenance library yProv4WFs within openEO, a platform to let users connect to Earth Observation cloud back-ends in a simple and unified way. In addition, it is demonstrated how the integration of data provenance concepts across EO processing chains enables researchers and stakeholders to better understand the flow, the dependencies, and the transformations involved in analytical workflows. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_08597 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Provenance-Aware Earth Observation Workflows: the openEO Case Study Omidi, H. Sacco, L. Hutter, V. Irsiegler, G. Claus, M. Schobben, M. Jacob, A. Schramm, M. Fiore, S. Distributed, Parallel, and Cluster Computing Capturing the history of operations and activities during a computational workflow is significantly important for Earth Observation (EO). The data provenance helps to collect the metadata that records the lineage of data products, providing information about how data are generated, transferred, manipulated, by whom all these operations are performed and through which processes, parameters, and datasets. This paper presents an approach to improve those aspects, by integrating the data provenance library yProv4WFs within openEO, a platform to let users connect to Earth Observation cloud back-ends in a simple and unified way. In addition, it is demonstrated how the integration of data provenance concepts across EO processing chains enables researchers and stakeholders to better understand the flow, the dependencies, and the transformations involved in analytical workflows. |
| title | Towards Provenance-Aware Earth Observation Workflows: the openEO Case Study |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2506.08597 |