A DataOps Toolbox Enabling Continuous Semantic Integration of Devices for Edge-Cloud AI Applications
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912714612277248 |
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| author | Scrocca, Mario Grassi, Marco Carenini, Alessio Calbimonte, Jean-Paul Anicic, Darko Celino, Irene |
| author_facet | Scrocca, Mario Grassi, Marco Carenini, Alessio Calbimonte, Jean-Paul Anicic, Darko Celino, Irene |
| contents | The implementation of AI-based applications in complex environments often requires the collaboration of several devices spanning from edge to cloud. Identifying the required devices and configuring them to collaborate is a challenge relevant to different scenarios, like industrial shopfloors, road infrastructures, and healthcare therapies. We discuss the design and implementation of a DataOps toolbox leveraging Semantic Web technologies and a low-code mechanism to address heterogeneous data interoperability requirements in the development of such applications. The toolbox supports a continuous semantic integration approach to tackle various types of devices, data formats, and semantics, as well as different communication interfaces. The paper presents the application of the toolbox to three use cases from different domains, the DataOps pipelines implemented, and how they guarantee interoperability of static nodes' information and runtime data exchanges. Finally, we discuss the results from the piloting activities in the use cases and the lessons learned. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_02708 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A DataOps Toolbox Enabling Continuous Semantic Integration of Devices for Edge-Cloud AI Applications Scrocca, Mario Grassi, Marco Carenini, Alessio Calbimonte, Jean-Paul Anicic, Darko Celino, Irene Distributed, Parallel, and Cluster Computing The implementation of AI-based applications in complex environments often requires the collaboration of several devices spanning from edge to cloud. Identifying the required devices and configuring them to collaborate is a challenge relevant to different scenarios, like industrial shopfloors, road infrastructures, and healthcare therapies. We discuss the design and implementation of a DataOps toolbox leveraging Semantic Web technologies and a low-code mechanism to address heterogeneous data interoperability requirements in the development of such applications. The toolbox supports a continuous semantic integration approach to tackle various types of devices, data formats, and semantics, as well as different communication interfaces. The paper presents the application of the toolbox to three use cases from different domains, the DataOps pipelines implemented, and how they guarantee interoperability of static nodes' information and runtime data exchanges. Finally, we discuss the results from the piloting activities in the use cases and the lessons learned. |
| title | A DataOps Toolbox Enabling Continuous Semantic Integration of Devices for Edge-Cloud AI Applications |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2508.02708 |