Definition of a DSL focused on Integrating Machine Learning into IoT-Enhanced BPs
The integration of Machine Learning into IoT-Enhanced Business Processes (BPs) offers significant opportunities to enable predictive and adaptive process behavior. However, in current practice, ML capabilities are typically developed through ad-hoc pipelines that remain disconnected from process mod...
| Autores: | , |
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| Tipo de recurso: | informe técnico |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
| OAI Identifier: | oai:dnet:riunet______::92814a347e27a917d1245eb88e44cd2b |
| Acceso en línea: | https://riunet.upv.es/handle/10251/234384 |
| Access Level: | acceso abierto |
| Palabra clave: | BPMN Business Process IoT Machine Learning |
| Sumario: | The integration of Machine Learning into IoT-Enhanced Business Processes (BPs) offers significant opportunities to enable predictive and adaptive process behavior. However, in current practice, ML capabilities are typically developed through ad-hoc pipelines that remain disconnected from process modeling and execution, leading to limited traceability, maintainability, and reuse. To address this challenge, this paper proposes a Domain-Specific Language (DSL) that supports the systematic and model-driven integration of ML into IoT-Enhanced BPs. The DSL enables the explicit specification of heterogeneous data sources, feature engineering operations, ML-ready datasets, and ML model configurations within a unified and platform-independent framework. The approach is supported by a model-to-code transformation that generates executable ML pipelines. |
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