Data Service Composition in Cyber-Physical Systems Adopting LLMs

The increasing adoption of Large Language Models (LLMs) has significantly lowered technical barriers across various domains, enabling tasks such as code generation, database querying, and service composition. This work explores the potential of LLMs to facilitate human access to heterogeneous data in Cyber-Physical Systems (CPS). The proposed system interprets natural language queries and, by leveraging in-context learning, dynamically synthesizes data integration pipelines that compose data services, thus enabling access to information from multiple sources. The execution of a pipeline generates a table that fulfills the input query, effectively integrating and structuring the retrieved data. This paper presents the design and implementation of the proposed solution and its evaluation using a real-world case study and the BIRD benchmark dataset. The results demonstrate its effectiveness in improving data retrieval, data service design, and execution in CPS.