SWEL: A Domain-Specific Language for Modeling Data-Intensive Workflows

Rubén Salado-Cid, Antonio Vallecillo, Kamram Munir and José Raúl Romero ()
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Rubén Salado-Cid: University of Córdoba
Antonio Vallecillo: Universidad de Málaga
Kamram Munir: University of the West of England
José Raúl Romero: University of Córdoba

Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, 2024, vol. 66, issue 2, No 3, 137-160

Abstract: Abstract Data-intensive applications aim at discovering valuable knowledge from large amounts of data coming from real-world sources. Typically, workflow languages are used to specify these applications, and their associated engines enable the execution of the specifications. However, as these applications become commonplace, new challenges arise. Existing workflow languages are normally platform-specific, which severely hinders their interoperability with other languages and execution engines. This also limits their reusability outside the platforms for which they were originally defined. Following the Design Science Research methodology, the paper presents SWEL (Scientific Workflow Execution Language). SWEL is a domain-specific modeling language for the specification of data-intensive workflows that follow the model-driven engineering principles, covering the high-level definition of tasks, information sources, platform requirements, and mappings to the target technologies. SWEL is platform-independent, enables collaboration among data scientists across multiple domains and facilitates interoperability. The evaluation results show that SWEL is suitable enough to represent the concepts and mechanisms of commonly used data-intensive workflows. Moreover, SWEL facilitates the development of related technologies such as editors, tools for exchanging knowledge assets between workflow management systems, and tools for collaborative workflow development.

Keywords: Model-driven engineering; Domain-specific modeling; Conceptual modeling; Data-intensive applications; Data-driven workflows; Data science (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s12599-023-00826-7

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