Research Paper: A Systematic Analysis of Problems in Open Collaborative Data Engineering

Abstract: Collaborative workflows are common in open-source software development. They reduce individual costs and improve the quality of work results. Open data shares many characteristics with open-source software as it can be used, modified, and redistributed by anyone, for free. However, in contrast to open-source software engineering, collaborative data engineering on open data lacks a shared understanding of processes, methods, and tools. This article presents a systematic literature review of collaboration processes, methods, and tools in data engineering as performed by open data users. An additional interview study with practitioners confirms and enhances the findings and strengthens the resulting insights. We find an ecosystem with heterogeneous participants and no standardized processes, methods, and tools. Participants face a variety of technical and social challenges during their work. Our work provides a structured overview of collaboration systems in open collaborative data engineering, enabling further research. Additionally, we contribute preliminary guidelines for successful open collaborative data engineering projects and recommendations to increase its adoption for open data ecosystems.

Keywords: collaboration, data engineering, open data, JValue

Reference: Philip Heltweg and Dirk Riehle. 2023. A Systematic Analysis of Problems in Open Collaborative Data Engineering. In Proceedings of the ACM Transactions on Social Computing.

The paper is available as a PDF.