Anna Gieß , Thorsten Schoormann , Frederik Möller , Inan Gür
{"title":"Discovering data spaces: A classification of design options","authors":"Anna Gieß , Thorsten Schoormann , Frederik Möller , Inan Gür","doi":"10.1016/j.compind.2024.104212","DOIUrl":null,"url":null,"abstract":"<div><div>Technical coordination between organizations and security concerns are among the major barriers to data sharing. Data spaces are an emerging digital infrastructure that helps address these challenges by sovereignly sharing data across institutional boundaries. The data space concept is at the core of many high-profile research initiatives in the European Union and receives great adoption in practice. Despite the great interest, there is, however, a demand for more conceptual clarity and approaches to describe and design them purposefully. We propose a taxonomy of data space design options grounded in a literature review, an analysis of real-world objects, and over nine hours of expert interviews with data space initiatives. The taxonomy advances our understanding of data space designs and gives a framework to practice making informed design decisions. Our work provides a comprehensive solution space for data space designers to (a) (re-)design data spaces more efficiently and (b) acquire a ‘big picture’ of what needs to be considered.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"164 ","pages":"Article 104212"},"PeriodicalIF":8.2000,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers in Industry","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0166361524001404","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Technical coordination between organizations and security concerns are among the major barriers to data sharing. Data spaces are an emerging digital infrastructure that helps address these challenges by sovereignly sharing data across institutional boundaries. The data space concept is at the core of many high-profile research initiatives in the European Union and receives great adoption in practice. Despite the great interest, there is, however, a demand for more conceptual clarity and approaches to describe and design them purposefully. We propose a taxonomy of data space design options grounded in a literature review, an analysis of real-world objects, and over nine hours of expert interviews with data space initiatives. The taxonomy advances our understanding of data space designs and gives a framework to practice making informed design decisions. Our work provides a comprehensive solution space for data space designers to (a) (re-)design data spaces more efficiently and (b) acquire a ‘big picture’ of what needs to be considered.
期刊介绍:
The objective of Computers in Industry is to present original, high-quality, application-oriented research papers that:
• Illuminate emerging trends and possibilities in the utilization of Information and Communication Technology in industry;
• Establish connections or integrations across various technology domains within the expansive realm of computer applications for industry;
• Foster connections or integrations across diverse application areas of ICT in industry.