Details
| Original language | English |
|---|---|
| Article number | 3 |
| Pages (from-to) | 1-12 |
| Number of pages | 12 |
| Journal | Journal of Data and Information Quality |
| Volume | 14 |
| Issue number | 1 |
| Early online date | 23 Dec 2021 |
| Publication status | Published - Mar 2022 |
| Externally published | Yes |
Abstract
A data ecosystem (DE) offers a keystone-player or alliance-driven infrastructure that enables the interaction of different stakeholders and the resolution of interoperability issues among shared data. However, despite years of research in data governance and management, trustability is still affected by the absence of transparent and traceable data-driven pipelines. In this work, we focus on requirements and challenges that DEs face when ensuring data transparency. Requirements are derived from the data and organizational management, as well as from broader legal and ethical considerations. We propose a novel knowledge-driven DE architecture, providing the pillars for satisfying the analyzed requirements. We illustrate the potential of our proposal in a real-world scenario. Last, we discuss and rate the potential of the proposed architecture in the fulfillmentof these requirements.
Keywords
- data ecosystems, data quality, Data transparency, trustability
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Decision Sciences(all)
- Information Systems and Management
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In: Journal of Data and Information Quality, Vol. 14, No. 1, 3, 03.2022, p. 1-12.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Knowledge-Driven Data Ecosystems Toward Data Transparency
AU - Geisler, Sandra
AU - Vidal, Maria Esther
AU - Cappiello, Cinzia
AU - Lóscio, Bernadette Farias
AU - Gal, Avigdor
AU - Jarke, Matthias
AU - Lenzerini, Maurizio
AU - Missier, Paolo
AU - Otto, Boris
AU - Paja, Elda
AU - Pernici, Barbara
AU - Rehof, Jakob
N1 - Funding Information: A. Gal was supported by the Benjamin and Florence Free Chair M. Lenzerini was supported by the MUR-PRIN project “HOPE” (grant 2017MMJJRE) and the EU under the H2020-EU.2.1.1 project TAILOR (grant 952215). M.-E. Vidal was supported by the EU H2020 project iASiS (grant 727658) and CLARIFY (grant 875160). S. Geisler was supported by the German Innovation Fund project SALUS (grant 01NVF18002). This work has also supported by the German Federal Ministry of Education and Research (BMBF) in the context of the InDaSpacePlus project (grant 01IS17031), Fraunhofer Cluster of Excellence “Cognitive Internet Technologies” (CCIT), and by the Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy - EXC-2023 Internet of Production - 390621612. Pernici acknowledges the support of the EU H2020 Crowd4SDG project, grant id 872944. Authors’ addresses: S. Geisler, Fraunhofer FIT, Germany, Schloss Birlinghoven, Sankt Augustin, 53757 and RWTH Aachen University, Germany, Ahornstrasse 55, Aachen, 52056 Schloss Birlinghoven, Sankt Augustin, 53757; email: geisler@cs.rwth-aachen.de; M.-E. Vidal, TIB-Leibniz Information Centre for Science and Technology, Gerrmany, Welfengarten 1B, Hannover, 30167; email: maria.vidal@tib.eu; C. Cappiello, Politecnico di Milano, Italy, piazza Leonardo da Vinci 32, Milano, 20133; email: cinzia.cappiello@polimi.it; B. F. Lóscio, Federal University of Pernambuco, Brazil, Cidade Universitaria, Recife/PE, 50740-560; email: bfl@cin.ufpe.br; A. Gal, Technion Israel Institute of Technology, Israel, Technion City, Haifa, 32000; email: avigal@ie.technion.ac.il; M. Jarke, RWTH Aachen University and Fraunhofer FIT, Germany, Ahornstrasse 55, Aachen, 52056; email: jarke@dbis.rwth-aachen.de; M. Lenzerini, Sapienza Università di Roma, Italy, via Ariosto 25, Roma, I-00185; email: lenzerini@diag.uniroma1.it; P. Missier, Newcastle University, United Kingdom, Firebrick Avenue, Newcastle upon Tyne, NE4 5TG; email: paolo.missier@ncl.ac.uk; B. Otto and J. Rehof, TU Dortmund University, Germany, Otto-Hahn-Str. 12, Dortmund, 44227, Fraunhofer ISST, Germany, Emil-Figge-Straße 91, Dortmund, 44227; emails: {boris.otto, jakob.rehof}@cs.tu-dortmund.de; E. Paja, IT University of Copenhagen, Denmark, Rued Langgaards Vej 7, Copenhagen S, DK-2300; email: elpa@itu.dk; B. Pernici, Politecnico di Milano, Italy, piazza Leonardo da Vinci 32, Milano, 20133; email: barbara.pernici@polimi.it. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM. 1936-1955/2021/12-ART3 $15.00 https://doi.org/10.1145/3467022
PY - 2022/3
Y1 - 2022/3
N2 - A data ecosystem (DE) offers a keystone-player or alliance-driven infrastructure that enables the interaction of different stakeholders and the resolution of interoperability issues among shared data. However, despite years of research in data governance and management, trustability is still affected by the absence of transparent and traceable data-driven pipelines. In this work, we focus on requirements and challenges that DEs face when ensuring data transparency. Requirements are derived from the data and organizational management, as well as from broader legal and ethical considerations. We propose a novel knowledge-driven DE architecture, providing the pillars for satisfying the analyzed requirements. We illustrate the potential of our proposal in a real-world scenario. Last, we discuss and rate the potential of the proposed architecture in the fulfillmentof these requirements.
AB - A data ecosystem (DE) offers a keystone-player or alliance-driven infrastructure that enables the interaction of different stakeholders and the resolution of interoperability issues among shared data. However, despite years of research in data governance and management, trustability is still affected by the absence of transparent and traceable data-driven pipelines. In this work, we focus on requirements and challenges that DEs face when ensuring data transparency. Requirements are derived from the data and organizational management, as well as from broader legal and ethical considerations. We propose a novel knowledge-driven DE architecture, providing the pillars for satisfying the analyzed requirements. We illustrate the potential of our proposal in a real-world scenario. Last, we discuss and rate the potential of the proposed architecture in the fulfillmentof these requirements.
KW - data ecosystems
KW - data quality
KW - Data transparency
KW - trustability
UR - http://www.scopus.com/inward/record.url?scp=85124698785&partnerID=8YFLogxK
U2 - 10.1145/3467022
DO - 10.1145/3467022
M3 - Article
AN - SCOPUS:85124698785
VL - 14
SP - 1
EP - 12
JO - Journal of Data and Information Quality
JF - Journal of Data and Information Quality
SN - 1936-1955
IS - 1
M1 - 3
ER -