Knowledge-Driven Data Ecosystems Toward Data Transparency

Sandra Geisler; Maria Esther Vidal; Cinzia Cappiello; Bernadette Farias Lóscio; Avigdor Gal; Matthias Jarke; Maurizio Lenzerini; Paolo Missier; Boris Otto; Elda Paja; Barbara Pernici; Jakob Rehof

doi:10.1145/3467022

Knowledge-Driven Data Ecosystems Toward Data Transparency

Sandra Geisler, Maria Esther Vidal, Cinzia Cappiello, Bernadette Farias Lóscio, Avigdor Gal, Matthias Jarke, Maurizio Lenzerini, Paolo Missier, Boris Otto, Elda Paja, Barbara Pernici, Jakob Rehof

Computer Science

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	3
Number of pages	12
Journal	Journal of Data and Information Quality
Volume	14
Issue number	1
Early online date	23 Dec 2021
DOIs	https://doi.org/10.1145/3467022
Publication status	Published - Mar 2022

Bibliographical note

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: [email protected]; M.-E. Vidal, TIB-Leibniz Information Centre for Science and Technology, Gerrmany, Welfengarten 1B, Hannover, 30167; email: [email protected]; C. Cappiello, Politecnico di Milano, Italy, piazza Leonardo da Vinci 32, Milano, 20133; email: [email protected]; B. F. Lóscio, Federal University of Pernambuco, Brazil, Cidade Universitaria, Recife/PE, 50740-560; email: [email protected]; A. Gal, Technion Israel Institute of Technology, Israel, Technion City, Haifa, 32000; email: [email protected]; M. Jarke, RWTH Aachen University and Fraunhofer FIT, Germany, Ahornstrasse 55, Aachen, 52056; email: [email protected]; M. Lenzerini, Sapienza Università di Roma, Italy, via Ariosto 25, Roma, I-00185; email: [email protected]; P. Missier, Newcastle University, United Kingdom, Firebrick Avenue, Newcastle upon Tyne, NE4 5TG; email: [email protected]; 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: [email protected]; B. Pernici, Politecnico di Milano, Italy, piazza Leonardo da Vinci 32, Milano, 20133; email: [email protected]. 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 [email protected]. © 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

Publisher Copyright:
© 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.

Keywords

data ecosystems
data quality
Data transparency
trustability

ASJC Scopus subject areas

Information Systems
Information Systems and Management

Access to Document

10.1145/3467022

Cite this

@article{a84033c509a24ec5847c9ee447253c4f,

title = "Knowledge-Driven Data Ecosystems Toward Data Transparency",

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",

author = "Sandra Geisler and Vidal, {Maria Esther} and Cinzia Cappiello and L{\'o}scio, {Bernadette Farias} and Avigdor Gal and Matthias Jarke and Maurizio Lenzerini and Paolo Missier and Boris Otto and Elda Paja and Barbara Pernici and Jakob Rehof",

note = "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{\textquoteright}s Excellence Strategy - EXC-2023 Internet of Production - 390621612. Pernici acknowledges the support of the EU H2020 Crowd4SDG project, grant id 872944. Authors{\textquoteright} 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: [email protected]; M.-E. Vidal, TIB-Leibniz Information Centre for Science and Technology, Gerrmany, Welfengarten 1B, Hannover, 30167; email: [email protected]; C. Cappiello, Politecnico di Milano, Italy, piazza Leonardo da Vinci 32, Milano, 20133; email: [email protected]; B. F. L{\'o}scio, Federal University of Pernambuco, Brazil, Cidade Universitaria, Recife/PE, 50740-560; email: [email protected]; A. Gal, Technion Israel Institute of Technology, Israel, Technion City, Haifa, 32000; email: [email protected]; M. Jarke, RWTH Aachen University and Fraunhofer FIT, Germany, Ahornstrasse 55, Aachen, 52056; email: [email protected]; M. Lenzerini, Sapienza Universit{\`a} di Roma, Italy, via Ariosto 25, Roma, I-00185; email: [email protected]; P. Missier, Newcastle University, United Kingdom, Firebrick Avenue, Newcastle upon Tyne, NE4 5TG; email: [email protected]; B. Otto and J. Rehof, TU Dortmund University, Germany, Otto-Hahn-Str. 12, Dortmund, 44227, Fraunhofer ISST, Germany, Emil-Figge-Stra{\ss}e 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: [email protected]; B. Pernici, Politecnico di Milano, Italy, piazza Leonardo da Vinci 32, Milano, 20133; email: [email protected]. 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 [email protected]. {\textcopyright} 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 Publisher Copyright: {\textcopyright} 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.",

year = "2022",

month = mar,

doi = "10.1145/3467022",

language = "English",

volume = "14",

journal = "Journal of Data and Information Quality",

issn = "1936-1955",

publisher = "Association for Computing Machinery (ACM)",

number = "1",

}

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: [email protected]; M.-E. Vidal, TIB-Leibniz Information Centre for Science and Technology, Gerrmany, Welfengarten 1B, Hannover, 30167; email: [email protected]; C. Cappiello, Politecnico di Milano, Italy, piazza Leonardo da Vinci 32, Milano, 20133; email: [email protected]; B. F. Lóscio, Federal University of Pernambuco, Brazil, Cidade Universitaria, Recife/PE, 50740-560; email: [email protected]; A. Gal, Technion Israel Institute of Technology, Israel, Technion City, Haifa, 32000; email: [email protected]; M. Jarke, RWTH Aachen University and Fraunhofer FIT, Germany, Ahornstrasse 55, Aachen, 52056; email: [email protected]; M. Lenzerini, Sapienza Università di Roma, Italy, via Ariosto 25, Roma, I-00185; email: [email protected]; P. Missier, Newcastle University, United Kingdom, Firebrick Avenue, Newcastle upon Tyne, NE4 5TG; email: [email protected]; 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: [email protected]; B. Pernici, Politecnico di Milano, Italy, piazza Leonardo da Vinci 32, Milano, 20133; email: [email protected]. 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 [email protected]. © 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 Publisher Copyright: © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.

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

SN - 1936-1955

VL - 14

JO - Journal of Data and Information Quality

JF - Journal of Data and Information Quality

IS - 1

M1 - 3

ER -

Knowledge-Driven Data Ecosystems Toward Data Transparency

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this