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Accounting for quality in data integration systems: a completeness-aware integration approach

Author

Listed:
  • Cinzia Daraio

    (Sapienza University of Rome)

  • Simone Leo

    (Sapienza University of Rome)

  • Monica Scannapieco

    (ISTAT)

Abstract

Ensuring the quality of integrated data is undoubtedly one of the main problems of integrated data systems. When focusing on multi-national and historical data integration systems, where the “space” and “time” dimensions play a relevant role, it is very much important to build the integration layer in such a way that the final user accesses a layer that is “by design” as much complete as possible. In this paper, we propose a method for accessing data in multipurpose data infrastructures, like data integration systems, which has the properties of (i) relieving the final user from the need to access single data sources while, at the same time, (ii) ensuring to maximize the amount of the information available for the user at the integration layer. Our approach is based on a completeness-aware integration approach which allows the user to have ready available all the maximum information that can get out of the integrated data system without having to carry out the preliminary data quality analysis on each of the databases included in the system. Our proposal of providing data quality information at the integrated level extends then the functions of the individual data sources, opening the data infrastructure to additional uses. This may be a first step to move from data infrastructures towards knowledge infrastructures. A case study on the research infrastructure for the science and innovation studies shows the usefulness of the proposed approach.

Suggested Citation

  • Cinzia Daraio & Simone Leo & Monica Scannapieco, 2022. "Accounting for quality in data integration systems: a completeness-aware integration approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1465-1490, March.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:3:d:10.1007_s11192-022-04266-0
    DOI: 10.1007/s11192-022-04266-0
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    References listed on IDEAS

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    1. Cinzia Daraio & Andrea Bonaccorsi, 2017. "Beyond university rankings? Generating new indicators on universities by linking data in open platforms," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(2), pages 508-529, February.
    2. Cinzia Daraio, 2017. "A framework for the Assessment of Research and its impacts," DIAG Technical Reports 2017-04, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    3. Marco Angelini & Cinzia Daraio & Maurizio Lenzerini & Francesco Leotta & Giuseppe Santucci, 2020. "Performance model’s development: a novel approach encompassing ontology-based data access and visual analytics," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 865-892, November.
    4. Cinzia Daraio & Maurizio Lenzerini & Claudio Leporelli & Henk F. Moed & Paolo Naggar & Andrea Bonaccorsi & Alessandro Bartolucci, 2016. "Data integration for research and innovation policy: an Ontology-Based Data Management approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(2), pages 857-871, February.
    5. Atanu Sengupta & Sanjoy De, 2020. "Review of Literature," India Studies in Business and Economics, in: Assessing Performance of Banks in India Fifty Years After Nationalization, chapter 0, pages 15-30, Springer.
    6. Hamid Ekbia & Michael Mattioli & Inna Kouper & G. Arave & Ali Ghazinejad & Timothy Bowman & Venkata Ratandeep Suri & Andrew Tsou & Scott Weingart & Cassidy R. Sugimoto, 2015. "Big data, bigger dilemmas: A critical review," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(8), pages 1523-1545, August.
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