IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v127y2022i12d10.1007_s11192-022-04399-2.html
   My bibliography  Save this article

A visual analytics approach for the assessment of information quality of performance models—a software review

Author

Listed:
  • Marco Angelini

    (Sapienza University of Rome)

  • Cinzia Daraio

    (Sapienza University of Rome)

  • Luca Urban

    (Sapienza University of Rome)

Abstract

In this paper we provide a review of the main functionalities of a Visual Analytics Environment (VAE) developed for the assessment of data and information quality in the context of performance evaluation of research organizations. Performing data and information quality tests are necessary procedures to ensure the bibliometric and research performance evaluation analysis of organizations have the necessary robustness. The proposed environment is helpful to guide the user to an Information Quality-aware development of Performance models. This interactive visual analytics environment offers to the user the possibility to produce and compare information quality-aware indicators, exploring and defining correct behavior, identifying anomalous cases from both data quality and information quality perspectives, and supporting the user in forming hypotheses on possible causes for those anomalies. The proposed approach, exploiting visual interactive exploration results in a more efficient process, minimizing the number of cases for which a manual investigation is needed. The illustration on European higher education institutions data demonstrates the use of the presented functionalities and their benefits.

Suggested Citation

  • Marco Angelini & Cinzia Daraio & Luca Urban, 2022. "A visual analytics approach for the assessment of information quality of performance models—a software review," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 6827-6853, December.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:12:d:10.1007_s11192-022-04399-2
    DOI: 10.1007/s11192-022-04399-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-022-04399-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-022-04399-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:127:y:2022:i:12:d:10.1007_s11192-022-04399-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.