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Performance model’s development: a novel approach encompassing ontology-based data access and visual analytics

Author

Listed:
  • Marco Angelini

    (Sapienza University of Rome)

  • Cinzia Daraio

    (Sapienza University of Rome)

  • Maurizio Lenzerini

    (Sapienza University of Rome)

  • Francesco Leotta

    (Sapienza University of Rome)

  • Giuseppe Santucci

    (Sapienza University of Rome)

Abstract

The quantitative evaluation of research is currently carried out by means of indicators calculated on data extracted and integrated by analysts who elaborate them by creating illustrative tables and plots of results. In this approach, the robustness of the metrics used and the possibility for users of the metrics to intervene in the evaluation process are completely neglected. We propose a new approach which is able to move forward, from indicators’ development to an interactive performance model’s development. It combines the advantages of the ontology-based data access paradigm with the flexibility and robustness of a visual analytics environment putting the consumer/stakeholder at the centre of the evaluation. A detailed description of such an approach is presented in the paper. The approach is illustrated and evaluated trough a comprehensive user’s study that proves the added capabilities and the benefits that a user of performance models can have by using this approach.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:125:y:2020:i:2:d:10.1007_s11192-020-03689-x
    DOI: 10.1007/s11192-020-03689-x
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    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    4. Marco Angelini & Cinzia Daraio & Maurizio Lenzerini & Francesco Leotta & Giuseppe Santucci, 2019. "Performance Model’s development: A Novel Approach encompassing Ontology-Based Data Access and Visual Analytics," DIAG Technical Reports 2019-11, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    5. 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".
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    Cited by:

    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.
    2. 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.

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