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Capturing data quality requirements for web applications by means of DQ_WebRE

Author

Listed:
  • César Guerra-García

    (Polytechnic University of San Luis Potosí, UPSLP)

  • Ismael Caballero

    (University of Castilla-La Mancha)

  • Mario Piattini

    (University of Castilla-La Mancha)

Abstract

The number of Web applications which are part of Business Intelligence (BI) applications has grown exponentially in recent years, as has their complexity. Consequently, the amount of data used by these applications has also increased. The larger the number of data used, the greater the chance to make errors is. That being the case, managing data with an acceptable level of quality is paramount to success in any organizational business process. In order to raise and maintain adequate levels of Data Quality (DQ), it is indispensable for Web applications to be able to satisfy specific DQ requirements. To do so, DQ requirements should be captured and introduced into the development process of the Web Application, together with the other software requirements needed in the applications. In the field of Web application development, however, there appears to us to exist a lack of proposals aimed at managing specific DQ software requirements. This paper considers the MDA (Model Driven Architecture) approach and, principally, the benefits provided by Model Driven Web Engineering (MDWE), putting forward a proposal for two artifacts. These consist of a metamodel and a UML profile for the management of Data Quality Software Requirements for Web Applications (DQ_WebRE).

Suggested Citation

  • César Guerra-García & Ismael Caballero & Mario Piattini, 2013. "Capturing data quality requirements for web applications by means of DQ_WebRE," Information Systems Frontiers, Springer, vol. 15(3), pages 433-445, July.
    • Handle: RePEc:spr:infosf:v:15:y:2013:i:3:d:10.1007_s10796-012-9401-x
    DOI: 10.1007/s10796-012-9401-x
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    References listed on IDEAS

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    1. Xiang Fang & Clyde W. Holsapple, 2011. "Impacts of navigation structure, task complexity, and users’ domain knowledge on Web site usability—an empirical study," Information Systems Frontiers, Springer, vol. 13(4), pages 453-469, September.
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    Cited by:

    1. Qi Liu & Gengzhong Feng & Nengmin Wang & Giri Kumar Tayi, 2018. "A multi-objective model for discovering high-quality knowledge based on data quality and prior knowledge," Information Systems Frontiers, Springer, vol. 20(2), pages 401-416, April.
    2. Qi Liu & Gengzhong Feng & Nengmin Wang & Giri Kumar Tayi, 0. "A multi-objective model for discovering high-quality knowledge based on data quality and prior knowledge," Information Systems Frontiers, Springer, vol. 0, pages 1-16.
    3. Malu Castellanos & Florian Daniel & Irene Garrigós & Jose-Norberto Mazón, 2013. "Business Intelligence and the Web," Information Systems Frontiers, Springer, vol. 15(3), pages 307-309, July.
    4. Álvaro Carrera & Carlos A. Iglesias & Mercedes Garijo, 2014. "Beast methodology: An agile testing methodology for multi-agent systems based on behaviour driven development," Information Systems Frontiers, Springer, vol. 16(2), pages 169-182, April.

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