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Information Quality Assessment for Data Fusion Systems

Author

Listed:
  • Miguel A. Becerra

    (Instituto Tecnológico Metropolitano, Cra. 74d #732, Medellín 050034, Colombia
    Facultad de Ciencias Básicas, Universidad de Medellín, MATBIOM, Cra. 87 #30-65, Medellín 050010, Colombia
    These authors contributed equally to this work.)

  • Catalina Tobón

    (Facultad de Ciencias Básicas, Universidad de Medellín, MATBIOM, Cra. 87 #30-65, Medellín 050010, Colombia
    These authors contributed equally to this work.)

  • Andrés Eduardo Castro-Ospina

    (Instituto Tecnológico Metropolitano, Cra. 74d #732, Medellín 050034, Colombia
    These authors contributed equally to this work.)

  • Diego H. Peluffo-Ordóñez

    (Modeling, Simulation and Data Analysis (MSDA) Research Program, Mohammed VI Polytechnic University, Ben Guerir 47963, Morocco
    Faculty of Engineering, Corporación Universitaria Autónoma de Nariño, Carrera 28 No. 19-24, Pasto 520001, Colombia)

Abstract

This paper provides a comprehensive description of the current literature on data fusion, with an emphasis on Information Quality (IQ) and performance evaluation. This literature review highlights recent studies that reveal existing gaps, the need to find a synergy between data fusion and IQ, several research issues, and the challenges and pitfalls in this field. First, the main models, frameworks, architectures, algorithms, solutions, problems, and requirements are analyzed. Second, a general data fusion engineering process is presented to show how complex it is to design a framework for a specific application. Third, an IQ approach, as well as the different methodologies and frameworks used to assess IQ in information systems are addressed; in addition, data fusion systems are presented along with their related criteria. Furthermore, information on the context in data fusion systems and its IQ assessment are discussed. Subsequently, the issue of data fusion systems’ performance is reviewed. Finally, some key aspects and concluding remarks are outlined, and some future lines of work are gathered.

Suggested Citation

  • Miguel A. Becerra & Catalina Tobón & Andrés Eduardo Castro-Ospina & Diego H. Peluffo-Ordóñez, 2021. "Information Quality Assessment for Data Fusion Systems," Data, MDPI, vol. 6(6), pages 1-30, June.
    • Handle: RePEc:gam:jdataj:v:6:y:2021:i:6:p:60-:d:571326
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    References listed on IDEAS

    as
    1. Reza Vaziri & Mehran Mohsenzadeh & Jafar Habibi, 2016. "TBDQ: A Pragmatic Task-Based Method to Data Quality Assessment and Improvement," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-30, May.
    2. Xiaoyu Xiong & Benjamin D. Youngman & Theodoros Economou, 2021. "Data fusion with Gaussian processes for estimation of environmental hazard events," Environmetrics, John Wiley & Sons, Ltd., vol. 32(3), May.
    3. Jeusfeld, M.A. & Quix, C. & Jarke, M., 1998. "Design and analysis of quality information for data warehouses," Other publications TiSEM fde64335-eb29-4c82-b7c8-5, Tilburg University, School of Economics and Management.
    4. Heidari, Farideh & Loucopoulos, Pericles, 2014. "Quality evaluation framework (QEF): Modeling and evaluating quality of business processes," International Journal of Accounting Information Systems, Elsevier, vol. 15(3), pages 193-223.
    5. Torres, Russell & Sidorova, Anna, 2019. "Reconceptualizing information quality as effective use in the context of business intelligence and analytics," International Journal of Information Management, Elsevier, vol. 49(C), pages 316-329.
    6. Besiki Stvilia & Les Gasser & Michael B. Twidale & Linda C. Smith, 2007. "A framework for information quality assessment," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(12), pages 1720-1733, October.
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    Cited by:

    1. Jorge Parraga-Alava & Roberth Alcivar-Cevallos & Jéssica Morales Carrillo & Magdalena Castro & Shabely Avellán & Aaron Loor & Fernando Mendoza, 2021. "LeLePhid: An Image Dataset for Aphid Detection and Infestation Severity on Lemon Leaves," Data, MDPI, vol. 6(5), pages 1-7, May.
    2. Marek Stawowy & Stanisław Duer & Krzysztof Perlicki & Tomasz Mrozek & Marta Harničárová, 2023. "Supporting Information Quality Management in Information and Communications Technology Systems with Uncertainty Modelling," Energies, MDPI, vol. 16(6), pages 1-18, March.

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