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Modeling and Management Big Data in Databases—A Systematic Literature Review

Author

Listed:
  • Diana Martinez-Mosquera

    (Department of Informatics and Computer Science, Escuela Politécnica Nacional, 170525 Quito, Ecuador)

  • Rosa Navarrete

    (Department of Informatics and Computer Science, Escuela Politécnica Nacional, 170525 Quito, Ecuador)

  • Sergio Lujan-Mora

    (Department of Software and Computing Systems, University of Alicante, 03690 Alicante, Spain)

Abstract

The work presented in this paper is motivated by the acknowledgement that a complete and updated systematic literature review (SLR) that consolidates all the research efforts for Big Data modeling and management is missing. This study answers three research questions. The first question is how the number of published papers about Big Data modeling and management has evolved over time. The second question is whether the research is focused on semi-structured and/or unstructured data and what techniques are applied. Finally, the third question determines what trends and gaps exist according to three key concepts: the data source, the modeling and the database. As result, 36 studies, collected from the most important scientific digital libraries and covering the period between 2010 and 2019, were deemed relevant. Moreover, we present a complete bibliometric analysis in order to provide detailed information about the authors and the publication data in a single document. This SLR reveal very interesting facts. For instance, Entity Relationship and document-oriented are the most researched models at the conceptual and logical abstraction level respectively and MongoDB is the most frequent implementation at the physical. Furthermore, 2.78% studies have proposed approaches oriented to hybrid databases with a real case for structured, semi-structured and unstructured data.

Suggested Citation

  • Diana Martinez-Mosquera & Rosa Navarrete & Sergio Lujan-Mora, 2020. "Modeling and Management Big Data in Databases—A Systematic Literature Review," Sustainability, MDPI, vol. 12(2), pages 1-41, January.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:2:p:634-:d:309002
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    Citations

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    Cited by:

    1. Eric Hitimana & Gaurav Bajpai & Richard Musabe & Louis Sibomana & Jayavel Kayalvizhi, 2021. "Implementation of IoT Framework with Data Analysis Using Deep Learning Methods for Occupancy Prediction in a Building," Future Internet, MDPI, vol. 13(3), pages 1-19, March.
    2. Johan Augusto Bocanegra Cifuentes & Davide Borelli & Antonio Cammi & Guglielmo Lomonaco & Mario Misale, 2020. "Lattice Boltzmann Method Applied to Nuclear Reactors—A Systematic Literature Review," Sustainability, MDPI, vol. 12(18), pages 1-37, September.
    3. Amalan Mahendran & Helen Thompson & James M. McGree, 2023. "A model robust subsampling approach for Generalised Linear Models in big data settings," Statistical Papers, Springer, vol. 64(4), pages 1137-1157, August.

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