Modeling and Management of Big Data in Databases
The main real-world datasets used in the studies analyzed for this paper were sensor data, image metadata, website publications, and electronic documents. Most of the studies analyzed did not document the specific languages they used to model their data or the tool they used. But due to the need to analyze large volumes of data with various structures, which arrive in high frequency, database research became more focused on NoSQL than relational databases. Why might a NoSQL vs. Relational approach be best for database management, according to growing trends captured in this review of research?
Abstract
The work presented in this paper is motivated by the acknowledgment 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.
Source: Diana Martinez-Mosquera, Rosa Navarrete and Sergio Lujan-Mora, https://www.mdpi.com/2071-1050/12/2/634/htm
This work is licensed under a Creative Commons Attribution 4.0 License.