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?
3. Results
In this section, we answer the research questions via the below activities:
1. A bibliometric analysis, to gather information about the authors and the publication data, the authors and countries with more contributions in the subject, the impact of the selected studies and how the research has grown throughout time, as well as the journals and conferences proceedings where the studies were published;
2. A literature review to map the studies according to three key concepts - source, modeling and database - in a concept matrix. In the source concept, we analyze the dataset sources and data types. In the modeling concept, we analyze the data abstraction levels, the data models proposed at the conceptual, logical and physical levels, the techniques used to perform transformations between abstraction levels, the applied modeling language, the modeling methodology and the proposed modeling tools. At the database concept, we analyze the type and conduct an evaluation and performance comparison between models;
3. A discussion to identify trends and gaps in Big Data modeling and management.