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?
2. Method
2.2. Conducting the SLR Study
The search of the SLR study was conducted in August 2019. Figure 3 provides a representation of the selection process applied to the studies. Duplicated studies were discarded from the potentially relevant studies stage.
Figure 3. Selection Process
2.2.1. Inclusion Stage
In this stage, we selected studies related to Big Data modeling and checked their titles, abstracts and keywords according to the previously planned strategy. We considered only English studies that addressed our research questions and published in conferences or journals. Our main objective is to identify the different approaches to data modeling and management in data stores in a general way for the different types of data at the three abstraction levels. As a result, 1 Chinese article and 27 articles corresponding to books, book chapters, letters, notes or editorials, were discarded. Additionally, we also discarded 1259 articles that, although mentioning data models, referred to specific applications or not related to data persistence but to data ingestion, data lakes or data analytics. From this stage, 117 studies were accepted.
2.2.2. Selection Stage
At this stage, a quick review of the full content of every study allowed us to select only those studies related to Big Data modeling. This resulted in the acceptance of 31 studies, the rejection of 70 papers and the filtering of 16 duplicated works.
At this phase, we eliminated the duplicated papers.
After scanning the whole content of these selected 31 studies, we also included five new papers after the snowballing review. Finally, 36 studies made up our final corpus to report the SLR study.