According to Figure 10, there are 25 selected studies that present their approaches at the three abstraction levels and eight studies at two levels differentiated from logical to physical, conceptual to logical and conceptual to physical. According to this concept, the proposed approaches for the transformation between the data abstraction levels are presented.
As presented in Figure 14, there are 19 studies where the authors propose their own novel mapping rules, which demonstrates the separate research that exists on this topic. Thus, it is difficult to decide which is the most appropriate when selecting any of them. Another interesting aspect is that 12 studies do not define transformation rules and there are six studies that propose transformations based on other techniques, such as the Linearization Algorithm (LA), ATL
We perceive that MongoDB is the most studied DBMS because large companies use it, probably because of its characteristics of support for aggregation and secondary indexes query operations and the consistency and partitioning tolerance mentioned in the Big Data Concepts subsection. Furthermore, these are open source databases with highly scalable, flexible and best performance compared with relational databases. These results give us the idea of a trend in each of the known data models—document-oriented—with MongoDB, column-oriented with Cassandra and graph with Neo4j.
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