In this concept, the studies that have made an evaluation and performance comparison of their data models are presented. We consider this topic important, due to the results obtained in the Transformation between Abstraction Levels subsection, where the results found that nine studies present individual proposals, 12 undefined and six with different techniques.
Similarly, in the Modeling subsection, 27 studies do not present a standard modeling and, according to the Modeling Tool subsection, 29 studies do not define an automatic modeling tool. Based only on this information, it is difficult to select any of the proposals. For this reason, some of the works have carried out an evaluation of their proposed approaches, based on the data load time
At this aspect, we present the database types that the selected relevant studies proposed. They have been classified into two main groups, homogeneous and hybrids. By homogeneous, we mean those databases where the data are implemented in a single database. By hybrids, we mean systems where there are several databases implemented that can be relational and/or NoSQL. According to several studies [14,61,62], due to the variety characteristics of Big Data, the design and management of a database has become complex, so the systems are oriented towards a Polyglot Persistence System (PPS). This means that, when Big Data is stored, it is better to use different storage technologies, that are hybrid databases, so that applications can select the most appropriate one depending on the data they need . Polyglot Persistence is the term used when an application is able to query data from different NoSQL databases
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