## 2 Related Work  
Several studies investigate the performances of individual databases, or present which SQL dialects or ORM frameworks the developer should choose and why. The investigation evolved from the analysis of individual databases and the necessary steps that need to be taken to ensure that a DBMS performs optimally [18] in comparison to relational and non-relational databases, considering the need to choose one of the alternatives for particular conditions. The comparison criteria include theoretical differences, features, restrictions, integrity, distribution, system requirements, architecture, queries, and insertion times [19]. Particular databases provide improvement possibilities, for example Oracle's optimizer hints that it may force various approaches for reducing the execution times of stored procedures. Some system parameters have greatly influenced the performance of the execution times of stored procedures and functions [20]. Another strategy is optimizing the execution plan, analyzed in depth in [21], using an application of mobile telephony through the execution of the stored procedure PL/SQL, which processes telephone invoices on a database with a million recordings. The existence of various performance optimization alternatives generated the idea of classifying them as prediction, diagnosis, and tuning approaches. The prediction of future performance can influence changes in configurations and resources. The analysis of anomalies can diagnose the ground cause introducing performance regression. Tuning operations improve the performance by adjusting influencing factors such as the indexes, views, stored procedures, and queries' designs [22]. Current trends in Big Data have generated more interest in the in-depth analysis of tuning possibilities. Obtaining quick results is limited by the speed of big networks and their data processing capabilities. Finding the data location and the dynamic modification of the current data generates problems in fetching the repository. Consequently, scaling the databases requires tuning the distributed framework and programming languages to process large datasets over the network [23]. The vast landscape of the database world and the advantages of NoSQL has generated interest in analyzing in depth their performances. The widely used open-source NoSQL document databases, Couchbase, CouchDB, and MongoDB are evaluated in [24] using the Yahoo! Cloud Serving Benchmark (YCSB) [25], which has become a standard for NoSQL database evaluation. Data representation in different formats and the data exchange necessity over the internet require analyses of the performances of the efficiency of mapping techniques, which need to be executed in two ways, before and after the transmission of data [26]. The rise of Cloud Computing creates particular performance challenges. The Cloud Provider intends to offer database services that are able to control the response time for specific customers. Popular databases that are widely used may be adapted to enable differentiated per-user/request performance on a priority basis through CPU scheduling and the synchronization mechanisms available within the operating system [27]. Database access optimization may also rely on the initial performance evaluation and on improving the performances through machine learning. The strategy is to first calculate consumption, then optimize the main performance modules and select the optimized structure of the database for calculation, and conduct a data analysis for the calculation results [28]. [PERSON] compares, while experimenting with the Ubuntu operating system, several ORM frameworks: Hibernate, OpenJPA, and EclipseLink. He performed five queries to access a table. The first query made a read using the primary key; three queries made a read using different type attributes; and one query made a read using two attributes. The experiments demonstrated that, for a large range of record numbers (10,000 to 160,000), Hibernate is the fastest and OpenJPA is the slowest.