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Currently a continuously increasing amount of data is generated and processed in a daily basis towards improving decision-making and facilitating the gaining of insights. In this context, current era is characterized as the “Era of Big Data” with data characteristics including high volume, velocity, variety, or veracity, creating multiple chances a...
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... stated in Section I, databases are split into two (2) categories. Relational databases are part of the first category and rely on the ACID principle [25]. In relational databases data is stored and presented in tables that have rows and columns, as shown in Fig. 1 [26]. Non-relational databases are part of the second category. This one consists of four (4) subcategories [27], as depicted in Fig. 1, and mainly focuses on the BASE principle [28]. The first subcategory is the key/value pairbased, where every value corresponds to a key. The second subcategory is the column-based where every column ...
Context 2
... two (2) categories. Relational databases are part of the first category and rely on the ACID principle [25]. In relational databases data is stored and presented in tables that have rows and columns, as shown in Fig. 1 [26]. Non-relational databases are part of the second category. This one consists of four (4) subcategories [27], as depicted in Fig. 1, and mainly focuses on the BASE principle [28]. The first subcategory is the key/value pairbased, where every value corresponds to a key. The second subcategory is the column-based where every column of a dataset is stored separately [29]. The third one is the graph-based, where data is stored and represented as a graph [30], [31]. ...
Context 3
... stated in Section I, databases are split into two (2) categories. Relational databases are part of the first category and rely on the ACID principle [25]. In relational databases data is stored and presented in tables that have rows and columns, as shown in Fig. 1 [26]. Non-relational databases are part of the second category. This one consists of four (4) subcategories [27], as depicted in Fig. 1, and mainly focuses on the BASE principle [28]. The first subcategory is the key/value pairbased, where every value corresponds to a key. The second subcategory is the column-based where every column ...
Context 4
... two (2) categories. Relational databases are part of the first category and rely on the ACID principle [25]. In relational databases data is stored and presented in tables that have rows and columns, as shown in Fig. 1 [26]. Non-relational databases are part of the second category. This one consists of four (4) subcategories [27], as depicted in Fig. 1, and mainly focuses on the BASE principle [28]. The first subcategory is the key/value pairbased, where every value corresponds to a key. The second subcategory is the column-based where every column of a dataset is stored separately [29]. The third one is the graph-based, where data is stored and represented as a graph [30], [31]. ...
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... However, such systems are intended to innovate in the field of Big Data management by providing optimal solutions to data analysts. A representative example of a system that is providing optimal solutions to its users is the Diastema Big Data analytics platform [100,101], which is providing a set of efficient and scalable components that provide user-friendly analytics via graph data modeling and supporting technical and non-technical stakeholders. In the same context, by exploiting Big Data processing and analytics tools and technologies, the PolicyCLOUD data-driven platform exploits added-value of analytics over various datasets to obtain actionable insights and drive decision making [102]. ...
Big Data is a phenomenon that affects today’s world, with new data being generated every second. Today’s enterprises face major challenges from the increasingly diverse data, as well as from indexing, searching, and analyzing such enormous amounts of data. In this context, several frameworks and libraries for processing and analyzing Big Data exist. Among those frameworks Hadoop MapReduce, Mahout, Spark, and MLlib appear to be the most popular, although it is unclear which of them best suits and performs in various data processing and analysis scenarios. This paper proposes EverAnalyzer, a self-adjustable Big Data management platform built to fill this gap by exploiting all of these frameworks. The platform is able to collect data both in a streaming and in a batch manner, utilizing the metadata obtained from its users’ processing and analytical processes applied to the collected data. Based on this metadata, the platform recommends the optimum framework for the data processing/analytical activities that the users aim to execute. To verify the platform’s efficiency, numerous experiments were carried out using 30 diverse datasets related to various diseases. The results revealed that EverAnalyzer correctly suggested the optimum framework in 80% of the cases, indicating that the platform made the best selections in the majority of the experiments.
