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The role of databases is to allow for the persistence of data, no matter if they are of the SQL or NoSQL type. In SQL databases, data are structured in a set of tables in the relational database model, grouped in rows and columns. CRUD operations (create, read, update, and delete) are used to manage the information contained in relational databases. Several dialects of the SQL language exist, as well as frameworks for mapping Java classes (models) to a relational database. The question is what we should choose for our Java application, and why? A comparison of the most frequently used relational database management systems, mixed with the most frequently used frameworks should give us some guidance about when to use what. The evaluation is conducted based on the time taken for each CRUD operation to run, from thousands to hundreds of thousands of entries, using the possible combinations in the relational database system and the framework. Aiming to assess and improve the performance, the experiments included the possibility of warming-up the Java Virtual Machine before the execution of queries. Also, the research investigated the time spent using different methods of code to determine the critical regions (bottlenecks). Thus, the conclusions provide a comprehensive overview of the performances of Java applications accessing databases depending on the suite decisions considering the database type, the framework in use, and the type of operation, with clear comparisons between the alternatives, the key findings of the advantages and drawbacks of each of them, and supporting architects and developers in their technological decisions and improving the speed of their programs. [ABSTRACT FROM AUTHOR]

Engineering or humanities data are stored in databases and are often used for search services. While the latest deep-learning technologies, such like BART and BERT, are utilized for data analysis, humanities data still rely on traditional databases. Representative analysis methods include n-gram and lexical statistical extraction. However, when using a database, performance limitation is often imposed on the result calculations. This study presents an experimental process using MariaDB on a PC, which is easily accessible in a laboratory, to analyze the impact of the database on data analysis performance. The findings highlight the fact that the database becomes a bottleneck when analyzing large-scale text data, particularly over hundreds of thousands of records. To address this issue, a method was proposed to provide real-time humanities data analysis web services by leveraging the open source database, with a focus on the Seungjeongwon-Ilgy, one of the largest datasets in the humanities fields. [ABSTRACT FROM AUTHOR]

Databases are critical for storing structured data, but deriving insights remains challenging. This paper investigates integrating classification, clustering, association rules, and anomaly detection within database architectures to enable intelligent analytics. A unified architecture is proposed along with an asynchronous incremental learning technique to efficiently handle dynamic data. Comprehensive experiments on diverse real-world datasets demonstrate 10-25% improvements in metrics like query latency, accuracy, and costs compared to conventional integration approaches. Emerging applications in multimedia, spatiotemporal, and IoT mining are discussed. The holistic convergence of multiple techniques is highlighted as the key innovation in progressing towards next-generation intelligent databases powered by analytics. [ABSTRACT FROM AUTHOR]

In a world rich in interconnected and complex data, the non-relational database paradigm can better handle large volumes of data at high speed with a scale-out architecture, which are two essential requirements for large industries and world-class applications. This article presents AMANDA, a flexible middleware for automatic migration between relational and non-relational databases based on a user-defined schema that offers support for multiple sources and target databases. We evaluate the performance of AMANDA by assessing the migration speed, query execution, query performance, and migration correctness, from two Relational Database Management Systems (RBMSs), i.e., Postgres and MySQL, to a non-relational database (NoSQL), i.e., DGpraph. The results show that AMANDA successfully migrates data 26 times faster than previous approaches, when considering Northwind. Regarding the IMDB database, it took 7 days to migrate 5.5 GB of data. [ABSTRACT FROM AUTHOR]

The authors updated their 2009 article on the MLA International Bibliography (MLAIB) by comparing it across three vendor platforms to determine if seemingly comparable searches continued to produce varying results. As much as possible, the authors tested the same searches as were tested in the original article and added additional ones for further exploration. Findings illustrate that while there are fewer differences than was the case previously, differences still exist. The authors also investigated discovery services to identify factors affecting MLAIB searches. The authors discuss the impact of their findings on users, from undergraduates to faculty researchers. [ABSTRACT FROM PUBLISHER]

Datacenters house massive databases and applications to provide business decision support and cloud services where commercial success is contingent on timely responses. The servers that these tasks run on dissipate a lot of power, requiring equally powerful cooling systems to maintain a safe and efficient temperature level. In a typical enterprise server, server chassis fans can generate vibrations that are powerful enough to degrade the performance of data-intensive workloads. Our methodology measures and reproduces real-life vibrations on a rack server to evaluate the performance of different hard disks. Effective hardware management relies on an accurate understanding of these devices and their interactions to mitigate any performance degradation and meet thermal constraints. While current strategies focus on managing processing resources, at the expense of more data-dependent workloads, this paper approaches server efficiency by targeting the cooling-performance relationship in conjunction with other dependencies between power, thermal, and cooling. We extract a model from common database benchmarks based on expected resource utilization and corresponding cooling needs, while considering these mechanical disturbances. Our proposed strategy uses convex optimization to maintain thermal constraints at all times, while reducing the energy consumption of a server by 65% compared to basic proportional-integral-derivative controllers, or by 19% in comparison to advanced hardware management techniques proposed in literature. [ABSTRACT FROM AUTHOR]

The need fo r achieving optimal performance for database applications is a primary objective for database designers and a primary requirement fo r database end users. Partitioning is one of the techniques used by designers to improve the performance of database access. The purpose of this study was to investigate the effect of horizontal table partitioning on query Response Time (RT) using three partitioning strategies: Zero partitioning, list partitioning and range partitioning. Three tables extracted from the Student Information System (SIS) at Yarmouk University in Jordan were used in this research. Variation in table size was used to determine when partitioning can have an impact (if any) on access performance. A set of 12 queries were run over a database of three different sizes. The results indicated that partitioning provided better RT than zero partitioning, on the other hand, range and list partitioning strategies showed little performance differences with the different database sizes. [ABSTRACT FROM AUTHOR]

A database system includes a set of different hardware and software resources with a large number of configuration parameters that affect and control the performance of database systems. Tuning these parameters within their diverse and complex environments requires a lot of expertise and it is a time-consuming, and often a misdirected process. Furthermore, tuning attempts often lack a methodology that has a holistic view of the database system. Therefore, in this paper, we introduce MAG , a layer-by-layer tuning framework that can be used to monitor , analyze , predict , and control database configuration parameters. In particular, the framework comprises three main components: NN M onitor predicts the system performance based on its given parameters; A nalyzer identifies and determines the source of the problem and then directs to deal with the problem; and NN G enerator generates the experimental training sets exploited in training NN M onitor . The proposed approach focuses on the root causes of database performance problems. The approach further strives avoiding the repetitive trial-and-error process that is a characteristic of a lot of performance-tuning efforts. We experimentally demonstrate the effectiveness of the proposed framework through an extensive set of evaluations. [ABSTRACT FROM AUTHOR]

The performance of database operations can be enhanced with an efficient storage structure design using attribute partitioning and/or tuple clustering. Previous research deals mostly with attribute partitioning. We address here the combined problem of attribute partitioning and tuple clustering. We propose a novel approach for this mixed fragmentation problem by applying a genetic algorithm iteratively to attribute partitioning and tuple clustering sub-problems. We compared our results to attribute-only partitioning and random search solution, resulting in a database access cost reduction of upto 70% and 67% respectively. We analyzed the effect of varying genetic parameters on the optimal solution through experimentation. [ABSTRACT FROM AUTHOR]

This article investigates MySQL's index capabilities. It begins by reviewing how indexes work, as well as their structure. Next, it reviews indexing features specific to each of the major MySQL data storage engines. This article then examines a broad range of situations in which indexes might help speed up your application. In addition to examining how indexes can be of assistance. In this article we present index usage type: B- trees, hash and bitmap, in order to optimize queries, although MySQL has implemented and indexes spacious R-trees. The index type corresponds to the particular kinds of internal algorithms and datastructures used to implement the index. In MySQL, support for a particular index type is dependent upon the storage engine. [ABSTRACT FROM AUTHOR]

In this article, the authors compared the MLA International Bibliography database (MLAIB) across five vendor platforms to determine how and why seemingly comparable searches produced varying results. A number of tests were conducted using a variety of fields, terms, and parameters, and examples of results are provided. The authors' findings are designed to illustrate the impact of these differences on users, from undergraduates to faculty researchers, whether they work individually or collaboratively and regardless of the platform to which their institution subscribes. [ABSTRACT FROM AUTHOR]

The optimal selection of secondary indexes asks for the quantitative evaluation of the performance of a number of candidate secondary indexes in order to determine the particular combination of indexes which satisfies the anticipated user transactions at a minimal cost. Previous studies determine the optimal selection by assuming that the cost of satisfying a query using a secondary index is not affected by the existence of other indexes in the database. This assumption is realistic when the inverted file organization is used to organize secondary indexes. The main reason is that inverted files do not alter the size of the file. However, the assumption is not valid when the next most popular method for structuring secondary indexes is used, namely, the multilist database structures. This is so, because each multilist increases the size of the file. This paper studies the secondary index selection problem by making the assumption that the multilist organization is utilized to structure secondary indexes and develops a dynamic programming algorithm to solve it. The practical significance of the study lies in the fact that multilists can be easily implemented on network databases. [ABSTRACT FROM AUTHOR]