Search

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]

Today's evolution of the software development imposing demands and the everincreasing competitivity between applications, considering not only their aesthetical appearance or ease of use, but also through continuously boosting their performance and suppressing their operation latency. This paper analyzes the resilience of architectural mechanisms for data persistence, to ensure the security of an information system against cyberattacks and preventing the disruption in the integrity and availability of the stored data. The study consists of a Fault Tree Analysis that underlines various design and implementation flaws, caused by the most common mistakes introduced by the software development teams in worldwide information systems. Solutions are proposed for every triggering event, minimizing the impact on the operations' efficiency and decreasing transactions' latency. [ABSTRACT FROM AUTHOR]

Relational databases are characterized by precise data model, formed by the entities and relationships. Integrity and consistency is maintained by the transactions, which ensure transfer from one consistent state to another. Consistency is ensured at the latest at the end of the transaction itself. SQL norm specifies two lock types applied on the data row level to serve the consistency in the parallel data access. However, Oracle Database uses a different approach relying on the transaction logs to provide historical consistent data image. In this paper, Reservable attributes are discussed, introduced in Oracle 23c version by focusing on the performance, limitation and general issues.

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]

Oracle, MSSQL, MySQL and PostgreSQL are four of the most popular relational databases. They are often used in internet applications. This paper aims to compare the efficiency of these technologies in terms of speed using virtualization with Docker. No publications that include virtualization were found among previous papers. After review of the literature, it was hypothesized that the Oracle engine would be the fastest. During the research, a series of experiments was carried out using the application, in which tests for measuring the time of instruction execution were implemented. Each query was measured 100 times and the first measurement was rejected. The obtained results confirmed the hypothesis about the superiority of the Oracle database. As in previous studies, it proved to be the fastest, also using virtualization.

The Buildings Performance Database (BPD) includes empirically measured data from a variety of data sources with varying degrees of data quality and data availability. The purpose of the data preparation process is to maintain data quality within the database and to ensure that all database entries have sufficient data for meaningful analysis and for the database API. Data preparation is a systematic process of mapping data into the Building Energy Data Exchange Specification (BEDES), cleansing data using a set of criteria and rules of thumb, and deriving values such as energy totals and dominant asset types. The data preparation process takes the most amount of effort and time therefore most of the cleansing process has been automated. The process also needs to adapt as more data is contributed to the BPD and as building technologies over time. The data preparation process is an essential step between data contributed by providers and data published to the public inthe BPD.

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.

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]

The main purpose of this article is to check which database: MySQL, MS SQL, PostgerSQL is the most efficient for Internet applications. This work contains information about the databases used, but the most important part of this article is database performance research. They are based on an application whose main task was database queries. The program was created based on new technologies, such as the Spring framework, the Hibernate library and JDBC Interface.

The object-relational impedance mismatch (ORIM) problem characterises differences between the object-oriented and relational approaches to data access. Queries generated by object-relational mapping (ORM) frameworks are designed to overcome ORIM difficulties and can cause performance concerns in environments which use object-oriented paradigms. The aim of this paper is twofold, first presenting a survey of database practitioners on the effectiveness of ORM tools followed by an experimental investigation into the extent of operational concerns through the comparison of ORM-generated query performance and SQL query performance with a benchmark data set. The results show there are perceived difficulties in tuning ORM tools and distrust around their effectiveness. Through experimental testing, these views are validated by demonstrating that ORMs exhibit performance issues to the detriment of the query and the overall scalability of the ORM-led approach. Future work on establishing a system to support the query optimiser when parsing and preparing ORM-generated queries is outlined. [ABSTRACT FROM AUTHOR]

The popularity of NoSQL databases has increased due to the need of (1) processing vast amount of data faster than the relational database management systems by taking the advantage of highly scalable architecture, (2) flexible (schema-free) data structure, and, (3) low latency and high performance. Despite that memory usage is not major criteria to evaluate performance of algorithms, since these databases serve the data from memory, their memory usages are also experimented alongside the time taken to complete each operation in the paper to reveal which one uses the memory most efficiently. Currently there exists over 225 NoSQL databases that provide different features and characteristics. So it is necessary to reveal which one provides better performance for different data operations. In this paper, we experiment the widely used in-memory databases to measure their performance in terms of (1) the time taken to complete operations, and (2) how efficiently they use memory during operations. As per the results reported in this paper, there is no database that provides the best performance for all data operations. It is also proved that even though a RDMS stores its data in memory, its overall performance is worse than NoSQL databases.

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]

Copyright of Journal of Polytechnic is the property of Journal of Polytechnic and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Technology enables millennials to remain "plugged in" via an array of platforms that provide an incessant flow of information. Despite these virtual connections, many undergraduate students experience challenges in analyzing data from the web, often selecting popular search engines to analyze business concepts. As the internet continues to shape the scope of digital content available to students, factors that influence student selection and use of relevant databases are important considerations for faculty in business schools. This survey of undergraduate students in an introductory business course found that performance features, along with ease of use, were primary factors influencing database selection. [ABSTRACT FROM AUTHOR]

The popularity of NoSQL databases has increased due to the need of (1) processing vast amount of data faster than the relational database management systems by taking the advantage of highly scalable architecture, (2) flexible (schema-free) data structure, and, (3) low latency and high performance. Despite that memory usage is not major criteria to evaluate performance of algorithms, since these databases serve the data from memory, their memory usages are also experimented alongside the time taken to complete each operation in the paper to reveal which one uses the memory most efficiently. Currently there exists over 225 NoSQL databases that provide different features and characteristics. So it is necessary to reveal which one provides better performance for different data operations. In this paper, we experiment the widely used in-memory databases to measure their performance in terms of (1) the time taken to complete operations, and (2) how efficiently they use memory during operations. As per the results reported in this paper, there is no database that provides the best performance for all data operations. It is also proved that even though a RDMS stores its data in memory, its overall performance is worse than NoSQL databases. [ABSTRACT FROM AUTHOR]