Performance Research of Object-Relational Mapping Technologies in Interaction to MySQL

In the study of marine geology, the sampling technology and preservation are very important links. Marine geology is developed on the basis of marine sediment sampling technology. ORM (Object-Relation Mapping) technology is an effective method to solve the “impedance mismatch” between object model and relation model. The research on object-relation mapping technology has become a research hotspot in the field of software architecture. The development of object-relational database has enhanced the ability of database to support complex data types, making the integrated storage of spatial data, topological relations and attribute data a reality. This paper proposes a class hierarchy based on the technical framework of object-relational mapping, and explains all kinds of classes. The application of this framework can simplify the mapping mechanism on the premise of meeting the requirements of marine geological sampling, thus providing users with an object-oriented way to intuitively access the database.

El presente artículo surgió como una iniciativa académica en la cual se observa que las áreas de conocimiento en el desarrollo de software bajo el paradigma de Programación Orientada a Objetos - POO está confrontado por un modelo de almacenamiento de datos de tipo relacional lo que plantea dos escenarios diferentes que los desarrolladores tratan de mitigar a través de conversiones entre tipos o utilizando herramientas intermedias como el mapeo de objetos relacional que traen ciertas ventajas y desventajas, y por lo cual, se planteo dentro del proyecto la posibilidad de utilizar un motorde almacenamiento de tipo no relacional o NoSQL. Con el diseño y desarrollo del framework para generar aplicaciones web, el usuario podrá definir los objetos que considere incluir en la aplicación, los cuales se almacenarán en el motor MongoDB, el cual, organiza los datos en forma de documentos. La estructura dinámica de estos documentos se puede utilizar en gran cantidad de proyectos, incluyendo muchos que tradicionalmente funcionarían sobre bases de datos relacionales. Con el objetivo de socializar y evaluar el trabajo realizado, se diseñaron algunos instrumentos para recopilar información de usuarios con experiencia en el sector de las bases de datos y el desarrollo de software. Como resultado se resalta que los desarrolladores de software tienen claro los conceptos de persistencia de objetos a través del mapeo objeto relacional (ORM), que el aprendizaje de estas técnicas de desarrollo de software a través de la implementación de código propio o de la utilización de API´s tiene un grado alto de complejidad y en su mayoría (un 60%) son conscientes que estas implementaciones generan un bajo rendimiento en las aplicaciones. Además, se resalta la apertura de estos a optar por otras alternativas para organizar y almacenar la información, diferentes al enfoque relacional utilizado desde hace varios años.

This research focuses on the development and implementation of a web-based citizen data management system in Keboan Anom Village, Sidoarjo, Indonesia. The aim of the study is to enhance the efficiency and effectiveness of citizen data recording and management processes. The System Development Life Cycle (SDLC) Waterfall model is employed as the methodology for system development, ensuring an organized and systematic approach. Data collection involves semi-structured interviews and unstructured observations, targeting the village's RT 01 (neighborhood association) officials. The application features two key functions: citizen registration, including permanent and non-permanent residents, and a comprehensive data summary. Access to these functions is limited to the RT Secretary and the RT Chairman. The implementation of the system provides improved data management and financial tracking capabilities, enabling simplified reporting. The web-based platform ensures accessibility for RT officials and residents with internet connectivity. Black box testing confirms the functionality of the website and its compliance with expected outputs. The results demonstrate that the application supports the transformation of Keboan Anom Village into a more efficient and technologically connected Digital Village. Future research is suggested to examine community participation levels in using the citizen data management application and its impact on decision-making and overall village governance, incorporating participatory approaches to understand the perspectives and active involvement of Keboan Anom Village residents.
 Highlight:
 
 Efficient citizen data management: The development of a web-based application for citizen data management in Desa Keboan Anom enables efficient recording and documentation of resident information, improving data accuracy and accessibility.
 Technological transformation: The implementation of the web-based system contributes to the realization of a Digital Village concept, facilitating efficient and technology-connected administration in Desa Keboan Anom, Sidoarjo.
 Participatory approach: Future research should focus on evaluating community participation levels and the impact of the citizen data management application on decision-making and overall village governance, utilizing a participatory approach to gain insights and foster active involvement of Keboan Anom Village residents.
 
 Keyword:
 Keboan Anom Village, Citizen Data Management, Web-Based Application, Efficiency, Participatory Approach

In the realm of data analytics, Power BI and SQL serve as essential tools for businesses aiming to derive actionable insights from their data. However, as datasets grow in complexity and volume, the efficiency of queries and the speed of data loading become critical factors affecting overall performance. This paper explores advanced performance tuning techniques for both Power BI and SQL, focusing on enhancing query efficiency and minimizing data load times. We investigate key strategies, including the optimization of data models, effective use of DAX (Data Analysis Expressions) functions, and the implementation of indexing and partitioning in SQL databases. Additionally, we analyze the impact of data source configurations and the importance of using best practices in report design to improve user experience. By conducting empirical tests and case studies, we illustrate how these tuning methods can lead to significant improvements in report rendering times and user interactions. Ultimately, our findings emphasize the need for a holistic approach to performance tuning, combining technical optimizations with best practices in data management. This research provides valuable insights for data professionals seeking to enhance the efficiency of their Power BI reports and SQL queries, ensuring that organizations can make data-driven decisions swiftly and effectively. The outcomes of this study not only contribute to the field of business intelligence but also pave the way for future research on optimizing analytics platforms.

Performance prediction and adaptation for database management system workload using Case-Based Reasoning approach

Qualitative data analysis in academic research is a challenge. In this context, the use of tools such as ATLAS.ti has emerged as a potential solution to improve the understanding and management of data in the analysis of in-depth interviews. The main objective of the research was to analyze the perspectives of Tourism and Hospitality Management students on the use of ATLAS.ti in the analysis of interviews in qualitative research. The methodology employs a qualitative approach and a descriptive-interpretative design. Data were collected through in-depth interviews and focus groups directed to 40 students of the X cycle who conducted this approach in their research to opt for the bachelor’s degree in Tourism and Hospitality Administration during the years 2022 and 2023. The findings reveal that the use of ATLAS.ti in qualitative data analysis is highly beneficial, facilitating the coding, organization, and identification of emerging patterns in in-depth interviews. The relevance of its effective use in qualitative analysis is highlighted, improving data management, and understanding of participants' perspectives. It is concluded that it is a valuable and effective tool in this context, although the need for researchers to acquire a deep understanding of the tool and receive adequate training is emphasized. It is suggested that they focus on continuous training in its use and constant practice of its advanced functionalities, especially in areas such as coding and code creation, to achieve a deeper interpretation of qualitative data

Digitalization of biocatalysis: Best practices to research data management.

Artificial intelligence (AI) has demonstrated remarkable potential in transforming medical diagnostics across various healthcare domains. This paper explores AI applications in cancer detection, dental medicine, brain tumor database management, and personalized treatment planning. AI technologies such as machine learning and deep learning have enhanced diagnostic accuracy, improved data management, and facilitated personalized treatment strategies. In cancer detection, AI-driven imaging analysis aids in early diagnosis and precise treatment decisions. In dental healthcare, AI applications improve oral disease detection, treatment planning, and workflow efficiency. AI-powered brain tumor databases streamline medical data management, enhancing diagnostic precision and research outcomes. Personalized treatment planning benefits from AI algorithms that analyze genetic, clinical, and lifestyle data to recommend tailored interventions. Despite these advancements, AI integration faces challenges related to data privacy, algorithm bias, and regulatory concerns. Addressing these issues requires improved data governance, ethical frameworks, and interdisciplinary collaboration among healthcare professionals, researchers, and policymakers. Through comprehensive validation, educational initiatives, and standardized protocols, AI adoption in healthcare can enhance patient outcomes and optimize clinical decision-making, advancing the future of precision medicine and personalized care.

SUMMARYThe use of real-world data from national TB care programs has great potential to answer key research questions in TB control and is now opportune due to increasing digital data collection and storage. We summarize an expert stakeholder workshop conducted on this topic in October 2019, with perspectives from academics, national TB program officers, and data managers. We discuss challenges and opportunities in the use of TB programmatic data for research and describe digital data availability in two large, high TB burden countries, Brazil and South Africa. From this, we posit that with a standardized data collection set, improved data management, and greater collaboration, more TB programmatic data can be used for research with measurable public health impact.

In the digital era, information technology is crucial for daily activities, enhancing efficiency and effectiveness. This transformation is evident in the business sector, where digitalization boosts data accuracy, reduces human error, and improves productivity and quality. For busy societies, laundry services like Lc Jaya Clean provide practical solutions for washing and caring for clothes. Utilizing a web-based laundry application system and the Rapid Application Development (RAD) method, Lc Jaya Clean enhances data management, customer interaction, and service flexibility. The study employs the RAD method, comprising requirements planning, system design, and implementation stages, facilitating quick prototyping, iterative testing, and user feedback. The successful development of the web- based laundry application used the Laravel framework and PHP, with Visual Studio Code, XAMPP, Figma, StarUML, and Draw.io for coding and design. The RAD process and user feedback produced a user-friendly, tailored application, improving data management and business processes for Lc Jaya Clean.

This study aimed to develop the KAMI 4.1 Index system application based on web application technology to provide a platform for controlled audit implementation and improve data management. The primary goals were to independently assess organizations' ability to obtain ISO 27001:2013 and enhance the audit process's effectiveness and efficiency. The research utilized web application technologies as materials. It employed a systematic approach, focusing on developing a web-based application using the waterfall model's stages of communication, planning, modeling, construction, and deployment. The resulting KAMI 4.1 Index system application introduced a new and efficient platform for controlled audit implementation, featuring an improved user experience and enhanced ease of use by incorporating existing audit calculations from the KAMI 4.1 index. Evaluation based on the ISO 25010:2011 quality of use model yielded a high total quality of use rate of 81.45%, indicating a "very good" categorization. However, areas requiring further research and improvement were identified, including data security, content coverage, freedom from risk, and error tracking. The study also suggested exploring integration possibilities of the audit system with other ISO audit needs, such as a quality assurance system complying with ISO 9001. Further research is necessary to gather information about user criteria and needs in different organizational contexts, ensuring the audit application system meets their requirements. Overall, this research contributes to developing the KAMI 4.1 Index system application and highlights directions for further enhancement and exploration in controlled audit implementation and data management.

The Interstate Conference on Water Policy has released an Executive Report of the 1989 Ground Water Information Management Workshops. The report summarizes workgroup findings and recommendations for action as identified at the four workshops conducted in the winter and spring of 1989 in Little Rock, Ark.; Sacramento, Calif.; Harrisburg, Pa.; and Omaha, Nebr. The workshops, cosponsored by ICWP and the U.S. Geological Survey, attracted over 200 participants from local, state, and federal government, academia, and the private sector.The two primary objectives of the workshop series were to provide participants with information about groundwater data management initiatives at all levels of government, and to elicit information and ideas from participants about improving data management and exchange. The report states that although the individual workshops reflected regional concerns and experiences, collectively they provide a solid foundation for developing a national perspective on groundwater information management needs.

This paper describes initial experiences with semi-automated performance tuning of a sparse linear solver in LS-DYNA, a large, widely used engineering application. Through a collection of tools supporting empirical optimization, we alleviate the burden of performance tuning for mapping today's sophisticated engineering software to increasingly complex hardware platforms. We describe a tool that automatically isolates code segments to create benchmark subsets for the purposes of performance tuning. We present a collection of automatically generated empirical results that demonstrate the sensitivity of the application's performance to optimization parameters. Through this case study, we demonstrate the importance of developing automatic performance tuning support for performance-sensitive applications.

New generation of health care IT systems are collecting and storing more and more data of patients. Useful knowledge can be extracted from the data in EMR or PHR to provide medical advises to patients, while through data analysis the result statistics can be used to support the scientific research. However, RDBMSs-based framework is not able to support the requirements of massive health care data storage, management and analysis. To solve the problem, this paper proposes a massive data management and analysis solution based on Hadoop to archive better performance, scalability and fault tolerance. The data management framework is presented. Besides, 2 different data analysis methods based on MapReduce and Hive are proposed. Experiment results of data upload, data query and data analysis show that the performance of the proposed framework is greatly improved, and a brief summary of the performance and the differences between 2 methods of MapReduce and Hive is also discussed.

With the increasing trend of putting more cores inside a single chip, more clusters adapt multicore multiprocessor nodes for high-performance computing (HPC). Cache coherent non-uniform memory access architectures (ccNUMA) are becoming an increasingly popular choice for such systems. In this paper, application performance analysis is provided using a 2312 Opteron cores system based on Sun Fire servers. Performance bottlenecks are identified and some potential solutions are proposed. With the proposed performance tunings, up to 30% application performance improvement was observed. In addition, provided experimental analysis can be utilized by HPC application developers in order to better understand clusters with ccNUMA nodes and also as a guideline for the usage of such architectures for scientific computing.