Efficient Data Management Research Articles

Transformative Frontiers: A Comprehensive Review of Emerging Technologies in Modern Healthcare.

The rapid evolution of emerging technologies in healthcare is reshaping the field of medical practices and patient outcomes, ushering in an era of unprecedented innovation. This narrative review touches upon the transformative impacts of various technologies, including virtual reality (VR), augmented reality (AR), the internet of medical things (IoMT), remote patient monitoring (RPM), financial technology (fintech) integration, cloud migration, and the pivotal role of machine learning (ML). It emphasizes the collaborative impact of these technologies, which is reshaping the healthcare landscape. Virtual reality and AR revolutionize medical training, IoMT extends healthcare boundaries, RPM facilitates proactive care, and fintech integration enhances financial processes. Cloud migration ensures scalable and efficient data management, while ML harnesses algorithms for diagnostic precision and personalized treatment.

Monitoring the psychological component of higher education quality using ICT tools

This study examines the use of information and communication technologies (ICT) to monitor the psychological component of higher education quality. A literature review covers the concepts of higher education quality and its monitoring, the psychological factors involved, and the potential of ICT tools, including Google services. The study details a monitoring procedure using Google Forms to assess indicators like relationships between participants, satisfaction levels, and organisational climate. The computer program “Personnel – Ψ” is also presented as a tool for comprehensive psychological diagnostics of teachers. The advantages of using ICT tools for efficient data collection, analysis and management are discussed. The results demonstrate the feasibility and effectiveness of using ICT to improve monitoring of the psychological aspects of educational quality and make evidence-based decisions to enhance the educational environment. The use of user-friendly tools like Google Forms facilitates regular assessment of quality indicators and aids institutions in ensuring participant satisfaction and optimal conditions for learning and growth.

Short Communication: Principles of Health Data Stewardship in Public Health Epidemiology - A Rapid Overview

This short communication discusses the importance of managing health data to enhance public health outcomes while emphasizing the significance of data privacy and accuracy. It presents guidelines for the handling and utilization of data covering aspects such as data quality, security, privacy, sharing, ethics, governance, transparency, accountability and sustainability. By highlighting the value of high-quality data, we advocate for leveraging Health Information Technology (HIT) solutions, like Electronic Health Records (EHRs), to improve data integrity. Efficient data management supports health initiatives like analyzing disease patterns and designing interventions underscoring its role in decision making and formulating public health strategies. Keywords: Health Data Stewardship; Public Health Epidemiology; Data Quality; Ethical Use; Health Information Technology

Sosialisasi dan Pelatihan Sistem Informasi Manajemen Pemberdayaan Kesejahteraan Keluarga Terintegrasi Berbasis Kampung

Empowering family well-being in rural communities is a crucial goal in sustainable development. One emerging innovation is the Village-Based Integrated Family Welfare Information System (VBIFWIS). This study delves into the effectiveness of the socialization and training of VBIFWIS in addressing the challenges of family empowerment in rural areas. The findings indicate that adequate socialization and targeted training can enhance the community's understanding of the benefits of VBIFWIS, improve access to services, and empower individuals in system utilization. However, initial challenges such as technology resistance and the learning curve necessitate a sustained approach. These findings provide valuable insights into how VBIFWIS can effectively empower family well-being in rural environments, emphasizing the significance of community participation, efficient data management, and addressing technological challenges.

Loopholes in the Indian Banking System concerning Non-Performing Assets (NPAs) – A Case Study of “Axis Bank and HDFC Bank”

This paper presents the development and implementation of an innovative IoT-based Surveillance Robot designed for remote control, utilizing a Wi-Fi module and a smartphone interface. The portable espionage robot integrates cutting-edge technology, featuring an AI Thinker ESP32-CAM module with an ESP32-S processor, an OV2640 camera, motor drivers, and a robust chassis with two moving wheels. The ESP32-CAM allows the capture and storage of images on a microSD card, facilitating efficient data management. The robot employs the HTTP Communication protocol, enabling users to access live video streaming from the OV2640 camera through a standard web browser. The web interface includes intuitive directional buttons for seamless control, allowing users to command the robot to move left, right, forward, and backward. The locomotion of the robot is achieved through two DC motors integrated with a Robot chassis and an L293D motor driver module. The real-time video feed is transmitted wirelessly to the user's smartphone over a Wi-Fi infrastructure, providing a live- streaming experience. This wireless surveillance robot finds applications in various scenarios such as war fields, disaster management, and beyond, showcasing its versatility in remote monitoring and reconnaissance tasks. Keywords: IoT, Surveillance Robot, Wireless Control, ESP32- CAM, HTTP Communication, Video Streaming, Smartphone Control, Disaster Management, Robotics, Remote Monitoring, Reconnaissance.

Best practices for data management and sharing in experimental biomedical research.

Effective data management is crucial for scientific integrity and reproducibility, a cornerstone of scientific progress. Well-organized and well-documented data enable validation and building on results. Data management encompasses activities including organization, documentation, storage, sharing, and preservation. Robust data management establishes credibility, fostering trust within the scientific community and benefiting researchers' careers. In experimental biomedicine, comprehensive data management is vital due to the typically intricate protocols, extensive metadata, and large datasets. Low-throughput experiments, in particular, require careful management to address variations and errors in protocols and raw data quality. Transparent and accountable research practices rely on accurate documentation of procedures, data collection, and analysis methods. Proper data management ensures long-term preservation and accessibility of valuable datasets. Well-managed data can be revisited, contributing to cumulative knowledge and potential new discoveries. Publicly funded research has an added responsibility for transparency, resource allocation, and avoiding redundancy. Meeting funding agency expectations increasingly requires rigorous methodologies, adherence to standards, comprehensive documentation, and widespread sharing of data, code, and other auxiliary resources. This review provides critical insights into raw and processed data, metadata, high-throughput versus low-throughput datasets, a common language for documentation, experimental and reporting guidelines, efficient data management systems, sharing practices, and relevant repositories. We systematically present available resources and optimal practices for wide use by experimental biomedical researchers.

Simplifying Multimodal Clinical Research Data Management: Introducing an Integrated and User-friendly Database Concept.

Clinical research, particularly in scientific data, grapples with the efficient management of multimodal and longitudinal clinical data. Especially in neuroscience, the volume of heterogeneous longitudinal data challenges researchers. While current research data management systems offer rich functionality, they suffer from architectural complexity that makes them difficult to install and maintain and require extensive user training. The focus is the development and presentation of a data management approach specifically tailored for clinical researchers involved in active patient care, especially in the neuroscientific environment of German university hospitals. Our design considers the implementation of FAIR (Findable, Accessible, Interoperable, and Reusable) principles and the secure handling of sensitive data in compliance with the General Data Protection Regulation. We introduce a streamlined database concept, featuring an intuitive graphical interface built on Hypertext Markup Language revision 5 (HTML5)/Cascading Style Sheets (CSS) technology. The system can be effortlessly deployed within local networks, that is, in Microsoft Windows 10 environments. Our design incorporates FAIR principles for effective data management. Moreover, we have streamlined data interchange through established standards like HL7 Clinical Document Architecture (CDA). To ensure data integrity, we have integrated real-time validation mechanisms that cover data type, plausibility, and Clinical Quality Language logic during data import and entry. We have developed and evaluated our concept with clinicians using a sample dataset of subjects who visited our memory clinic over a 3-year period and collected several multimodal clinical parameters. A notable advantage is the unified data matrix, which simplifies data aggregation, anonymization, and export. THIS STREAMLINES DATA EXCHANGE AND ENHANCES DATABASE INTEGRATION WITH PLATFORMS LIKE KONSTANZ INFORMATION MINER (KNIME): . Our approach offers a significant advancement for capturing and managing clinical research data, specifically tailored for small-scale initiatives operating within limited information technology (IT) infrastructures. It is designed for immediate, hassle-free deployment by clinicians and researchers.The database template and precompiled versions of the user interface are available at: https://github.com/stebro01/research_database_sqlite_i2b2.git.

Sales Analysis on Garment Industry with Datawarehouse and ETL Implementation on Star Schema

In the current industrial era, clothing companies face increasingly complex challenges in maintaining the quality and sales of their products. To overcome these obstacles, companies must have a good vision, mission, and decision-making capabilities supported by efficient data management. Data is becoming a valuable asset in Industry 4.0, pivotal in process management, increased productivity, and competitive advantage. This research explores the implementation of Data Warehouse (data warehouse) and ETL (Extraction, Transformation, and Load) processes on star schema for PT Golden Flower Tbk. A data warehouse is a container for storing company data, supporting decision-making, and providing business insights. Meanwhile, the ETL process ensures that the data entered into the warehouse is clean, structured, and ready for analysis. PTA garment manufacturing and exporter company, USPT Golden Flower Tbk, uses the Pentaho application to carry out the ETL process. Pentaho, a Business Intelligence tool, facilitates easy data processing and analysis. This research also explains the use of dimension tables and fact tables in star schemas, which form the basis for more in-depth data analysis. Through visualization and analysis using star schema, Mondrian, and schema workbench, companies can identify sales patterns in each branch and understand which products are most in demand by consumers. The results of the star schema, moMondrianand schema workbench provide a better understanding of the company's sales performance, enabling more informed decision-making and more effective strategies.

Analysis of Blockchain Technology in Patient Data Management System : Security, Privacy, and Efficiency in the Digital Healthcare Context

In the rapidly evolving landscape of digital healthcare, ensuring secure, confidential, and efficient patient data management has become paramount. Blockchain technology has emerged as a promising solution to address these challenges. This study aims to analyze the implementation of blockchain technology within patient data management systems, with a specific focus on security, privacy, and efficiency in the context of digital healthcare. Within this framework, a notable gap arises between technological innovation trends and their practical application within the healthcare sector. This research expands our understanding of how blockchain can be effectively harnessed to tackle security and patient data privacy challenges within the increasingly interconnected digital healthcare environment. Employing a comprehensive methodology that encompasses qualitative and quantitative approaches, including surveys, literature analysis, and case studies, this study delves into how blockchain technology can provide an added layer of security to patient data, enhance privacy through improved access control, and streamline the sharing of health information. The novelty of this research lies in its holistic approach to identifying gaps in blockchain technology application methods within patient data management, while also addressing practical challenges inherent to the digital healthcare context. The anticipated outcomes of this analysis will provide valuable insights for decision-makers aiming to implement blockchain technology to fortify data security, safeguard privacy, and enhance efficiency within patient data management systems. In conclusion, this research contributes to a nuanced comprehension of the potential of blockchain technology to augment security, privacy, and efficiency in patient data management. By shedding light on innovative methods like blockchain integration, this study offers valuable guidance for effectively navigating the dynamic landscape of digital healthcare.

BDIDA-IoT: A Blockchain-Based Decentralized Identity Architecture Enhances the Efficiency of IoT Data Flow

With the booming development of the Internet of Things (IoT) industry, millions of data are generated every day. How to use and manage these data safely and efficiently has become a hot issue of concern to people. Due to the accumulation of IoT data, the isolated data island phenomenon makes it difficult to connect and interact with each data owner, and the security and privacy of IoT data also become a challenge. Blockchain is a decentralized database technology that uses distributed accounting to ensure reliable data transmission and access, along with smart contracts that can be executed automatically to program and manipulate data. At the same time, blockchain techniques have stronger security and privacy, which can better meet the needs of users. In this paper, we analyze the current management mode and challenges of IoT data and propose an IoT data platform based on blockchain. The proposed platform takes into account important factors including IoT data traceability, IoT data trusted transactions, etc. Our IoT data platform achieves the trusted management and transaction of IoT data. We also propose follow-up optimization solutions to expand the application scope of our platform and achieve more efficient management of IoT data.

Automation and Computerization of (Bio)sensing Systems.

Sensing systems necessitate automation to reduce human effort, increase reproducibility, and enable remote sensing. In this perspective, we highlight different types of sensing systems with elements of automation, which are based on flow injection and sequential injection analysis, microfluidics, robotics, and other prototypes addressing specific real-world problems. Finally, we discuss the role of computer technology in sensing systems. Automated flow injection and sequential injection techniques offer precise and efficient sample handling and dependable outcomes. They enable continuous analysis of numerous samples, boosting throughput, and saving time and resources. They enhance safety by minimizing contact with hazardous chemicals. Microfluidic systems are enhanced by automation to enable precise control of parameters and increase of analysis speed. Robotic sampling and sample preparation platforms excel in precise execution of intricate, repetitive tasks such as sample handling, dilution, and transfer. These platforms enhance efficiency by multitasking, use minimal sample volumes, and they seamlessly integrate with analytical instruments. Other sensor prototypes utilize mechanical devices and computer technology to address real-world issues, offering efficient, accurate, and economical real-time solutions for analyte identification and quantification in remote areas. Computer technology is crucial in modern sensing systems, enabling data acquisition, signal processing, real-time analysis, and data storage. Machine learning and artificial intelligence enhance predictions from the sensor data, supporting the Internet of Things with efficient data management.

Perancangan Prototipe Business Intelligence Dashboard Penjualan PT ABC

In a competitive business era, efficient and informative sales data management is essential for company success. The purpose of developing this sales dashboard prototype is to monitor sales data, integrate data from various sources, and make smarter business decisions. PT ABC, as a company engaged in milk sales, realizes the importance of timely monitoring of sales data and accurate analysis. The method used in the development of the dashboard is the SDLC prototyping method. The initial stages include needs identification, then design, followed by the creation of an initial prototype that is used to get feedback from stakeholders. The prototype will be designed using Power BI tools then the prototype results will be improved based on feedback. Through this design, the needs of decision makers will be better understood. The result of the design is sales data visualization based on Key Performance Indicators, namely sales targets and sales growth.

Evaluating The Effectiveness of Information and Communication Technologies (Icts) in Fire Response in Disaster Management in Batam, Indonesia

This research aims to evaluate the utilization of Information and Communication Technologies (ICTs) in the context of fire response for disaster management in Batam, Indonesia, spanning the years 2018 to 2023. Employing a mixed-methods approach involving surveys and case studies, the study investigates strategies and provides recommendations for health adaptation actions during climate-related disasters. The research assesses the effectiveness of ICTs in disaster risk management, with a specific focus on evaluating the role of the Disaster Management Act in Indonesia. The findings reveal that ICT-based early warning systems, enhanced communication networks, and efficient data management significantly contribute to improving Batam's preparedness and response to disasters. Despite these positive outcomes, the study identifies challenges, including issues related to equitable technology access, infrastructure limitations in remote areas, and privacy concerns that warrant attention. This study presents valuable quantitative insights into the effectiveness of ICTs in disaster management in Batam, Indonesia. It emphasizes the importance of addressing the identified challenges for a more robust disaster mitigation framework. The results have significant implications for policymakers and practitioners, underscoring the need for targeted interventions to overcome barriers and ensure the effective integration of ICTs in disaster response and management practices in Indonesia.

Cyber–physical system architecture of autonomous robot ecosystem for industrial asset monitoring

Driven by advancements in Industry 4.0, the Internet of Things (IoT), digital twins (DT), and cyber–physical systems (CPS), there is a growing interest in the digitalizing of asset integrity management. CPS, in particular, is a pivotal technology for the development of intelligent and interconnected systems. The design of a scalable, low-latency communication network with efficient data management is crucial for connecting physical and digital twins in heterogeneous robot fleets. This paper introduces a generalized cyber–physical architecture aimed at governing an autonomous multi-robot ecosystem via a scalable communication network. The objective is to ensure accurate and near-real-time perception of the remote environment by digital twins during robot missions. Our approach integrates techniques such as downsampling, compression, and dynamic bandwidth management to facilitate effective communication and cooperative inspection missions. This allow for efficient bi-directional data exchange between digital and physical twins, thereby enhancing the overall performance of the system.This study contributes to the ongoing research on the deployment of cyber–physical systems for heterogeneous multi-robot fleets in remote inspection missions. The feasibility of the approach has been demonstrated through simulations in a representative environment. In these experiments, a fleet of robots is used to map an unknown building and generate a common 3D probabilistic voxel-grid map, while evaluating and managing bandwidth requirements. This study represents a step forward towards the practical implementation of continuous remote inspection with multi-robot systems through cyber–physical infrastructure. It offers potential improvements in scalability, interoperability, and performance for industrial asset monitoring.

INSIGHT: in situ heuristic tool for the efficient reduction of grazing-incidence X-ray scattering data.

INSIGHT is a Python-based software tool for processing and reducing 2D grazing-incidence wide- and small-angle X-ray scattering (GIWAXS/GISAXS) data. It offers the geometric transformation of the 2D GIWAXS/GISAXS detector image to reciprocal space, including vectorized and parallelized pixel-wise intensity correction calculations. An explicit focus on efficient data management and batch processing enables full control of large time-resolved synchrotron and laboratory data sets for a detailed analysis of kinetic GIWAXS/GISAXS studies of thin films. It processes data acquired with arbitrarily rotated detectors and performs vertical, horizontal, azimuthal and radial cuts in reciprocal space. It further allows crystallographic indexing and GIWAXS pattern simulation, and provides various plotting and export functionalities. Customized scripting offers a one-step solution to reduce, process, analyze and export findings of large in situ and operando data sets.

Data management practices aligned with LGPD in the data science context

Data management is a comprehensive process that includes data collection, storage, organization, and maintenance for efficient data analysis and use. With data generation at unprecedented volumes and at breakneck speed, effective data management has become an operational necessity and a competitive advantage. In the context of Data Science, data is the raw material for insights and innovations, making its proper management fundamental to ensure reliability and accuracy. This article stresses the importance of data governance, security and privacy, especially in view of the increase in data protection regulations such as the LGPD. It also addresses efficiency in data management as essential to driving innovation and facilitating data-driven decision-making in organizations and academia. The ability to manage large volumes of data is unique because well-managed data provides a valuable source of information, enabling trend identification, process optimization and strategic decision making, leading to innovation and competitive advantages. Also, this article discusses LGPD compliance, not only as a legal issue, but also as an opportunity for companies to build trust with their users by demonstrating a commitment to data security and privacy. Efficient data management also involves choosing appropriate tools and technologies and implementing policies that ensure data compliance and security.

Library management database design and application

Under the data-centric business environment, efficient database design is pivotal for efficient data management across various sectors. For libraries and bookstores, a well-tailored database is essential to categorize, index, and swiftly retrieve vast amounts of data, ensuring efficient operations. MySQL is an open-source database management system which facilitates efficient data manipulation. Based on MySQL, this essay delves into the realm of library database design and application, addressing the need for streamlined systems. This research addresses the need for adaptation in the evolving technology landscape and offers practical insights for librarians and researchers aiming to optimize information management in modern libraries. Employing the basic steps for database design, we explore one methodology for designing a library management database based on the design of database requirement, modeling, backend implementation, efficiency optimization and testing using MySQL. Through this approach, the study provides a potential solution that meets the basic requirements of libraries and enhances information management practices.

Analisis Sistem Informasi Menajemen Terhadap Pengambilan Keputusan Pada Kantor Wilayah Badan Pertanahan Sumatera Utara

This research aims to analyze the role of the Management Information System (SIM) in supporting the decision-making process at the North Sumatra Land Agency Regional Office. This study explores the integration, reliability and contribution of information systems used in managing property and land information and their impact on the efficiency and effectiveness of decisions. The research methodology includes surveys of system users, interviews with top-level managers, and documentation of analyzes related to the technological infrastructure implemented. The data collected was analyzed using qualitative methods to understand the adequacy of the system, its suitability to user needs, and its contribution to the decision-making process. The research results show that the implemented Management Information System has a number of advantages, including efficient data management, better information accessibility, and real-time data availability. However, challenges such as data security and the need for increased integration with other systems were identified as potential improvements. In the context of decision making, MIS is proven to play an important role in providing timely and accurate information for management in making strategic decisions regarding land ownership, document updates and property transfers. Recommendations are prepared based on research results to improve the efficiency and effectiveness of the Management Information System to support better decision making at the North Sumatra Land Agency Regional Office. This research provides valuable insights for managers and developers of information systems in the context of the Land Agency, as well as a basis for improving the system to increase its decision-making role to a more strategic and responsive nature.

Strategies for Master Data Management Integration and Their Benefits

This study examines the efficacy of various methodologies employed for the integration of Master Data Management (MDM) and assesses the corresponding advantages in modern data-centric organizations. The main goal is to improve the processes of managing organizational data, tackle existing issues, and maximize the overall effectiveness of decision-making based on data. The research employs a comprehensive methodology, undertaking a meticulous examination of the current body of literature pertaining to Master Data Management (MDM) and integration methodologies. Both qualitative and quantitative research methodologies are utilized to assess the efficacy of various Master Data Management (MDM) integration solutions. The research design incorporates meticulous data gathering methodologies and sophisticated analytic approaches to guarantee the production of reliable and resilient findings. The study investigates various solutions for integrating Master Data Management (MDM), including consolidation, federation, and coexistence. Comprehensive elucidations of each method are furnished, bolstered by empirical illustrations and empirical analyses, proffering pragmatic perspectives. This paper provides a systematic analysis of the benefits associated with the integration of Master Data Management (MDM), with a particular focus on enhancements in data quality, decision-making processes, and operational efficiency. The importance of MDM integration in modern business environments is substantiated by empirical evidence and illustrative examples. In summary, this study highlights the crucial significance of integrating Master Data Management (MDM) in achieving organizational success. Through the examination of obstacles, the provision of pragmatic approaches, and the presentation of empirical illustrations, this research adds to the current scholarly understanding of efficient data management. It is recommended that further research endeavors be undertaken to delve deeper .....

Data standards based Mine Side Data Integration Governance Platform Research and Applications

INTRODUCTION: With the advent of the mining digitalization era, the rapid growth and diversity of mine-side data make mining production management more complex. In this context, it becomes crucial to build a set of mine-side data integration and governance platforms based on data standards. The establishment of this platform aims to solve the problems of mining data fragmentation and inconsistent standards and to improve the efficiency of data management and utilization.OBJECTIVES: The purpose of this study is to construct an efficient mine-side data integration and governance platform by studying and applying data standards to address the challenges of data heterogeneity and fragmentation. Through the development of unified data standards, the Author can realize the standardized management of mining data, improve data quality, and provide more reliable support for mining production.METHODS: The study adopts a comprehensive approach. First, a comprehensive understanding of the problems and needs facing mining data management was obtained through a literature review and field research. Second, data standards applicable to mine-side data are developed to ensure that all types of data can be consistently understood and processed. Next, a data integration and governance platform based on these standards is established, and advanced data mining and artificial intelligence technologies are applied to realize automatic integration and cleaning of multi-source data.RESULTS: Through experiments, this study proved that a mine-side data integration governance platform based on data standards was successfully constructed. The platform realizes efficient integration and cleaning of data and improves the consistency and quality of data. In practical application, the platform provides more accurate data support for mining production management and achieves remarkable results.CONCLUSION: This study provides strong theoretical and practical support for the research and application of mine-side data integration and governance platforms based on data standards. Through the development of data standards, an efficient data management platform has been established, which provides practical solutions for solving mining data management challenges. In the future, the performance of the platform can be further optimized, and the updating and maintenance of data standards can be strengthened to adapt to the constant changes in the digital development of the mining industry. This research has a positive impact on promoting the construction of mining industry informatization and improving the level of production management.