Efficient Data Management Research Articles

Peningkatan Efisiensi dan Manajemen Informasi dalam Pelayanan Unit Kesehatan Sekolah (UKS) Berbasis Website

The School Health Unit is an important component in the education system to promote the health and well-being of students. UKS aims to provide comprehensive health services to students, including disease prevention, health monitoring, health counseling, and basic medicine. But there are several obstacles that are often faced in managing data in the UKS, including limited technology infrastructure, lack of training and skills in data management, time constraints, data privacy and security issues and several other obstacles that might occur. For this reason, a website-based information system is needed that can be used easily by all people but still with useful features such as drug stock lists and student visits so as to be more efficient in inputting data. The School Health Unit Information System (SI-UKS) is a technology platform used to manage and organize student health information and support the administrative and operational activities of the School Health Unit. The School Health Unit Information System (SI-UKS) plays an important role in improving the efficiency and management of information in school health services. Through efficient management of student data, good data reporting and analysis, and integrated communication, SI-UKS helps improve the quality of health services and overall student well-being. In designing this website-based Information System using the waterfall method.

Advancing Road Safety through Cloud Based RSU Solutions for Smart Internet of Vehicles

This research proposes a system to improve road safety by detecting and alerting drivers about hazardous road conditions in real-time. Vehicles equipped with sensors, GPS, and communication devices can autonomously detect hazardous road conditions and transmit alerts to a central cloud system through IoT MQTT protocol. Upon receiving alerts, vehicles can dynamically adjust their routes to avoid hazardous areas, reducing the risk of accidents. By utilizing IoT technology, the system ensures the reliability and authenticity of shared information, enhancing trust and overall road safety awareness. The proposed system architecture leverages cloud-based Roadside Units (RSUs) to facilitate communication among smart vehicles, providing real-time road condition information and ensuring secure and efficient data exchange. The system aims to address the challenges of existing IoV frameworks, such as data protection, key management, storage, concurrency performance, and response speed, by implementing robust security measures and efficient data management strategies.

Student Result Analysis and Management

Abstract: Student result analysis and management (SRAM) gives a straightforward interface to support student data. It might be utilised by instructive universities or colleges to keep up the records of students effectively. Student information system manages all sorts of student details, academic related reports results, batch details, and other resource related details too. An effective result analysis system plays a pivotal role in facilitating efficient management of student data and performance assessment. Institutions staff and administrators need to manage students’ information and academic records. For staff by offering analysis, streamlined administrative tasks and efficient records management. For students, it provides easy access to their academic performance and results metrics. This abstract delineates the design and implementation of a robust result analysis system, encompassing a PHP-based backend. The system caters to the needs of teachers, students, and administrators, offering functionalities tailored to each user category. Ultimately, the deployment of the result analysis system on a web server caters to the needs of educational institutions, facilitating efficient management of student data and fostering a conducive environment for academic assessment and improvement

A blockchain-based hybrid encryption technique with anti-quantum signature for securing electronic health records

Electronic health records (EHRs) are important for the efficient management of healthcare data. However, Healthcare data travels across an open route, i.e., the Internet, making EHR security a difficult process to do. This puts healthcare data vulnerable to cyber assaults. A possible method for protecting EHRs is blockchain technology. In this work, we develop an EHR architecture based on blockchain, which ensures all stakeholder's safety and privacy. We analyze various security architectures used for EHRs and the standard encryption system is integrated with quantum computing (QC). To safeguard the conventional traditional encrypting system against quantum assaults, we provide a hybrid signature technique that combines the Elliptic Curve Digital Signature Algorithm (ECDSA) and Dilithium within the anti-quantum lattice-based blind signature. Based on the difficulty of lattice problems over finite fields, Dilithium is a lattice-based signature method that is substantially safe against selected message assaults. The developed technique creates high entropy secret keys using the lattice basis delegation mechanism. The combination of ECDSA and Dilithium provides an efficient and secure signature system that is resilient to quantum attacks. The proposed scheme ensures that only authorized users with a defined role can use the database to access the data. We evaluate the efficiency of our scheme by comparing its performance to other state-of-the-art solutions in terms of transaction throughput, resource utilization, and communication cost. Results demonstrate that the developed technique outperforms the existing techniques in terms of efficiency and security.

PERANCANGAN STRATEGI VALIDASI DATA DENGAN PEMANFAATAN APLIKASI VALIDASI BERBASIS GMAPS UNTUK PRODUK INDIHOME

Guaranteeing the accuracy and completeness of data is a critical aspect in the scope of telecommunications services. In this context, this research aims to design an effective data validation strategy through the use of a Google Maps (GMaps) based validation application in Indihome products. The design involved an approach that combined GMaps interactive map technology, the React JS framework, and the MongoDB database. Using the React JS framework, an interactive user interface has been developed to facilitate the use of validation applications. Utilization of Google Maps allows visual integration of validation data with precise geographic locations. The use of the MongoDB database supports efficient storage and management of validation data, ensuring that the data obtained meets requirements. Application testing has been carried out to ensure the effectiveness of the designed data validation strategy. The result of this research is the design of an effective data validation strategy, through the application of GMaps, React JS, and MongoDB technology. This implementation is expected to provide a more accurate, efficient and intuitive solution in ensuring data quality in Indihome services. This research can also provide benefits for technological developments in the field of data validation and the application of map concepts in user experience

Life Integrate: Unify Your Life, Simplify Your Success

Abstract: Efficient management and organization of personal and commercial data is critical for reducing stress and increasing productivity in the fast-paced digital environment we live in. An inventive smartphone software called "Life Integrate" attempts to meet this requirement by effectively managing duties and merging different facets of life. The project aims to mimic the effectiveness of an AI personal assistant by bridging the gap between contemporary lives and efficient organizing. The software smoothly distinguishes between personal and professional data by utilizing technologies like machine learning and natural language processing, which improves the user experience and efficiency. One to three clicks can easily share data with the app, which adds to its effectiveness and user-friendliness. Aiming to streamline processes, key features include real-time editing, interactive chats, speech-activated assistants, and contextual awareness. The project's emphasis on developing a personal bot that excels at data collection and generates intelligent ideas based on collected data exemplifies an intriguing combination of customization and artificial intelligence. With its 13 primary sub-tabs and extensive questionnaires for storing both personal and professional data, the app offers an approach to organization and management that is user-centric. Effective app development and deployment are ensured by the use of the Flutter and Dart platforms. In general, "Life Integrate" seeks to transform the information landscape, making it easier to acquire and use, and facilitating more enjoyable interactions between humans and machines. With this project, we hope to create a future in which people can easily incorporate technology into their daily lives, increasing productivity and lowering stress levels

Efficient Data Management in Agricultural IoT: Compression, Security, and MQTT Protocol Analysis.

The integration of Internet of Things (IoT) technology into agriculture has revolutionized farming practices by using connected devices and sensors to optimize processes and facilitate sustainable execution. Because most IoT devices have limited resources, the vital requirement to efficiently manage data traffic while ensuring data security in agricultural IoT solutions creates several challenges. Therefore, it is important to study the data amount that IoT protocols generate for resource-constrained devices, as it has a direct impact on the device performance and overall usability of the IoT solution. In this paper, we present a comprehensive study that focuses on optimizing data transmission in agricultural IoT solutions with the use of compression algorithms and secure technologies. Through experimentation and analysis, we evaluate different approaches to minimize data traffic while protecting sensitive agricultural data. Our results highlight the effectiveness of compression algorithms, especially Huffman coding, in reducing data size and optimizing resource usage. In addition, the integration of encryption techniques, such as AES, provides the security of the transmitted data without incurring significant overhead. By assessing different communication scenarios, we identify the most efficient approach, a combination of Huffman encoding and AES encryption, to strike a balance between data security and transmission efficiency.

Enhancing Contextual Masking in Reversible Linguistic Steganography with Ensemble Methods

Various cybercrimes can be prevented by text authentication that is responsible for preserving digital identities and contents. Digital signatures come in handy as a way of authenticating texts, which is an extensively used method. One approach to this problem is linguistic steganography, which allows hiding the signature in other words within the text and thereby facilitating efficient data management. However, it should be noted that there is a danger that these kinds of changes may result in inappropriate decisions being taken by automated computing systems not to mention change their final outputs (unseen). As such, many people are becoming more concerned with the possibility of reversing steganography so that it becomes possible to eliminate any distortions made during the process. This paper uses Contextual masking instead of masking randomly with BERT model. The goal behind this research was developing a natural language text specific Reversible Steganographic System. Our model uses pre-trained BERT as a transformer based masked language model and reversibly embeds messages through predictive word substitution. To quantify predictive uncertainty, we introduce an adaptive steganographic technique using Bayesian deep learning. This experiment shows us how our proposed system balances imperceptibility with capacity while maintaining near semantics at all times. Also, we integrate ensemble methods instead of Monte Carlo to balance the imperceptibility.

CADCare: Smart System for CHD Identification & Sensor Alerts

INTRODUCTION: Cardiovascular diseases, particularly coronary artery disease (CAD), present a global health challenge, necessitating effective detection and diagnosis methods for early intervention. Various machine learning and deep learning approaches have emerged, utilizing diverse data sources such as electrocardiogram (ECG) signals and clinical features to enhance CAD detection. Additionally, circadian heart rate variability (HRV) has been explored as a potential diagnostic marker for CAD severity. This research aims to contribute to the burgeoning field of medical AI and its application in cardiology.OBJECTIVES: This study seeks to develop a Comprehensive Coronary Artery Disease Detection System integrating real-time heart rate monitoring and CAD prediction via an Android application. The objectives include seamless data transmission, efficient cloud-based data management, and the utilization of AI models, including ANNs, CNNs for ECG images, and hybrid models combining clinical and ECG data, to improve early CAD detection and management.METHODS: The system architecture involves integrating key sensors, an Arduino microcontroller, a Bluetooth module, and AI models to facilitate early CAD detection. An Android application complements the system, offering heart rate monitoring and CAD prediction using various data sources. Cloud computing is employed for efficient data management and analysis.RESULTS: The developed system successfully integrates cutting-edge technology to enhance CAD detection, achieving accurate and efficient results in real-time heart rate monitoring and CAD prediction.CONCLUSION: The Comprehensive Coronary Artery Disease Detection System, leveraging AI and cloud computing, contributes to proactive health monitoring and informed decision-making in CAD management and prevention, thereby addressing a critical need in cardiovascular health care.

Towards Municipal Data Utilities: Experiences Regarding the Development of a Municipal Data Utility for Intra- and Intermunicipal Actors within the German City of Mainz

This paper describes the requirements analysis phase towards the establishment and implementation of a municipal data utility (KDW = Kommunales Datenwerk, German) to facilitate data sharing between intra- and intermunicipal stakeholders. Against the backdrop of increasing digitisation and the growing importance of data-driven decision making in municipal governance, this paper aims to address the pressing need for efficient data management solutions within and across municipalities. Based on a structured self-developed methodology, the authors use a qualitative research approach: the paper examines the experiences and challenges encountered during the requirements phase, the design phase, and the development phase of the KDW. The findings indicate that the establishment of a robust KDW requires (1) extensive stakeholder engagement, (2) clear governance structures, and (3) a robust technical infrastructure. In addition, the study highlights the critical importance of establishing a sound legal framework that addresses data ownership, privacy, security and regulatory compliance. Addressing legal and regulatory barriers to data sharing is paramount to the successful implementation and operation of the KDW. The paper concludes by highlighting the potential benefits of KDWs and outlining future work. The overall methodology, approach, and outcome are validated within the city of Mainz, and the lessons learned are accommodated in the insights presented in the rest of the paper.

PQCAIE: Post quantum cryptographic authentication scheme for IoT-based e-health systems

The increasing integration of Internet of Things (IoT) technologies in consumer electronics has revolutionized various sectors, including healthcare. This evolution has led to the development of IoT-enabled consumer health devices and systems, offering benefits such as enhanced remote health monitoring and more efficient health data management. However, these advancements also pose significant security challenges, especially regarding data privacy and secure access. A critical concern is the vulnerability of current cryptographic methods to potential future quantum computing capabilities. This paper focuses on addressing these challenges by exploring the implementation of Post-Quantum Cryptography (PQC) in IoT-based consumer health electronics. Specifically, it evaluates the application of PQC methods in conjunction with Transport Layer Security 1.3 (TLS 1.3) for robust authentication in these systems. The study analyzes the performance and security efficacy of these schemes, comparing them to existing cryptographic approaches. Additionally, it delves into the practical hurdles and prospective solutions related to the deployment of post-quantum cryptographic techniques in the context of consumer health electronics, paving the way for more secure and reliable healthcare technology in the era of advanced consumer electronics.

Machine Learning in Transportation

The number of vehicles and people on the planet is growing, and this has made traffic management more complex and requires accuracy and efficiency that go beyond human intervention. This work explores the use of machine learning (ML) algorithms in intelligent transportation systems, with a focus on traffic control. It looks at how ML might help with issues caused by increasing traffic, such as accidents and congestion. In an effort to lessen traffic- related problems, the suggested ML-based Traffic Management System (TMS) keeps an eye on cars, adjusts traffic lights, finds congestion, and provides other routes. In order to construct an intelligent transport system that uses machine learning (ML) algorithms, stimulate efficient traffic data management, and improve contemporary traffic management systems, the article outlines the principles of operation, obstacles encountered, and suggestions offered.

A blockchain‐based secure framework for data management

AbstractData management is a crucial requirement due to the autonomous and constrained nature of Unmanned Aerial Vehicles (UAVs), Internet of Things (IoTs), and the aviation domain. The autonomous and restricted nature of these sectors increases the need for a shared, distributed database, strong access control management, consensus in autonomous decision‐making, and effective communication across diverse protocols and devices. This research presents a comprehensive approach and offers a new viewpoint to the field of blockchain while establishing a fundamental baseline for future improvements in data management systems and addressing the shortcomings of previously proposed existing frameworks in order to fulfill the complex needs of secure data management. This study contributes to the advancement of secure and efficient data management systems by implementing robust data monitoring for error detection, ensuring data integrity, and enabling encrypted or anonymous data sharing based on sensitivity levels. Additionally, the integration of diverse devices, enforcement of immutable regulations compliance, and development of permissioned blockchain systems for identity management further enhance the system's capabilities, offering comprehensive solutions for modern data management challenges. In the tests, the proposed framework showed increased successful transactions in all rate controllers. Besides, effect of the validator number on throughput and latency is tested and analyzed thoroughly.

Driving intelligent IoT monitoring and control through cloud computing and machine learning

At present, cloud computing and the Internet of Things are closely integrated. IoT devices gather data through sensors and transmit it to the cloud for storage, processing, and analysis. This synergy enables efficient data management and in-depth analysis, facilitating real-time monitoring and predictive maintenance. This article explores leveraging cloud computing and machine learning for intelligent IoT monitoring and control. Edge computing, a distributed architecture, decentralizes data processing from the cloud to reduce latency and improve efficiency. This combination enhances security and drives the development of intelligent systems.

BSEO-Semiautomatic Method for Determination of Oil Recovery with Nanofluids in Microfluidic Devices.

Microfluidic models have become essential instruments for studying enhanced oil recovery techniques through fluid and chemical injection into micromodels to observe interactions with pore structures and resident fluids. The widespread use of cost-effective lab-on-a-chip devices, known for efficient data extraction and minimal reagent usage, has driven demand for efficient data management methods crucial for high-performance data and image analyses. This article introduces a semiautomatic method for calculating oil recovery in polymeric nanofluid flooding experiments based on the background subtraction (BSEO). It employs the background subtraction technique, generating a foreground binary mask to detect injected fluids represented as pixel areas. The pixel difference is then compared to a threshold value to determine whether the given pixel is foreground or background. Moreover, the proposed method compares its performance with two other representative methods: the ground truth (manual segmentation) and Fiji-ImageJ software. The experiments yielded promising results. Low values of mean-squared error (MSE), mean absolute error (MAE), and root-mean-squared error (RMSE) indicate minimal prediction errors, while a substantial coefficient of determination (R2) of 98% highlights the strong correlation between the method's predictions and the observed outcomes. In conclusion, the presented method emphasizes the viability of BSEO as a robust alternative, offering the advantages of reduced computational resource usage and faster processing times.

Pagadian Diocesan Schools Information Monitoring System through Data Visualization

The Pagadian Diocesan Schools Information Monitoring System through Data Visualization is a web-based application meticulously developed to address the challenges of managing educational data across multiple schools within the Pagadian Diocese. Utilizing HTML, MySQL, PHP, CSS, and Bootstrap, the system streamlines the login process for administrators, enhances data monitoring, and provides a centralized view for superintendents. Through advanced data visualization tools, the platform presents essential metrics such as student population, income, and expenses, contributing significantly to the Pagadian government’s education initiatives while adhering to ethical data usage practices. Continuous user feedback ensures the refinement of the system, guaranteeing efficient and effective data management within the Pagadian Diocese. The study also encompasses the design, development, and testing phases of the system, following the Waterfall Model. Through rigorous testing, the system demonstrated commendable performance, stability, and security measures, with high user-friendliness and compatibility. The ISO/Logical Summary Report identified strengths in Functional Stability, Portability, Reliability, Security, Usability, and Maintainability, leading to a Total ISO/Logical Flow score of 90.94%. Overall, the system promises a smooth, effective, and dependable user experience, with room for improvement in UI design and error handling to enhance its performance further.

HydroRTC: A web-based data transfer and communication library for collaborative data processing and sharing in the hydrological domain

The exponential growth in data generated by satellites, radars, sensors, and analysis and reanalysis from model outputs for the hydrological domain requires efficient real-time data management and distribution mechanisms. This paper introduces HydroRTC, a web-based data transfer and communication library designed to accelerate large-scale data sharing and analysis. Leveraging next-generation web technologies like WebSockets, WebRTC and Node.js, the library enables seamless peer-to-peer sharing, smart data transmission, and large dataset streaming. Three primary scenarios are presented as use cases, demonstrating the potential of HydroRTC as server-to-peer with intelligent data scheduling and large data streaming, peer-to-peer data sharing, and peer-to-server for data exchange. HydroRTC offers a promising solution for collaborative infrastructures in the hydrological and environmental domain, allowing real-time and high-throughput data sharing and transfer for enhancing research efficiency and collaboration capabilities.

Introduction to Data Protection Frameworks: A Review

In the digital era, where data breaches are prevalent and harmful, "An Introduction to Data Protection Frameworks: A Review" provides a critical analysis of mechanisms aimed at safeguarding personal data. The review commences by discussing data protection, underscoring its significance as a basic right and its pivotal role in the digital realm. It then explores the historical backdrop, tracing the progression of data protection laws from their origins to the extensive frameworks in place today. The review meticulously details the fundamental principles of data protection—fairness, accuracy, minimization, and accountability—which form the bedrock of efficient data management. Additionally, it highlights the rights of data subjects, such as access, rectification, and erasure, empowering individuals amidst the rise of data-driven practices. It further delves into the obligations of data controllers and processors, stressing the importance of adherence and integrity in data management. The paper also conducted a comparative examination of global frameworks such as the GDPR, which is critical for international data regulation. Lastly, the review explores the obstacles and future prospects of data protection, recognizing the challenges posed by technological progress and the global exchange of information.

FIWARE-Compatible Smart Data Models for Satellite Imagery and Flood Risk Assessment to Enhance Data Management

The increasing rate of adoption of innovative technological achievements along with the penetration of the Next Generation Internet (NGI) technologies and Artificial Intelligence (AI) in the water sector are leading to a shift to a Water-Smart Society. New challenges have emerged in terms of data interoperability, sharing, and trustworthiness due to the rapidly increasing volume of heterogeneous data generated by multiple technologies. Hence, there is a need for efficient harmonization and smart modeling of the data to foster advanced AI analytical processes, which will lead to efficient water data management. The main objective of this work is to propose two Smart Data Models focusing on the modeling of the satellite imagery data and the flood risk assessment processes. The utilization of those models reinforces the fusion and homogenization of diverse information and data, facilitating the adoption of AI technologies for flood mapping and monitoring. Furthermore, a holistic framework is developed and evaluated via qualitative and quantitative performance indicators revealing the efficacy of the proposed models concerning the usage of the models in real cases. The framework is based on the well-known and compatible technologies on NGSI-LD standards which are customized and applicable easily to support the water data management processes effectively.

Potential and challenges of blockchain technology implementation in Higher Education: A systematic literature review

This study examined the potential applications and challenges of implementing blockchain technology within higher education systems. Blockchain, known for its decentralized and immutable nature, attracted significant interest due to its ability to provide secure, transparent, and efficient data management. In the context of higher education, blockchain has the potential to revolutionize academic record management, enhance verification processes, prevent fraud, and ensure the authenticity of credentials. This systematic literature review identified key use cases, such as academic credentialing, student data management, and the protection of intellectual property in research. Despite its promising potential, the adoption of blockchain in higher education, particularly in Indonesia, remained in its early stages. The study discussed major obstacles to implementation, including technical limitations, high costs, limited awareness, and regulatory barriers. Addressing these challenges required further research and collaboration between academic institutions, government agencies, and technology providers to enable widespread adoption. The review emphasized the need for a comprehensive framework to guide the practical implementation of blockchain, ensuring its benefits could be fully realized in educational environments.