Data Management Research Articles

Potential use of Six Sigma metrics in the quality control review of hospital glucose meters

Background and aimThis study applied Six Sigma metrics to facilitate the quality control (QC) review for hospital glucose meters. Materials and methodsQC data from a period of six months on all hospital glucose meters were extracted from the data management system. Sigma values for each meter at two QC levels were calculated and evaluated each month by combining the imprecision, the absolute bias between the meter mean and all-meter mean, and the standards from ISO 15179:2013. The effectiveness of using Sigma values in identifying meters with possible quality problems for further Levey-Jennings QC chart review was assessed. ResultsMore than 80 % of the meter's Sigma values within the six months were greater than 4 at either QC level. At the high QC level, twice as many Sigma values were below 4 than the low QC level. Including Sigma values 4, 3.5 or 3 in the criteria for the QC review reduced the number of chart review to 32.8 %, 11.2 % or 3.5 %, respectively. ConclusionsThe majority of the glucose meters examined in this study demonstrated optimal Sigma values. The Sigma metrics-based approach could be a valuable tool to guide an effective QC review of glucose meters for quality improvement.

Changes in the Johne’s disease situation in GB dairy herds over 10 years, as revealed by regular milk ELISA data

Effective management of cattle infected with Johne’s Disease (JD) is crucial to minimizing transmission and within-herd prevalence. Within Great Britain (GB), the voluntary National Johne’s Management Plan (NJMP) requires farmers and a certified vet to conduct a risk assessment to determine the herd risk, examine the herd JD status and formulate a management plan. Individual milk ELISA tests for JD antibodies are widely used to monitor infection. The JD Tracker application, available within the dairy data management software InterHerd+ and other web-based environments, is being used by farmers and veterinarians to facilitate the practical use of milk ELISA data to aid JD-related management decisions. The JD Tracker application uses a herd’s milk ELISA data to calculate a collection of ‘JD parameters’ that are indicative of the current JD status of the herd alongside contemporary and retrospective drivers linked to transmission and maintenance of infection. Herein, we use milk ELISA data from 154 regularly testing herds to review the temporal trends in JD parameters from 2013 to 2022. Since 2015, JD Tracker parameters have improved in these herds, most notably average test value (ATV) and within-herd prevalence (%Pos30). Trends in driver parameters suggest that farmers are progressively less likely to serve repeat test-positive (J5) cows and are more readily removing them. The data also reveal that the burden of JD is disproportionately greater in herds with higher ATV. In 2022, the 25 % of herds with the highest ATVs accounted for 42 % of positive tests and 42 % of repeat ELISA positive (J5) cows. Retrospectively, it is not possible to identify with certainty factors that directly contributed to the trends in JD parameters, but it is notable that the introduction of the NJMP was coincided with the improving JD situation. In 2019, participation in the NJMP or an equivalent scheme became mandatory for dairy farms to be compliant with the food and farms standards assurance scheme Red Tractor, with the result that JD management plans are now completed by 95 % of UK dairy farms. As far as we know, the UK is unique in its development of a tool (the JD Tracker) which adds utility to milk ELISA data using specifically designed JD parameters. Anticipated further work includes the development of a national database of JD testing herds and application of the JD Tracker at national scale to enable more comprehensive industry-level monitoring of JD within GB dairy farms.

Decentralized and Secure Collaborative Framework for Personalized Diabetes Prediction.

Diabetes is a global epidemic with severe consequences for individuals and healthcare systems. While early and personalized prediction can significantly improve outcomes, traditional centralized prediction models suffer from privacy risks and limited data diversity. This paper introduces a novel framework that integrates blockchain and federated learning to address these challenges. Blockchain provides a secure, decentralized foundation for data management, access control, and auditability. Federated learning enables model training on distributed datasets without compromising patient privacy. This collaborative approach facilitates the development of more robust and personalized diabetes prediction models, leveraging the combined data resources of multiple healthcare institutions. We have performed extensive evaluation experiments and security analyses. The results demonstrate good performance while significantly enhancing privacy and security compared to centralized approaches. Our framework offers a promising solution for the ethical and effective use of healthcare data in diabetes prediction.

JCDC: A blockchain-based framework for secure data storage and circulation in JointCloud

JointCloud computing represents a new generation cloud computing paradigm, which deeply integrates the cloud resources of multiple Cloud Service Providers (CSPs) to offer tailored cloud services to users. In contrast to traditional multi-cloud environment, JointCloud environment involve data circulation among multiple CSPs. However, in JointCloud environment, CSPs are not always fully trustworthy and they may illegally infringe upon users’ data privacy and security for their own benefit. Additionally, the heterogeneity arising from different data storage formats, structures, access control, and permission management mechanisms adopted by various CSPs makes achieving unified data management in JointCloud challenging. Therefore, to ensure secure storage and efficient circulation of data within JointCloud, it is essential to prevent violations for user privacy and data ownership, shield the heterogeneity of underlying data management mechanisms across different CSPs, and establish trusted transactions between CSPs. In this paper, we propose a framework called JointCloud Data Chain (JCDC) based on JointCloud computing and blockchain for data storage and circulation, aiming to ensure secure data storage and trustworthy transactions. JCDC utilizes blockchain to record data ownership and control data circulation, while integrating storage resources from various CSPs to construct a distributed off-chain Personal Data Storage (PDS) for expanding system storage capacity. Additionally, JCDC employs Certificateless Public Key Cryptography (CL-PKC) and Proxy Re-encryption technologies for user identity management and secure data transactions. Furthermore, smart contracts are designed to enable automated data storage and sharing. We conduct a security analysis of JCDC and develop a prototype system to validate its performance and practicality. Finally, extensive experimentation and analysis demonstrate that JCDC exhibits low time latency and cost, which makes it practical.

A conceptual framework for integrating volunteers in emergency response planning and optimization assisted by decision support systems

In disaster response, the overwhelming amount of time-sensitive information and response options, combined with the dynamic nature of disasters, makes decision-making challenging for emergency service providers. Furthermore, it is often not economically feasible for countries to maintain a large number of full-time emergency responders. As such, many countries rely heavily on volunteer emergency responders during major disasters. This means that the success of disaster response often hinges on the efficient use of this volunteer workforce. We propose a framework for a Decision Support System (DSS) designed to optimize the use of volunteers by emergency services. This framework includes the data management layer, integrating necessary inputs and information; the analytical layer, which serves as the system's processing core; the user interface layer; and the decision-making layer. We argue that, while significant academic focus has been on the analytical layer, practical implementation requires the integration of all four components. Additionally, we emphasize the need for coordination with a broad spectrum of stakeholders involved in data provision, decision-making, and resource deployment for operationalizing this DSS. We also explore and analyze existing methodologies for developing the analytical layers, the requirements of these models, and the current methodological gaps. The proposed framework establishes a clear roadmap for adopting emergency response approaches that are human-centric, but at the same time, effectively utilize advancements in modeling, optimization, machine learning, and data integration.

An SDN-based Hybrid-DL-driven cognitive intrusion detection system for IoT ecosystem

Smart homes, healthcare, transportation, agriculture, manufacturing, and many more sectors can all benefit from IoT technology. This revolutionary way of interacting with the world could increase efficiency, convenience, and productivity. However, it also raises concerns regarding security, privacy, and data management. To mitigate security threats in IoT networks, this article presents a software-defined networking-enabled cognitive hybrid-deep learning model for intrusion detection in IoT ecosystem. The primary purpose of this framework is to continuously and efficiently identify cybersecurity threats in the IoT environment. With the influence of the cognitive computing paradigm, the designed system can analyse, understand, and even respond to various traffic approaching IoT devices. The proposed model is trained and tested using the cutting-edge N-BaIoT and CICDDoS2019 datasets. The experimental results demonstrate a high level of accuracy, yielding an acceptable false positive rate in conjunction with reasonable testing time. Furthermore, the architecture considers the limited resources of IoT devices, ensuring that they are not overburdened in the process. The proposed model obtained an acceptable accuracy rate of 99. 86 %, 99. 96 % precision, 99. 903 % recall, and 99.93 % F1-Score. The proposed model outperformed the other hybrid-DL models, such as Cu-GRU+LSTM and Cu-GRU+DNN.

A Situation Analysis of the Capacity of Laboratories in Faith-Based Hospitals in Zambia to Conduct Surveillance of Antimicrobial Resistance: Opportunities to Improve Diagnostic Stewardship.

Antimicrobial resistance (AMR) is a public health problem exacerbated by the overuse and misuse of antibiotics and the inadequate capacity of laboratories to conduct AMR surveillance. This study assessed the capacity of laboratories in seven faith-based hospitals to conduct AMR testing and surveillance in Zambia. This multi-facility, cross-sectional exploratory study was conducted from February 2024 to April 2024. We collected and analysed data using the self-scoring Laboratory Assessment of Antibiotic Resistance Testing Capacity (LAARC) tool. This study found an average score of 39%, indicating a low capacity of laboratories to conduct AMR surveillance. The highest capacity score was 47%, while the lowest was 25%. Only one hospital had a full capacity (100%) to utilise a laboratory information system (LIS). Three hospitals had a satisfactory capacity to perform data management with scores of 83%, 85%, and 95%. Only one hospital had a full capacity (100%) to process specimens, and only one hospital had good safety requirements for a microbiology laboratory, with a score of 89%. This study demonstrates that all the assessed hospitals had a low capacity to conduct AMR surveillance, which could affect diagnostic stewardship. Therefore, there is an urgent need to strengthen the microbiology capacity of laboratories to enhance AMR surveillance in Zambia.

Improving the experience of machine learning in compressive strength prediction of industrial concrete considering mixing proportions, engineered ratios and atmospheric features

In previous literature on predicting compressive strength (CS) using machine learning (ML), the focus has primarily been on algorithm-specific improvements, with less emphasis on improving the data perspective of CS prediction. Departing from these conventional investigations, this study presents data-centric enhancements. A systematic and comprehensive methodology is adopted, involving series of steps. Firstly, a large dataset (> 24,214 datapoints) comprising 26 input features relating to mixture proportions, engineered ratios and atmospheric parameters is prepared and processed. Feature engineering is then performed for selection and processing of data features. Subsequently, six scenarios are designed and investigated for CS prediction, focusing on distinct combinations of input features. Finally, CS predictions for all six scenarios are compared using five well-known ML model, and results are validated using statistical hypothesis testing. The results show significance improvements in CS prediction, with a 12.8 % increase in coefficient of determination (R2) and a 61 % reduction in root mean square error (RMSE) achieved by incorporating engineered and atmospheric parameters in conjunction with mixture proportion as inputs in CS prediction. Based on CS prediction in six scenarios, the study presents useful discussions focusing on concrete data perspective, role of engineering ratio and atmospheric features, and interaction and influence of input features in CS prediction. In conclusion, this work emphasizes the importance of enhancing data management practices for concrete performance assessment to have a more efficient, realistic, and sustainable outcomes in the concrete industry.

DESIGN AND IMPLEMENTATION OF A WEB-BASED INFORMATION SYSTEM FOR UNIVERSITY STAFF UNION

The Academic Staff Union of Universities (ASUU) in Nigerian public universities has long championed the welfare and interests of academic staff. However, operational inefficiencies stemming from manual processes hinder its effectiveness in addressing contemporary challenges. This inefficiency hampers ASUU’s effectiveness and limits its ability to interact efficiently with its members and stakeholders. Therefore, this study proposes that the ASUU Web-Based Information System (AWBIS) should modernize data management, communication, and decision-making within the union. Drawing on related work in online information systems across various sectors, the methodology involves requirements gathering, leading to the development and implementation of AWBIS. Leveraging development technologies, AWBIS offers modules for member management, communication, event organization, and biometric attendance. This project uses the waterfall model approach to developing software as the research methodology. The data collection techniques include interviews and questionnaires. The AWBIS enables various operations to be performed online, consisting of registering members by the admin, logging members, and allowing members to manage their profiles. The system also provides a login page for both members and admins, each with their own username and password. A secure and accessible database for storing memberprofiles, academic information, and other relevant data was created. The successful implementation of AWBIS will significantly streamline ASUU’s operations, resulting in improved communication, reduced administrative burden, and enhanced transparency. Members will have access to up-to-date information, and the system will facilitate better coordination of ASUU’s activities. Future enhancements, including financial management, election processes, loan applications, and mobile app development, are suggested to further optimize AWBIS’s functionality and accessibility. AWBIS is a model for leveraging technology to advance organizational efficiency and effectiveness within the education sector.

A situational ethics and harmonization challenges in the case of EU biodiversity data governance

ABSTRACT The article discusses challenges in harmonizing biodiversity data by considering situational ethics in the valuation of data practices. Data harmonization involves technical processes for managing and governing data by linking, aligning, and binding datasets. Our study views data harmonization as a socio-technical practice encompassing both technical management and cultural contexts of data production and valuation. Using qualitative interviews, we aim to understand data harmonization through Reflexive Thematic Analysis, identifying patterns within empirical data. Our research shows data harmonization as aligning datasets and settings involving human and non-human agents within EU biodiversity data politics. Through qualitative inquiry, we explore EU biodiversity data politics as a model of biodiversity datafication, highlighting challenges and values such as learning processes for all actors, simplicity in data management, broad adoption of observation schemes, and promotion of the Western model of engaged citizenship as a good observer.

Child Health Dataset Publishing and Mining Based on Differential Privacy Preservation

With the emergence and development of application requirements such as data analysis and publishing, it is particularly important to use differential privacy protection technology to provide more reliable, secure, and compliant datasets for research in the field of children’s health. This paper focuses on the differential privacy protection of the ultrasound examination health dataset of adolescents in southern Texas from three aspects: differential privacy protection with output perturbation on basic statistics, publication of differential privacy marginal histogram and synthesized data, and a machine learning differential privacy learning algorithm. Firstly, differential privacy protection results with output perturbation show that Laplace and Gaussian mechanisms for numerical data, as well as the exponential mechanism for non-numerical data, can achieve the goal of protecting privacy. The exponential mechanism provides higher privacy protection. Secondly, a differential privacy marginal histogram with four attributes can be obtained with an appropriate privacy budget that approximates the marginal histogram of the original data. In order to publish synthetic data, we construct a synthetic query to obtain the corresponding differential privacy histogram for two attributes. Further, a synthetic dataset can be constructed by following the data distribution of the original dataset and the quality of the synthetic data publication can also be evaluated by the mean square error and error rate. Finally, consider a differential privacy logistic regression model under machine learning to predict whether children have fatty liver in binary classification tasks. The experimental results show that the model combined with quadratic perturbation has better accuracy and privacy protection. This paper can provide differential privacy protection models under different demands, which provides important data release and analysis options for data managers and research organizations, in addition to enriching the research on child health data releasing and mining.

Musculoskeletal Disorder (MSD) Health Data Collection, Personalized Management and Exchange Using Fast Healthcare Interoperability Resources (FHIR).

With the proliferation and growing complexity of healthcare systems emerges the challenge of implementing scalable and interoperable solutions to seamlessly integrate heterogenous data from sources such as wearables, electronic health records, and patient reports that can provide a comprehensive and personalized view of the patient's health. Lack of standardization hinders the coordination between systems and stakeholders, impacting continuity of care and patient outcomes. Common musculoskeletal conditions affect people of all ages and can have a significant impact on quality of life. With physical activity and rehabilitation, these conditions can be mitigated, promoting recovery and preventing recurrence. Proper management of patient data allows for clinical decision support, facilitating personalized interventions and a patient-centered approach. Fast Healthcare Interoperability Resources (FHIR) is a widely adopted standard that defines healthcare concepts with the objective of easing information exchange and enabling interoperability throughout the healthcare sector, reducing implementation complexity without losing information integrity. This article explores the literature that reviews the contemporary role of FHIR, approaching its functioning, benefits, and challenges, and presents a methodology for structuring several types of health and wellbeing data, that can be routinely collected as observations and then encapsulated in FHIR resources, to ensure interoperability across systems. These were developed considering health industry standard guidelines, technological specifications, and using the experience gained from the implementation in various study cases, within European health-related research projects, to assess its effectiveness in the exchange of patient data in existing healthcare systems towards improving musculoskeletal disorders (MSDs).

Revolutionizing semantic integration of maintenance cost prediction for building systems using artificial neural networks

The maintenance cost management for building services has significantly advanced with the advent of computational techniques and semantic web technologies. Building services, including HVAC, plumbing, firefighting, and electrical systems, ensure building safety and efficiency. However, predicting maintenance costs is challenging due to the complexity and variability in usage patterns and environmental conditions. To address this challenge, this study introduces a Semantically Integrated Knowledge-based Artificial Neural Network (SIKB-ANN) framework. The SIKB-ANN framework integrates Semantic Web Technology (SWT) and Artificial Neural Networks (ANN) to improve the accuracy of maintenance cost predictions. SWT enhances data interoperability and standardization, while ANN manages complex non-linear data relationships. The model was trained with historical maintenance data from a firm (2017–2022) and validated using metrics such as Coefficient of Determination (R2), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The model achieved high predictive accuracy, with R2 values of 0.986 for the test set and 0.944 for the training set, indicating its robustness and reliability. An essential contribution of this research is the innovative integration of semantic ontologies with ANN, significantly enhancing predictive capabilities and providing a structured data management framework. This approach improves prediction accuracy and supports better decision-making and resource allocation in building management. The study highlights the potential of combining semantic technologies with machine learning to address complex predictive challenges in the built environment. Future research should consider integrating real-time data streams, advanced machine learning techniques, and broader applications in facility management.

On the Development of a Digital Data Management Platform for Battery Material and Processing Data

Research on battery materials and systems is broader than ever and a large amount of data is being generated. The data collected is often challenging to correlate or compare because they are not compiled in a structured way since no generally uniform data structure exists yet in the battery field. Besides, manufacturing steps and analysis parameters vary enormously from laboratory scale to industrial scale, from research institutions to consortia.Within the "DigiBatMat project" we have created a digital platform for battery material data and processing data that combines collected material data and knowledge within a defined structure based on taxonomies and ontologies. The structure of the digital platform was developed to cover the Battery LabFactory Braunschweig (BLB) pilot-scale production line for lithium-ion cells while being compatible with other battery production processes, for example at the laboratory bench scale. The platform’s underlying data structure is divided into an “electrode production” and “cell production” taxonomy, providing input modules for the most typical battery material and cell characterization methods. This approach offers a commercially available platform with a defined structure, which still allows flexibility for individual electrode and cell productions through option based input. Due to this structure, we aim to go beyond simple data storage by implementing functionalities that allow users to work with and analyze the data within the digital platform. Beside the functionalities we have implemented so far, which are shown here, we also aim to develop further functionalities that enable the identification of new correlations that are otherwise difficult or even impossible to obtain.A consistent data set is necessary to build up and validate such a digital platform. Hence, we specifically generated “reference” data and tested their management and analysis with the platform. We focus on three key use cases of collecting process data and characterizing electrodes and cells properties along the production process. In one of these key use cases we compare state-of-the-art cathodes with Ni-rich active materials, namely NMC811 and NCA. Data are collected from cells with such cathodes with varying loadings and electrode densities. Characterization of the electrodes includes, among other things, microscope data analysis of the microstructure and electrochemical characterization such as electrochemical impedance spectroscopy and rate capability. By showing how raw data can be imported into the database, processed and analyzed, we aim to demonstrate the possibilities and advantages of our digital platform.

Cost analysis of IPv6 distributed mobility management protocols in comparison with TFMIPv6.

The past decade has witnessed a significant evolution in the role of the Internet, transitioning from individual connectivity to an integral aspect of various domains. This shift has prompted a move in IP paradigms from hierarchical to distributed architectures characterized by decentralized structures. This transition empowers efficient data routing and management across diverse networks. However, traditional distributed mobility management (DMM) protocols, reliant on tunneling mechanisms, incur overheads, costs, and delays, exacerbating challenges in managing the exponential growth of mobile data traffic. This research proposes Tunnel-Free Mobility for IPv6 (TFMIPv6) as a solution to address the shortcomings of existing DMM protocols. TFMIPv6 eliminates the need for tunneling, simplifying routing processes and reducing latency. A comprehensive cost analysis and performance evaluation are conducted, comparing TFMIPv6 with traditional protocols such as MIPv6, PMIPv6, FMIPv6, and HMIPv6. The study reveals significant improvements with TFMIPv6. Signaling costs are reduced by 50%, packet delivery costs by 23%, and tunneling costs are completely eliminated (100%). Real-world network traffic datasets are used for simulation, providing statistical evidence of TFMIPv6's efficacy in supporting an uninterrupted movement of IPv6 data across networks.

Innovative Wearable Sweat Sensor Array for Real-Time Volatile Organic Compound Detection in Noninvasive Diabetes Monitoring.

Wearable sweat sensors are reshaping healthcare monitoring, providing real-time data on hydration and electrolyte levels with user-friendly, noninvasive devices. This paper introduces a highly portable two-channel microfluidic device for simultaneous sweat sampling and the real-time detection of volatile organic compound (VOC) biomarkers. This innovative wearable microfluidic system is tailored for monitoring diabetes through the continuous and noninvasive tracking of acetone and ammonia VOCs, and it seamlessly integrates with smartphones for easy data management. The core of this system lies in the utilization of carbon polymer dots (CPDs) and carbon dots (CDs) derived from monomers such as catechol, resorcinol, o-phenylenediamine, urea, and citric acid. These dots are seamlessly integrated into hydrogels made from gelatin and poly(vinyl alcohol), resulting in an advanced solid-state fluorometric sensor coating on a cellulose paper substrate. These sensors exhibit exceptional performance, offering linear detection ranges of 0.05-0.15 ppm for acetone and 0.25-0.37 ppm for ammonia, with notably low detection limits of 0.01 and 0.08 ppm, respectively. Rigorous optimization of operational parameters, encompassing the temperature, sample volume, and assay time, has been undertaken to maximize device performance. Furthermore, these sensors demonstrate impressive selectivity, effectively discerning between biologically similar substances and other potential compounds commonly present in sweat. As this field matures, the prospect of cost-effective, continuous, personalized health monitoring through wearable VOC sensors holds significant potential for overcoming barriers to comprehensive medical care in underserved regions. This highlights the transformative capacity of wearable VOC sweat sensing in ensuring equitable access to advanced healthcare diagnostics, particularly in remote or geographically isolated areas.

MAN Network Optimization Through EIGRP Dynamic Routing and DUAL Algorithm: A Study Using Cisco Packet Tracer

This research examines the optimization of the Metropolitan Area Network (MAN) through the implementation of the Enhanced Interior Gateway Routing Protocol (EIGRP) and the Diffusing Update Algorithm (DUAL) using the Cisco Packet Tracer. The main goal is to improve the efficiency of routing and data management in MAN networks. This study involves designing a network topology with three routers, setting appropriate IP configurations, and implementing EIGRP and the DUAL algorithm. The results show significant improvements in routing efficiency and data management, confirming the importance of selecting appropriate routing protocols and network configurations for MAN network optimization. These findings contribute to network optimization practices, providing important insights for the development of more efficient urban communications infrastructure.

FUTURE TRENDS IN SQL DATABASES AND BIG DATA ANALYTICS: IMPACT OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE

This study systematically reviews the integration of machine learning (ML) and artificial intelligence (AI) into SQL databases and big data analytics, highlighting significant advancements and emerging trends. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive review of 60 selected articles published between 2010 and 2023 was conducted. The findings reveal substantial improvements in query optimization through ML algorithms, which adapt dynamically to changing data patterns, reducing processing times and enhancing performance. Additionally, embedding ML models within SQL databases facilitates real-time predictive analytics, streamlining workflows, and improving the accuracy and speed of predictions. AI-driven security systems provide proactive and real-time threat detection, significantly enhancing data protection. The development of hybrid systems that combine relational and non-relational databases offers versatile and efficient data management solutions, addressing the limitations of traditional systems. This study confirms the evolving role of AI and ML in transforming data management practices and aligns with and extends previous research findings.

Design and optimization strategy of electricity marketing information system supported by cloud computing platform

This paper provides an in-depth discussion on the comprehensive requirements analysis, design implementation, algorithm optimization, and experimental evaluation of an electric power marketing information system, aiming to build a modern information system that is efficient, secure, and user-friendly. In the requirements analysis phase, the importance of business process optimization, data management analysis, security compliance, system integration and scalability is emphasized, while the diversified needs of end customers are considered. For the design and implementation part, the system architecture is based on microservices and cloud-native technologies to ensure high performance and security; and modularized development is achieved through Spring Boot, Vue.js and other technology stacks. For algorithm optimization, LSTM is used for power demand prediction and anomaly detection by combining integrated learning and self-encoder, which improves the prediction accuracy and anomaly identification capability. Experimental evaluation shows that the system demonstrates good performance, security and scalability in cloud computing environment, and the cost-effectiveness is significantly better than traditional deployment.

Efficient and Secure Management of Medical Data Sharing Based on Blockchain Technology

In the current landscape of medical data management, processing data across diverse institutions and maximizing their value are paramount. However, traditional methods lack a secure and efficient mechanism for end-to-end traceability and supervision, posing challenges in distributed scenarios lacking mutual trust. Leveraging blockchain’s decentralized, tamper-proof, and traceable features, this paper introduces a blockchain-based medical data management platform. This platform enables full-process management of raw data, operational behaviors, intermediate data, and final data, meeting the needs of trusted storage and supervision of data. We propose two methods, namely, naive method and DAG-based method, to realize forward tracking and backward tracing of medical data stored on the blockchain, respectively. We validated and analyzed the storage and query performance of the medical data management platform on real medical data, and we also conducted experimental analyses on the efficiency of the proposed traceability algorithm under different data scales and processing path lengths. The results demonstrate that our platform and traceability methods effectively meet the management needs of medical data distributed across institutions.