Data Management Research Articles

Digital Twins in Manufacturing: A Survey of Current Practices and Future Trends

Digital Twin (DT) is the advanced tool of smart manufacturing which provides a proper way to model, analyse, and control the physical systems. A DT is the lifelike rendition of a physical object or phenomenon that uses real-time actual phenomena data to depict its actions, efficiency, and responses within the surrounding context. This technology allows electronic manufacturers to obtain a real-time stream of their systems, in which predictive maintenance, increased operation effectiveness, and improved product quality can be attained. By developing an ever-evolving replica of the physical structures, systems or even operation lines, Digital Twins enable the functionality of exploring various demanding circumstances and schemata, all in order to make effective decisions. When the manufacturing environments are rapidly changing, and there are more interconnections between them, the use of Digital Twins is especially useful. They continue improvement by highlighting problems, forecasting failure, and making choices based on data. The purpose of this article is to examine smart manufacturing via the lens of IoT DT technology. It gives general information about its uses, such as in the processes, time-saving and safety measures. As well, the paper considers the difficulties related to the practical application of DT, including costs, data management, and requirements for the formation of standards. Thus, based on analysing future developments and the role of Digital Twins as an innovation enabler in different industries, this study can benefit scholars, practitioners, and politicians who look forward to unlocking the potential of this innovative solution.

Religious Mandatory Premarital HIV Testing; Assessment of Perceptions on Ethical Concerns Among Healthcare Workers in Kwaebibirem

Objective: This cross-sectional survey describes healthcare workers’ (HCWs’) perceptions on religious mandatory premarital HIV testing- (RMPmHT) associated ethical concerns. Methodology: An online survey was completed with an e-questionnaire, circulated via WhatsApp, (a freeware, centralized instant messaging cross-platform). This involved 530 participants and data were descriptively analyzed using Epi info 3.5.1. Results: The findings illustrate that HCWs, mostly aged 30 or younger with a mean age of 30.4 (±7.2), were largely familiar with RMPmHT, (58.5%). Despite general acceptance, disclosing positive results to RO leadership was widely discouraged. HCWs considered RMPmHT non-intrusive, yet paradoxically believed it increased the risk of stigmatization. Despite an unclear ethical paradigm and state silence on RMPmHT, HCWs didn't see knowledge of a positive result as a 'right' for RO leadership. Policy frameworks for 'medico-legal' permission and result disclosure were seen as ambiguous. Concerns about fragile confidentiality structures persist, and the prohibition of marriages for HIV-positive or RMPmHT-dissenting couples was widely deprecated. Conclusion: RMPmHT, tenably, infringes upon the right to marry and found a family, bodily integrity, privacy, and information as negative results may subtly be a precondition for marriage. Advocacy for the need to situate RMPmHT in a clear ethical paradigm remains imperative. Policy frameworks guiding documentation of consent processes, HIV couple discordancy, counselling, information, disclosure, data management and linking RMPmHT services to public health institutions should be engendered.

Innovative approaches in data management and cybersecurity: Insights from recent studies

Innovative approaches in data management and cybersecurity: Insights from recent studies

Aplikasi Terintegrasi SIPONTREN dalam Meningkatkan Efisiensi Administrasi di Yayasan Nurul Amin

This community service program aims to enhance data management efficiency at Yayasan Nurul Amin by implementing the Pangkalan Data Pesantren (PDTREN) application system using the Asset-Based Community Development (ABCD) approach. The main issue faced was the manual management of student, financial, and administrative data, leading to reporting delays and difficulties in monitoring. The goal of this service is to develop an information system that integrates and simplifies data management, and to improve technology skills within the foundation. The approach used is ABCD, which focuses on leveraging the assets and capabilities within the community, involving needs analysis, system design, in-person testing with 20 users, and training. The data collected includes system testing results, user feedback, and evaluation of training effectiveness. The conclusion of this program shows that the SIPONTREN system successfully improved data management efficiency, expedited reporting processes, and enhanced student monitoring. The training provided also significantly increased the technology skills of the foundation’s staff, supported system sustainability, and empowered the community. This program contributes significantly to the modernization of educational management in pesantren and can serve as a model for similar educational institutions

Machine learning models for personalised healthcare on marketable generative-AI with ethical implications

Personalised healthcare, underpinned by a deep understanding of individual patient variability, demands innovative solutions. Machine Learning (ML) models offer a promising avenue to achieve this by facilitating the development of enhanced Digital Twins (DTs). This research proposes a novel framework for creating DTs tailored to individual patients, considering not only physical attributes but also the intricate interplay of social, and biological factors. By capturing this comprehensive patient profile, ML-powered DTs have the potential to revolutionise healthcare by enabling predictive, preventive, and personalised care strategies, ultimately leading to improved patient outcomes and the development of a marketable, trust-worthy product. Current healthcare solutions lack personalisation, often failing to consider individual differences in disease presentation and response to treatment. Traditional DTs in healthcare often adopt a disease-centric or organ-specific approach, thereby restricting their capacity to deliver comprehensive, personalised care. To address this limitation, we propose a holistic Artificial Intelligence (AI) framework centered on ML models. Initially focusing on diabetes, our research aims to enhance diagnosis, treatment, and predictive capabilities through personalised insights, thereby optimising patient outcomes and care management. The benefits identified with our ML model are early disease prevention and risk stratification, optimised treatment planning and therapy selection, enhanced patient-physician communication and shared decision-making, reduced healthcare costs and improved resource allocation. Our models are designed to optimise patient care while prioritising safety and societal benefit. To ensure this, we have conducted a thorough assessment of potential ethical implications. Key challenges identified include data privacy and security, algorithmic bias, diagnostic accuracy, data interoperability and standardisation, integration with existing healthcare systems, ethical management of sensitive patient data, refinement of ML methodologies, addressing legal and ethical AI challenges, and suggesting robust ethical guidelines. A comprehensive evaluation of accuracy, reliability, and associated risks will be conducted prior to full-scale integration into the healthcare ecosystem to establish a robust ethical framework for our research models.

Open Source based Smart Meter Real-time Data Acquisition and Management using MQTT Protocol

Open Source based Smart Meter Real-time Data Acquisition and Management using MQTT Protocol

PACMMOD Volume 2 Issue 4: Editorial

We are pleased to present the 4th issue of Volume 2 of PACMMOD. This issue contains papers that were submitted to the SIGMOD research track in January 2024. Papers accepted in this issue have been invited for presentation in the research track of the ACM SIGMOD Conference on Management of Data 2025, to be held in Berlin, Germany. We look forward to seeing you in Berlin from the 22nd to the 27th June, 2025.

Digital data collection protocols and template design for an oral health survey of elite athletes in Ireland

AimTo design a digital template for form building, data collection and data management to assess the diet and oral health of elite athletes.Subject and methodsA web-based open-source platform, KoboToolbox (KBTB), was used to aid design and manufacture of a digital data collection form for use in oral health surveys. KBTB frontend form builder was used to create a customised questionnaire. Dental charting systems for the chosen indices were designed using XLSForms, converted to XForm and uploaded to the KBTB interface to create a single form for digital data input. This template was used to collect data for a cross-sectional study investigating the diet and oral health of a convenience sample of elite athletes in Ireland.ResultsA digital data collection (DDC) form was designed for use in oral health surveys. The customised form was then used to collect data for a survey of 88 elite athletes on the KBTB platform. Dietary behaviours were recorded by the participants using KBTB. Following data collection, the dataset was downloaded from KBTB and cleaned for subsequent analysis.ConclusionThe design of a versatile form and oral health questionnaire template using digital tools customised for elite athletes was useful for efficient, comprehensive field data collection. DDC tools are convenient for data form design, collection, and storage. This template can be easily modified for use by other researchers planning oral health surveys.

70-Gene signature-guided adjuvant systemic treatment adjustments in early-stage ER+ breast cancer patients: 7-year follow-up of a prospective multicenter cohort study.

A previous prospective multicenter study revealed the change of the oncologists' chemotherapy advice due to the 70-Gene signature (GS) test result in half of the estrogen receptor-positive (ER+) invasive early-stage breast cancer patients with disputable chemotherapy indication. This resulted in less patients receiving chemotherapy. This study aims to complement these results by the 7-year oncological outcomes according to the 70-GS test result and the oncologists' pre-test advice. Patients operated for early-stage ER+ breast cancer with disputable chemotherapy indication, had been prospectively included between 2013 and 2015. Oncologists were asked whether they intended to administer adjuvant chemotherapy before deployment of the 70-GS test. Information on adjuvant systemic treatment and oncological outcome was obtained through active follow-up by data managers of the Netherlands Cancer Registry. The primary endpoint of this study was distant metastasis-free survival (DMFS) according to the genomic risk. Exploratory analyses were done to evaluate DMFS in relation to the oncologists' pre-test advice. After a median follow-up of 7 years, distant metastases were diagnosed in 23 of the 606 patients (3.8%) and 36 (5.9%) patients had died. The DMFS rate for the 357 70-GS genomic low-risk patients was 94.2% (95% CI 91.2-96.2) and 89.1% for the 249 genomic high-risk patients (95% CI 84.3-92.4). Of the low-risk patients 3% had received chemotherapy compared to 80% of the high-risk patients. For the subgroups based on the pre-test oncologists' advice (no chemotherapy/chemotherapy/unsure) there were no clinically relevant differences in DMFS (89.8, 93.2 and 92.0%, respectively), while comparable proportions of patients had received chemotherapy. In patients with early-stage ER+ breast cancer with a disputable chemotherapy indication it is sensible to deploy the 70-GS to better select patients for adjuvant chemotherapy.

Managing the Rohingya Refugee Crisis in Aceh: Strategies for Conflict Management

This study examines Aceh's challenges in managing Rohingya refugees and proposes conflict management strategies. Using a qualitative approach, it identifies issues in implementing Presidential Regulation 125/2016 and potential conflicts with local communities. A three-phase conflict management framework is proposed: prevention, cessation, and post-conflict recovery. Prevention strategies include developing institutional mechanisms, early warning systems, and community capacity building. Conflict cessation focuses on establishing specialized task forces and strengthening security systems. Post-conflict recovery emphasizes inclusive economic initiatives, sustained dialogue forums, integrated data management, comprehensive relocation studies, and psychosocial support. The research highlights the importance of collaborative efforts involving government agencies, international organizations, and local communities in addressing the complex challenges of refugee management

CYBERSECURITY IN CLINICAL PRACTICE

Cybersecurity is a concern for patient safety. Recent cyberattacks on healthcare facilities around the world have exposed the risks to patients: from treatment delays due to hospital and clinic closures, to the threat harm from personal data theft, to patient death. The recent Covid-19 pandemic has further increased cyberattacks against healthcare organizations. Frontline healthcare workers are often warned about the dangers of poor data management and advised to take precautions to ensure data security. However, many workers are overwhelmed by conflicting administrative priorities, cybersecurity risks go unnoticed.

The Critical Role of Role-Based Access Control (RBAC) in securing backup, recovery, and storage systems

In the contemporary digital landscape, safeguarding data through robust access control mechanisms is crucial. Role-Based Access Control (RBAC) is an effective strategy for managing access to backup and recovery systems, as well as storage devices. RBAC assigns permissions based on user roles, thereby enforcing the principle of least privilege and enhancing data security. This blog explores the significance of RBAC in these contexts and extends its relevance to ransomware and malware threats, as well as disaster recovery. Supported by scientific research and industry case studies, it underscores how RBAC can mitigate risks, ensure regulatory compliance, and improve data management practices.

A Comparative Analysis of Microsoft Fabric and Snowflake: Architectural Paradigms and Performance Metrics in Modern Cloud Data Platforms

Abstract: This article presents a comprehensive comparative analysis of two leading cloud data platforms: Microsoft Fabric and Snowflake. As organizations increasingly rely on cloud-based solutions for data management and analytics, understanding the strengths and limitations of these platforms becomes crucial. This article examines the architectural foundations, functional capabilities, performance metrics, and cost considerations of both platforms. We explore Microsoft Fabric's integrated service model within the Azure ecosystem and Snowflake's multi-cluster shared data architecture, assessing their approaches to data integration, storage, analytics, and machine learning integration. Through empirical performance comparisons and an evaluation of scalability mechanisms, we provide insights into the operational efficiencies of each platform. Additionally, we analyze the security features, compliance standards, and pricing models to offer a holistic view of the total cost of ownership. Our findings reveal distinct advantages in Microsoft Fabric's end-to-end integration and Snowflake's performance in multicloud environments, while also highlighting areas for potential improvement in both platforms. This article aims to serve as a valuable resource for organizations navigating the complex landscape of cloud data solutions, offering evidence-based criteria for platform selection aligned with specific business needs and technological ecosystems

Blockchain-Driven Generalization of Policy Management for Multiproduct Insurance Companies

This article presents a Blockchain-based solution for the management of multipolicies in insurance companies, introducing a standardized policy model to facilitate streamlined operations and enhance collaboration between entities. The model ensures uniform policy management, providing scalability and flexibility to adapt to new market demands. The solution leverages Merkle trees for secure data management, with each policy represented by an independent Merkle tree, enabling updates and additions without altering existing policies. The architecture, implemented on a private Ethereum network using Hyperledger Besu and Tessera, ensures secure and transparent transactions, robust dispute resolution, and fraud prevention mechanisms. The validation phase demonstrated the model’s efficiency in reducing data redundancy and ensuring the consistency and integrity of policy information. Additionally, the system’s technical management has been simplified, operational redundancies have been eliminated, and privacy is enhanced.

Post-project financial auditing as a continuous improvement tool for SMEs

Post-project financial auditing is a critical tool for ensuring transparency, accountability, and financial accuracy within small and medium-sized enterprises (SMEs). As SMEs navigate project-based operations, it is essential to assess the financial outcomes of completed projects to inform future decision-making and resource allocation. This review explores the role of post-project financial auditing as a continuous improvement tool in SMEs, highlighting its ability to enhance financial discipline, improve cost control, and promote long-term financial sustainability. By examining key aspects of project revenues, expenditures, and profitability, post-project financial audits provide insights into financial performance that are essential for strategic planning. These audits help identify budget deviations, resource misallocations, and cost inefficiencies, enabling SMEs to adjust future financial planning and project management practices. In doing so, audits contribute to refining budgeting processes, promoting data-driven decision-making, and reducing financial risks in subsequent projects. The review also emphasizes the benefits of post-project auditing in fostering a culture of financial responsibility within SMEs. Through the audit process, SMEs can strengthen internal controls, monitor compliance with financial regulations, and ensure that project outcomes align with organizational financial goals. Additionally, the integration of audit findings into future financial strategies enables SMEs to continuously improve their profitability, mitigate risks, and enhance overall financial stability. Despite the clear advantages, challenges such as resource constraints, data management issues, and limited audit expertise can hinder the adoption of post-project audits in SMEs. The review offers practical recommendations to address these challenges, advocating for the use of scalable audit frameworks, digital tools, and ongoing staff training to support effective auditing practices. Ultimately, post-project financial auditing represents a vital mechanism for driving continuous improvement and ensuring the financial resilience of SMEs in an increasingly competitive marketplace.

Prevalence of Thyroid Dysfunction among Patients with Type II diabetes Mellitus in Tertiary Care Center: A Descriptive Cross-sectional Study

Introduction: Diabetes mellitus (DM) and thyroid dysfunction (TD) are two of the most prevalent endocrine disorders globally. Both conditions have significant effects on metabolism and are known to influence each other. Thyroid hormones play a crucial role in glucose homeostasis by affecting insulin secretion, sensitivity, and glucose metabolism.Conversely, altered glucose metabolism in diabetes can impact thyroid function, leading to a higher prevalence of thyroid disorders in diabetic patients. Understanding this relationship is important for improving the clinical management of patients with Type II DM. However, there is limited data on the prevalence of thyroid dysfunction in diabetic populations within specific regions. Methods: A cross-sectional descriptive study was conducted from February to September 2023 among 209 patients diagnosed with Type II DM at a tertiary care center after obtaining ethical approval from Institutional Review Committee(Reference No: 367 (6-11) E2). Thyroid function was assessed using thyroid-stimulating hormone (TSH), free T3 (FT3), and free T4 (FT4) levels. Thyroid dysfunction was classified into hypothyroidism and hyperthyroidism based on standard clinical and laboratory criteria. Data management involved entry into Microsoft Excel, verification for accuracy, and subsequent analysis using SPSS version 29.Results: The study included 209 patients (61% female, 39% male), with a mean age of 65.87 ± 13.7 years. The prevalence of thyroid disorders was 77(36.8%): 53(25.3%) hypothyroidism,24 (11.4%) hyperthyroidism among patient with type II diabetes mellitus.Conclusions: Our study shows a high prevalence of thyroid disorders, especially subclinical hypothyroidism, in individuals with type 2 diabetes. Poorly controlled blood glucose (HbA1c ≥ 7.5) significantly increases the risk, underscoring the need for routine thyroid screening in T2DM management

Implementation of the Internet of Things in Centrifuge Calibrators

The implementation of Internet of Things (IoT) technology in centrifuge calibrators brings innovation by enhancing accuracy and efficiency in medical device calibration. This research develops an IoT-based centrifuge calibrator capable of storing calibration data digitally and providing real-time access, using the E18-D80NK sensor for rotational motion detection, which is more cost-effective than conventional laser modules. Measurement results demonstrate high accuracy across various RPM settings (1000, 1500, 2000, 2500, and 3000 RPM), with minimal error and stability within acceptable tolerance limits. The key advantage of IoT implementation lies in efficient calibration data management, enabling real-time access and integration with other systems for effective analysis and monitoring of calibration outcomes. Overall, IoT technology in centrifuge calibration not only enhances healthcare service quality with reliable results but also supports efficient and transparent equipment management in medical settings, offering long-term benefits to healthcare facilities and patients.

The impact of artificial intelligence on regulatory compliance in the oil and gas industry

Artificial Intelligence (AI) is increasingly transforming the regulatory compliance landscape in the oil and gas industry. This paper examines the profound impact of AI on ensuring adherence to complex regulatory frameworks governing this sector. Regulatory compliance in the oil and gas industry involves adhering to a myriad of environmental, safety, and operational regulations, often posing significant challenges due to the volume and complexity of data involved. AI technologies, including machine learning, natural language processing, and predictive analytics, offer innovative solutions to these challenges. AI enhances data management and analysis by automating data collection, processing, and reporting, thereby increasing accuracy and efficiency. Predictive maintenance and risk assessment tools powered by AI can identify potential compliance issues before they arise, allowing for proactive measures. Moreover, AI-driven compliance monitoring systems enable real-time tracking of regulatory adherence, reducing the risk of non-compliance and associated penalties. Automated auditing and inspection processes further streamline compliance checks, ensuring thorough and consistent evaluations. Case studies demonstrate successful AI implementations in regulatory compliance, such as automated reporting systems in offshore drilling and predictive maintenance in pipeline management, which have resulted in improved compliance rates and reduced operational risks. However, the adoption of AI is not without challenges. Issues related to data quality and integration, cybersecurity, and regulatory acceptance pose significant hurdles. Additionally, ethical and legal considerations surrounding AI deployment must be addressed to ensure responsible use. AI holds substantial potential to revolutionize regulatory compliance in the oil and gas industry by enhancing efficiency, accuracy, and proactive risk management. As AI technologies continue to evolve, their integration into compliance processes will likely become more sophisticated, offering greater benefits and addressing current limitations. The future of regulatory compliance in the oil and gas sector will be increasingly shaped by the advancements in AI, driving both operational excellence and adherence to stringent regulatory standards.

API management and cloud integration model for SMEs

In the rapidly evolving technological landscape, Small and Medium-sized Enterprises (SMEs) face significant challenges in maintaining efficient and scalable IT systems. This review outlines a strategic model for API management and cloud integration designed to enhance system performance and optimize cloud-native features. The proposed model focuses on the redesign and optimization of Application Programming Interfaces (APIs) to leverage cloud capabilities effectively, addressing common issues faced by SMEs in system integration and performance. The model begins with a comprehensive assessment of existing API architectures to identify inefficiencies and performance bottlenecks. It emphasizes the need for a cloud-native approach, which includes the adoption of microservices architecture and serverless computing. By redesigning APIs to align with cloud-native principles, the model aims to achieve greater scalability, flexibility, and responsiveness in handling varying workloads. Key components of the model include API versioning strategies, improved security protocols, and enhanced data management practices. The integration of cloud-native features such as containerization, orchestration, and automated scaling ensures that APIs are optimized for performance and reliability. This approach not only streamlines system operations but also reduces the overhead associated with traditional server-based architectures. Furthermore, the model incorporates best practices for continuous integration and continuous deployment (CI/CD), facilitating seamless updates and maintenance of APIs. It also includes monitoring and analytics tools to provide real-time insights into API performance and user interactions, enabling proactive management and optimization. By implementing this strategic model, SMEs can achieve significant improvements in system performance, reduce latency, and enhance overall cloud integration. The model supports the efficient utilization of cloud resources, leading to cost savings and improved operational efficiency. This approach positions SMEs to better compete in the digital economy by leveraging advanced technologies and optimizing their IT infrastructure.

A hybrid architecture for secure data sharing in multi-clouds system

Abstract Cloud computing is one of the most cutting-edge technologies around the world that plays a major role in IT industries and personal use. Several sectors are showing efforts in adopting cloud computing to their services, considering the provided cost reduction and process efficiency. However, outsourcing users’ sensitive data increases the concerns regarding the security, privacy and integrity of stored data. Therefore, there is a need to build a trusting relationship between users and cloud systems. Hence, in this paper, we propose a privacy-preserving framework, called Hybrid and Secure Data Sharing Architecture (HSDSA), for secure data storage in cloud systems. The basic idea of HSDSA is to improve data security in a multi-cloud environment using a combination of cryptography techniques. These techniques ensure that the user has total control over the data generation and management of the decryption without relying on a trusted authority. HSDSA provides removal of centralized file storage distribution and ensures data integrity in the recovery process. We evaluate our contribution under Cloudera, and the results demonstrate the efficiency of HSDSA compared with the existing system.