Data Management Framework Research Articles

BIG DATA AND CHRONIC DISEASE MANAGEMENT THROUGH PATIENT MONITORING AND TREATMENT WITH DATA ANALYTICS

This systematic review investigates the role of predictive analytics in chronic disease management, focusing on its capacity to predict disease progression, reduce hospitalizations, and enhance patient outcomes. A total of 35 peer-reviewed articles were analyzed to evaluate how predictive models, utilizing data from electronic health records (EHRs), wearable devices, and real-time monitoring systems, are applied in managing chronic diseases such as diabetes and cardiovascular conditions. The findings indicate that predictive models have significantly improved early disease detection accuracy, with studies showing improved forecasting of heart failure exacerbations and notable reductions in hospital readmissions due to timely interventions. Additionally, many studies reported a reduction in healthcare utilization through predictive analytics-driven early interventions. However, challenges regarding data quality and model accuracy were frequently cited, particularly concerning data integration and harmonization across various healthcare systems. Ethical and privacy concerns, including data security and algorithmic bias, were also highlighted, underscoring the need to address these issues for responsible and equitable use of predictive analytics. This review affirms the growing impact of predictive analytics in chronic disease management, while calling for advancements in data management and ethical frameworks to maximize its potential.

BIM and IFC Data Readiness for AI Integration in the Construction Industry: A Review Approach

Building Information Modelling (BIM) has been increasingly integrated with Artificial Intelligence (AI) solutions to automate building construction processes. However, the methods for effectively transforming data from BIM formats, such as Industry Foundation Classes (IFC), into formats suitable for AI applications still need to be explored. This paper conducts a Systematic Literature Review (SLR) following the PRISMA guidelines to analyse current data preparation approaches in BIM applications. The goal is to identify the most suitable methods for AI integration by reviewing current data preparation practices in BIM applications. The review included a total of 93 articles from SCOPUS and WoS. The results include eight common data types, two data management frameworks, and four primary data conversion methods. Further analysis identified three barriers: first, the IFC format’s lack of support for time-series data; second, limitations in extracting geometric information from BIM models; and third, the absence of established toolchains to convert IFC files into usable formats. Based on the evidence, the data readiness is at an intermediate level. This research may serve as a guideline for future studies to address the limitations in data preparation within BIM for AI integration.

The Role of Edge Computing in Driving Real-time Personalized Marketing: a Data-driven Business Perspective

The fast pace of marketing innovation in the digital age has sparked a growing need for instant, personalized conversations with customers. This study investigates the vital significance of edge computing in elevating real-time personalized marketing by reducing latency, improving speed of data processing, and allowing companies to quickly, data-driven decisions. Through a rigorous investigation, the research reports on the ways edge computing solves problems linked to cloud solutions, like bandwidth issues and privacy matters, providing a more effective framework for localized data management. Conducting a mixed-methods study using case studies and quantitative performance metrics, this research showcases important enhancements in consumer engagement and conversion rates amongst enterprises that have implemented edge computing for their personalized marketing efforts. Findings indicate a reduction of 30% in data transfer latency and a 25% higher level of interactions with customers in real time as a result of marketing activities made possible by the edge. The paper wraps up with a look at the innovation of this method in the face of technological innovations and presents recommendations for firms attempting to raise consumer engagement via personalized marketing approaches facilitated by edge computing.

Development of GIS-based framework for reservoir data management and quality evaluation

Abstract Accurately measuring and understanding reservoirs is critical for ensuring successful reservoir development, reservoir development evaluation have become an increasingly important issue in reservoir development engineering and present many challenges, particularly when attempting to integrate and visualize multiple types of data. This paper presents the methodologies, techniques and integrated services adopted for the design and implementation of A quantitative oil and gas reservoir data management and evaluation system. The system is an integrated application based on petroleum geology and GIS technology for managing, displaying, administering and analyzing petroleum geological data. In addition, the system is capable of evaluating reservoir quality and input data quality which provides effective recommendations for the design and preparation of reservoir development programs. In this study, a GIS-based quantitative evaluation module was developed considering both reservoir and input data quality. In this case, the reservoir quality index is determined by five major elements, and the input data quality is determined by the quality of six data sources. The weights of each element are determined by principal component analysis (PCA). Developed a raw data output processing software in C# using ArcGIS engine functions, which automatically generates visual maps and reservoir quality assessment information. The feasibility of the platform is verified by using the geological data of Linfen gas field Formation in the eastern slope of Shanxi. The integrated framework system proposed in this study enables decision-makers and the publics to quickly response to oil reservoir development events and allows an easy adaptation to other regional and scientific domains related to real-time monitoring and evaluating.

Blockchain-based cloud-edge collaborative data management for human-robot collaboration digital twin system

Human-robot collaboration demonstrates broad application prospects in product customization. Digital twin represents an advanced real-virtual interaction technology that plays an essential role in enhancing perception and interaction for human-robot collaboration. A digital twin-based human-robot collaboration system has been proposed to devise collaborative strategies, simulate collaborative processes, and ensure human safety. However, there exist research gaps in implementing human-robot collaboration digital twin systems. A significant challenge lies in constructing data models for describing data types and content in human-robot collaboration digital twin systems. Additionally, addressing data management aspects, including data sharing and storage, is crucial for the effective operation of human-robot collaboration digital twin systems. To bridge existing deficiencies, a novel approach is introduced for managing data in human-robot collaboration digital twin systems through a blockchain-based cloud-edge collaborative method. Initially, a conceptualization of the human-robot collaboration digital twin system alongside a cloud-edge data management framework is introduced. Subsequently, a data model is delineated to outline data categories and contents of human-robot collaboration digital twin systems. Following this, an exploration is conducted on methodologies for data sharing and storage utilizing blockchain and cloud technologies. Ultimately, the efficacy of the proposed approaches is validated through a case study.

Digital transformation of the future of forestry: an exploration of key concepts in the principles behind Forest 4.0

This paper looks at the incorporation of blockchain and Internet of Things (IoT) technologies into Forest 4.0, a sector that harnesses advanced tools such as artificial intelligence and big data for efficient and sustainable forest monitoring and management. The synergy of blockchain and IoT has gained significant attention, offering a secure and decentralized framework for data management, traceability, and supply chain oversight. The provided use cases demonstrate how these technologies improve forest practices, with insight into smart contract implementation and decentralized systems for sustainable forest management. The major findings imply that digital technologies such as blockchain, IoT, AI, WSNs, etc. can help improve forest management sustainability, efficiency and transparency, and integration of these technologies can provide significant information for decision-making and resource allocation, as well as improve supply chain transparency and sustainable forest practices.

Application of edge computing and IoT technology in supply chain finance

This study proposes an IoT data management framework based on blockchain and edge computing and conducts detailed simulation experiment design and performance evaluation for the field of supply chain finance. Previous methods often struggled with high latency, limited scalability, and inadequate risk management, and to address these issues, the experimental platform includes traditional centralized systems, distributed systems, blockchain-based systems, and edge computing + blockchain systems. By monitoring indicators such as data processing delay, data transmission delay, data retrieval time, system throughput, and data integrity of each platform, and introducing evaluation formulas for credit risk, operational risk, and market risk, the performance of different systems in processing supply chain financial data is comprehensively analyzed. The experimental results show that the edge computing + blockchain system performs well in data processing efficiency, security, real-time, and system throughput, especially under high load conditions. Our market risk value is reduced to 0.015. This framework improves the efficiency and security of data transmission and effectively reduces credit risk and operational risk, providing an efficient and reliable solution for data management in supply chain finance.

Secured Public Health data Management Framework using the Internet of Things in a Smart City Environment

The Internet of Things (IoT) is being embraced and impacts various aspects of lives. Integrating IoT technology in the healthcare sector paves the way for continuous remote monitoring of individuals' health status. The healthcare (HC) sector has traditionally produced substantial data. Due to the implementation of the IoT, this quantity has significantly increased. Ensuring proper HC storage is imperative to derive meaningful insights from the data. This study presents a public health (PH) framework that utilizes cloudlets and IoT technology. The primary objective of this PH architecture is to facilitate the retrieval of real-time data by using cloudlets. The research provides and executes an HC data management system to store vast HC data and handle consumer queries to retrieve HC information. The efficacy of the proposed model has been evaluated in terms of data transmission time, energy usage, query response time, and data packet loss. The proposed model outperforms existing PH systems by comparing the assessment findings of the proposed approach with the performance of typical PH systems.

A customizable data management framework for high-repetition-rate high-energy-density science

The high-energy-density (HED) physics community is moving toward a new paradigm of high-repetition-rate (HRR) operation. To fully leverage the scientific power of HRR HED facilities, all of the components of each subsystem (laser, targetry, and performance diagnostics) must be connected and synchronized in a reliable and robust manner while the data acquired are tagged and archived in real time. To this end, GA has begun developing a generalized NoSQL-database framework, the MongoDB repository for information and archiving. An organizational strategy has been developed that shifts HED data organization from a shot-based to a diagnostic-based approach in order to increase archival and retrieval efficiency that lends itself to optimization applications. This work is a first step in pushing HRR HED science toward data management solutions that emphasize machine actionability and aim to stimulate community engagement to define data standards in HED science.

Accelerating imaging research at large-scale scientific facilities through scientific computing.

To date, computed tomography experiments, carried-out at synchrotron radiation facilities worldwide, pose a tremendous challenge in terms of the breadth and complexity of the experimental datasets produced. Furthermore, near real-time three-dimensional reconstruction capabilities are becoming a crucial requirement in order to perform high-quality and result-informed synchrotron imaging experiments, where a large amount of data is collected and processed within a short time window. To address these challenges, we have developed and deployed a synchrotron computed tomography framework designed to automatically process online the experimental data from the synchrotron imaging beamlines, while leveraging the high-performance computing cluster capabilities to accelerate the real-time feedback to the users on their experimental results. We have, further, integrated it within a modern unified national authentication and data management framework, which we have developed and deployed, spanning the entire data lifecycle of a large-scale scientific facility. In this study, the overall architecture, functional modules and workflow design of our synchrotron computed tomography framework are presented in detail. Moreover, the successful integration of the imaging beamlines at the Shanghai Synchrotron Radiation Facility into our scientific computing framework is also detailed, which, ultimately, resulted in accelerating and fully automating their entire data processing pipelines. In fact, when compared with the original three-dimensional tomography reconstruction approaches, the implementation of our synchrotron computed tomography framework led to an acceleration in the experimental data processing capabilities, while maintaining a high level of integration with all the beamline processing software and systems.

The Impact of Intelligence Gathering, Risk Analysis, and Scenario Planning on Defense Policy Formulation

In the evolving landscape of global security, the integration of intelligence gathering, risk analysis, and scenario planning is paramount for effective defense policy formulation. This study aims to underscore the critical role of these elements in contemporary defense strategies. Employing qualitative research methods, particularly secondary data analysis, this research investigates the transformative impact of artificial intelligence (AI) and machine learning (ML) on threat assessments, the benefits and challenges of big data analytics in risk analysis, and the value of interdisciplinary perspectives in scenario planning. The findings reveal that AI and ML significantly enhance the accuracy and reliability of threat assessments by enabling real-time data processing and predictive analytics. However, challenges such as data privacy and algorithmic biases persist. Big data analytics offers substantial benefits in identifying and mitigating emerging threats but requires robust data management frameworks to address issues of data quality and integration. Additionally, scenario planning is highlighted as a strategic tool that enhances defense strategies by anticipating various future scenarios and enabling proactive measures. Furthermore, the integration of interdisciplinary perspectives in scenario planning fosters more robust and adaptable defense policies, ensuring a comprehensive approach to security challenges. In conclusion, the integration of advanced technologies and interdisciplinary methods in intelligence gathering, risk analysis, and scenario planning is crucial for developing resilient and adaptive defense policies.

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.

A Conceptual Framework for Data Governance and Management in Digital Taxation: A Systematic Review of Electronic Invoicing

A Conceptual Framework for Data Governance and Management in Digital Taxation: A Systematic Review of Electronic Invoicing

Improved Dynamic Regression Framework for Effective Data Management in Wireless Networks on Cloud-assisted Internet of Everything Platform

Improved Dynamic Regression Framework for Effective Data Management in Wireless Networks on Cloud-assisted Internet of Everything Platform

Intelligent privacy‐preserving data management framework for medicine supply chain system

AbstractIn today's era, the pharmaceutical industry has integrated blockchain to secure the sensitive information of medicines, wherein public and private blockchains are used to preserve the security and privacy of the medicine supply chain data. However, conventional blockchains often limit scalability due to limited storage. Moreover, blockchain has loopholes; for example, it is not able to prove the validity of the data prior to being stored in the blockchain, which leads to fake data being added to the blockchain. As a result, it causes an issue of data provenance. Motivated by this, the proposed framework incorporated artificial intelligence (AI) algorithms to enhance the efficiency of the medicine supply chain data. The proposed framework integrated machine learning (ML) and blockchain, where ML classifies the valid and invalid data of the medicine supply chain, whereas blockchain stores only valid data and maintains its security and privacy. This identification helps the blockchain to verify medicine supply chain data before adding it to the blockchain. Additionally, we employed an InterPlanetary file system (IPFS) that saves medicine supply chain data and computes its hash to offer decentralized storage. Further, this hash data is stored on a private Hyperledger Fabric blockchain, which requires minimal storage instead of storing an entire large file. This minimal storage optimizes the process of data storage and retrieval in the Hyperledger Fabric blockchain, which enhances the scalability of the proposed framework. Finally, the result of the proposed framework is assessed in two phases: ML and blockchain, wherein the ML model's performance is measured by statistical measures and the blockchain‐based result is assessed using several performance parameters such as throughput is around (618 transactions per second), latency (0.12 s), response time (11 s) and data rate (282 Mbps).

More is better? The role of strategic data management in a lean manufacturing process

Purpose Lean manufacturing has been pivotal in emphasizing the reduction of cycle times, minimizing manufacturing costs and diminishing inventories. This research endeavors to formulate a lean data management paradigm, through the design and execution of a strategic edge-cloud data governance approach. This study aims to discern anomalous or unforeseen patterns within data sets, enabling an efficacious examination of product shortcomings within manufacturing processes, while concurrently minimizing the redundancy associated with the storage, access and processing of nonvalue-adding data. Design/methodology/approach Adopting a lean data management framework within both edge and cloud computing contexts, this study ensures the preservation of significant time series sequences, while ascertaining the optimal quantity of normal time series data to retain. The efficacy of detected anomalous patterns, both at the edge and in the cloud, is assessed. A comparative analysis between traditional data management practices and the strategic edge-cloud data governance approach facilitates an exploration into the equilibrium between anomaly detection and space conservation in cloud environments for aggregated data analysis. Findings Evaluation of the proposed framework through a real-world inspection case study revealed its capability to navigate alternative strategies for harmonizing anomaly detection with data storage efficiency in cloud-based analysis. Contrary to the conventional belief that retaining comprehensive data in the cloud maximizes anomaly detection rates, our findings suggest that a strategic edge-cloud data governance model, which retains a specific subset of normal data, can achieve comparable or superior accuracy with less normal data relative to traditional methods. This approach further demonstrates enhanced space efficiency and mitigates various forms of waste, including temporal delays, storage of noncontributory normal data, costs associated with the analysis of such data and excess data transmission. Originality/value By treating inspected normal data as nonvalue-added, this study probes the intricacies of maintaining an optimal balance of such data in light of anomaly detection performance from aggregated data sets. Our proposed methodology augments existing research by integrating a strategic edge-cloud data governance model within a lean data analytical framework, thereby ensuring the retention of adequate data for effective anomaly detection.

CONVERGENCE OF INTERNET OF THINGS (IOT) AND EDGE COMPUTING: ENHANCING SCALABILITY AND EFFICIENCY THROUGH CLOUD INTEGRATION: A REVIEW

This paper investigates the convergence of edge computing, cloud computing, and the Internet of Things (IoT). Given the exponential growth of IoT devices, a robust infrastructure is necessary to manage the vast volumes of generated data. Cloud computing offers on-demand access to shared computing resources over the internet, but solely relying on it may pose security risks and connectivity issues. Edge computing mitigates these concerns by enabling faster processing closer to devices, reducing dependency on constant connectivity. The paper examines the advantages and challenges of both edge and cloud computing for IoT and explores how they can complement each other to create a resilient framework for IoT data management. Additionally, it assesses the current research landscape and proposes potential avenues for future development in this field.

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.

Online Education Big Data Management and Mining Based on Intelligent Technology

The advent of online education platforms has revolutionized traditional learning paradigms, providing unprecedented access to educational resources worldwide. However, the vast amounts of data generated by these platforms present significant challenges in terms of effective management and utilization. This paper proposes a comprehensive approach to address these challenges through the integration of big data management and mining techniques, supported by intelligent technologies. By harnessing the power of advanced data management strategies and cutting-edge machine learning algorithms, our approach aims to unlock valuable insights from online education data, thereby enhancing the learning experience and improving educational results. This paper presents a systematic framework for online education big data management and mining, encompassing data collection, storage, preprocessing, analysis, and visualization. Furthermore, we discuss the role of intelligent technologies such as artificial intelligence, natural language processing, and predictive analytics in optimizing the process of data mining and knowledge discovery. Through case studies and experimental evaluations, we demonstrate the effectiveness and applicability of our approach in real-world online education scenarios. Our research contributes to the growing body of knowledge in the field of educational data mining and underscores the potential of intelligent technology in shaping the future of online learning.

A novel six-dimensional digital twin model for data management and its application in roll forming

Roll forming is a cost-effective, efficient, and flexible plastic processing technique that involves the gradual transverse bending of metal strips using sequentially arranged forming dies. It is an important technological approach in response to lightweight, energy efficiency, and safety in various sectors, including new energy vehicles, aerospace, and rail transportation. However, the complexity of the process, diverse data types, and the effects of coupling and nonlinearity have led to challenges in process stability and quality control. The unclear impact mechanism of heterogeneous time-series data from multiple sources on product quality significantly hinders the development and widespread adoption of roll forming in industries. To facilitate the study of its mechanisms and optimize the control of forming quality, this paper introduces a digital twin (DT) model tailored for the roll forming field. It also presents a product-oriented feature data management framework based on the DT model. This framework facilitates feature-based data categorization across the complete lifecycle, enabling advanced data analysis in the roll forming domain. The feasibility and advantages of the proposed model and framework are validated through application to produce hat-shaped components. It is hoped that the work can provide valuable insights to the digitalization and intelligent transformation of roll forming field.