Real-time Data Integration Research Articles

Digital twins for smart building at the facility management stage: a systematic review of enablers, applications and challenges

PurposeDespite the opportunities of digital twins (DTs) for smart buildings, limited research has been conducted regarding the facility management stage, and this is explained by the high complexity of accurately representing and modelling the physics behind the DTs process. This study thus organises and consolidates the fragmented literature on DTs implementation for smart buildings at the facility management stage by exploring the enablers, applications and challenges and examining the interrelationships amongst them.Design/methodology/approachA systematic literature review approach is adopted to analyse and synthesise the existing literature relating to the subject topic.FindingsThe study revealed six main categories of enablers of DTs for smart building at the facility management stage, namely perception technologies, network technologies, storage technologies, application technologies, knowledge-building and design processes. Three substantial categories of DTs application for smart buildings were revealed at the facility management stage: efficient operation and service monitoring, efficient building energy management and effective smart building maintenance. Subsequently, the top four major challenges were identified as being “lack of a systematic and comprehensive reference model”, “real-time data integration”, “the complexity and uncertainty nature of real-time data” and “real-time data visualisation”. An integrative framework is finally proposed by examining the interactive relationship amongst the enablers, the applications and the challenges.Practical implicationsThe findings could guide facility managers/engineers to fairly understand the enablers, applications and challenges when DTs are being implemented to improve smart building performance and achieve user satisfaction at the facility management stage.Originality/valueThis study contributes to the knowledge body on DTs by extending the scope of the existing studies to identify the enablers and applications of DTs for smart buildings at the facility management stage and the specific challenges.

Augmented Reality and Nursing Care Improvement: Enhancing Patient Outcomes and Nursing Efficiency

Abstract The advanced technology of augmented reality (AR) is still finding its place in various fields, including healthcare. Much of the discussion of AR in this review article revolves around its potential to improve care in nursing, with particular emphasis on the effects on patient outcomes and work satisfaction among nurses. There are challenges in using conventional approaches to nursing education and clinical skill training, which include the availability of resources and real patients for practice. AR solves these issues by updating the learning mode, as students can immerse in virtually real and actual scenarios in clinical settings for nursing students. These simulations include procedures such as intravenous (IV) line insertion and emergency care, as well as the “skills check” that is presented immediately after providing retention of skills and assurance. In clinical applications, AR can display pertinent patient information, including physical examination data, laboratory data, or patient history, on the surface of the patient’s skin or the side of the bed. This real-time data integration increases the level of accuracy in monitoring, minimizes the possibility of human error occurrence, and increases the workflow rate. Moreover, AR can give step-by-step instructions and snap a clinician back to the key general rules of practice, which is highly essential during stressful conditions, such as in an emergency. However, going beyond the traditional roles of education and decision support, AR can help to optimize patients’ direct care and management activities, including IV-line insertion and wound evaluation. The present technology also has the advantage of giving valuable instruments of patient awareness, thus making patients easily understand even those medical conditions and treatments that may be perceived to be complicated and, therefore, enhance patient compliance with the recommended treatments. The demonstrated possibility to use AR to control the levels of pain and anxiety, along with other advantages, only proves the multifunctional nature of this technology. However, there are some setbacks in AR, such as technical ones, costs, and sufficient training for AR to be utilized optimally. The barriers that need to be overcome are also presented and require improvement for the implementation on a large scale. Further studies and innovations with health-care authorities and technology practitioners are essential for the advancement of AR in nursing care and its capability of enhancing patient well-being and nurses’ productivity.

Effective Data Migration Strategies Using Talend and DataStage

Modern company operations need reliable data migration across systems to preserve continuity, performance, and business process integrity. Data transfer techniques need careful preparation, powerful tools, and system knowledge. Talend and IBM DataStage, two renowned data integration and migration systems, are used in this study to implement successful data transfer techniques. Talend and DataStage provide ETL, data quality management, and real-time integration services for data migration. Talend, an open-source platform, supports several data sources and formats with flexibility, scalability, and many connections. Organizations seeking cost-effective solutions appreciate it for its real-time data processing, user-friendly interface, and comprehensive data transformation capabilities. Talend's powerful data profiling, cleaning, and governance tools assure high-quality, compliant data migration. IBM DataStage, part of IBM InfoSphere, is a sophisticated ETL tool for large-scale data integration. DataStage is ideal for complicated data settings with big data volumes because to its great performance and scalability. DataStage can handle the most difficult data migration operations due to its parallel processing and interaction with IBM's data management environment. Enterprises who need a dependable data migration solution can benefit from the platform's metadata management, data lineage, and real-time data integration, which interface with many databases and applications.

Dynamic line rating for grid transfer capability optimization in Malaysia

<div align="center"><span>This paper details a case study on the implementation of dynamic line rating (DLR) to enhance the ampacity rating of Malaysia’s grid. Utilizing heat balance equations endorsed by the Institute of Electrical and Electronics Engineering (IEEE 738) and the International Council on Large Electric Systems (CIGRE technical brochure 601), the ampacity rating of a Zebra-type aluminum cable steel reinforced (ACSR) conductor on a 275 kV transmission line has been assessed. Real-time weather conditions and conductor temperatures, measured hourly by the DLR sensor over the course of a year, were incorporated into the ampacity calculation to determine the available margin. The weather parameters were analyzed based on the monsoon seasons. A comparative analysis between various methods outlined in the standards and the estimated ampacity rating derived from both standards is presented. According to both standards, the findings indicate that DLR surpasses static line rating (SLR), highlighting the presence of untapped ampacity for grid optimization. Remarkably, CIGRE TB 601 exhibits a higher ampacity rating margin than the IEEE 738 standard, with a percentage difference of 16.20%. The study concludes that the conductor is underutilized and proposes optimization through the integration of real-time weather conditions data into the heat balance equations.</span></div>

Real-Time Semantic Data Integration and Reasoning in Life- and Time-Critical Decision Support Systems

Natural disasters such as earthquakes, floods, and forest fires involve critical situations in which human lives and infrastructures are in jeopardy. People are often injured and/or trapped without being able to be assisted by first responders on time. Moreover, in most cases, the harsh environment jeopardizes first responders by significantly increasing the difficulty of their mission. In such scenarios, time is crucial and often of vital importance. First responders must have a clear and complete view of the current situation every few seconds/minutes to efficiently and timely tackle emerging challenges, ensuring the safety of both victims and personnel. Advances in related technology including robots, drones, and Internet of Things (IoT)-enabled equipment have increased their usability and importance in life- and time-critical decision support systems such as the ones designed and developed for Search and Rescue (SAR) missions. Such systems depend on efficiency in their ability to integrate large volumes of heterogeneous and streaming data and reason with this data in (near) real time. In addition, real-time critical data integration and reasoning need to be performed on edge devices that reside near the missions, instead of using cloud infrastructure. The aim of this paper is twofold: (a) to review technologies and approaches related to real-time semantic data integration and reasoning on IoT-enabled collaborative entities and edge devices in life- and time-critical decision support systems, with a focus on systems designed for SAR missions and (b) to identify open issues and challenges focusing on the specific topic. In addition, this paper proposes a novel approach that will go beyond the state-of-the-art in efficiently recognizing time-critical high-level events, supporting commanders and first responders with meaningful and life-critical insights about the current and predicted state of the environment in which they operate.

The Use of Material and Supply Chain Management and Service Organization to Reduce Inventory Levels While Providing Adequate Service to Customers

In the dynamic landscape of service organizations, effective Material and Supply Chain Management (MSCM) plays a pivotal role in achieving operational efficiency, cost reduction, and superior customer service. This research paper delves into the strategic application of MSCM practices to streamline inventory management processes within service-oriented industries, emphasizing the delicate balance between minimizing stock levels and meeting customer service expectations. The research aims to explore the various methodologies and technologies employed in service organizations to enhance material and supply chain operations. Emphasis will be placed on understanding how advanced technologies, data analytics, and automation contribute to minimizing excess inventory while concurrently ensuring prompt and satisfactory customer service. Key areas of investigation include the adoption of just-in-time inventory systems, demand forecasting models, and supplier relationship management strategies. The paper will critically assess case studies and industry best practices to draw insights into successful MSCM implementations and their impact on inventory levels, service quality, and overall organizational performance. Furthermore, the study will address challenges associated with implementing MSCM in service organizations, such as the inherent variability in service demand, the importance of real-time data integration, and the role of collaboration between various stakeholders within the supply chain. The anticipated outcomes of this research include providing service organizations with a comprehensive understanding of the benefits, challenges, and strategic considerations associated with adopting robust MSCM practices. The findings aim to contribute valuable insights to both academia and industry professionals seeking to optimize their material and supply chain processes for improved inventory management and enhanced customer service delivery. International Journal of Scientific Research in Engineering and Management (IJSREM) Volume: 08 Issue: 01 | January - 2024 SJIF Rating: 8.176 ISSN: 2582-3930 © 2024, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM28288 | Page 2 Keywords:Material and Supply Chain Management (MSCM),Service organizations,Inventory optimization,Customer service,Just-in-time inventory,Demand forecasting,Supplier relationship management

Methods for Addressing Pavement Defects Based on Digital Twin Technology--A Case Study of Snow and Water Accumulation on Road Surface

This paper explores the utilization of digital twin technology for the management of urban road infrastructure challenges, specifically addressing issues related to snow and water accumulation. The study emphasizes the constraints associated with traditional road maintenance methods and proposes the implementation of a digital twin city framework to augment real-time monitoring and enhance emergency response efficiency. Through the integration of real-time data from sensors, traffic, and weather sources, the digital twin model replicates road conditions and forecasts potential issues, thereby enabling early warning systems. Furthermore, the research delves into the application of conductive asphalt concrete and superhydrophobic materials to mitigate road-related challenges. The results indicate that digital twin technology significantly enhances economic and operational efficiency in urban road infrastructure management when compared to conventional approaches.

Dynamic Forecasting of Traffic Event Duration in Istanbul: A Classification Approach with Real-Time Data Integration

Dynamic Forecasting of Traffic Event Duration in Istanbul: A Classification Approach with Real-Time Data Integration

Digital twin of earth: A novel information framework for managing a sustainable earth

<p>As the progress of human society rapidly enters a new stage, sustainable development has become an essential target. It is necessary to have an effective approach to managing the resources and ecosystems of Earth, with the target of promoting sustainable development while meeting the needs and requirements of both present and future generations. Digital twin technology provides a fresh perspective on sustainable management with a better understanding of the interconnectedness of Earth's systems and accurate simulation and prediction of future changes. By integrating various types of data and models, including climate models, geophysical models, and social media data, a digital twin of Earth can simulate and analyze various aspects of the planet. The digital twin of Earth leverages real-time multi-source data integration through advanced IoT and wireless sensor networks, feeding into a data lake architecture that supports the seamless integration and processing of heterogeneous data. The integrated data then informs dynamic models, combining numerical simulation models with intelligent algorithms for enhanced prediction and analysis. This approach emphasizes the integration of environmental, social, and economic considerations into decision-making that balances economic growth, social well-being, and environmental protection to achieve sustainable development. This paper introduces the definition and information framework of the new concept of a digital twin of Earth to promote the current research on Earth management based on digital transformation techniques.</p>

Facilitating efficient energy distribution and storage: The role of data consistency technologies in Azure Cosmos DB

This article delves into the critical aspect of data consistency in globally distributed databases, with a specific focus on Azure Cosmos DB, Microsoft’s flagship globally distributed database service. It begins by outlining the inherent challenges of maintaining data consistency across a distributed architecture, such as network latency and the need for effective conflict resolution mechanisms. The introduction sets the stage for a deeper exploration of these challenges and their implications for applications requiring global scalability. The subsequent sections provide a detailed examination of the architecture and features of Azure Cosmos DB, highlighting its global data distribution capabilities, support for multiple data models, and flexible consistency models. The discussion emphasizes the importance of selecting the appropriate consistency level based on application requirements, balancing the trade-offs between consistency, performance, and availability. Further, the article addresses the technical underpinnings and solutions employed by Azure Cosmos DB to achieve data consistency, including advanced algorithms like vector clocks for session consistency and log replication mechanisms for strong and bounded consistency models. These technologies play a pivotal role in ensuring data integrity and timely access across the distributed database. The conclusion synthesizes the insights gained from the exploration of Azure Cosmos DB’s approach to data consistency, underscoring the platform’s adeptness at providing a robust, flexible, and efficient solution for managing data in a globally distributed context. The article emphasizes the critical role of platforms like Azure Cosmos DB in meeting the modern digital enterprise’s demands for real-time data access and integrity across a global infrastructure.

Identifying Sensors Data Integrity Threats of Smart Agriculture: A Collaborative Filtering Approach

HighlightsData integrity threat is one of the challenges faced by smart agriculture, where humans make complex decisions based on the results and recommendations of automated farm/livestock monitoring systems.Smart or digital farming heavily relies on data, which can face various integrity threats including data modification, data destruction, etc.The use of collaborative filtering as a lightweight method to detect abnormal data has proven effective from our experimental study. It can detect suspicious data with high accuracy and precision enhancing digital agricultural security.Abstract. Smart agriculture leverages human intelligence and artificial intelligence in digital farming, which heavily relies on data collection, analysis, and decision support delivery. Farmers need reliable data and a decision support system to make accurate decisions and take appropriate actions. The Internet of Things (IoT) not only creates the backbone for farm data collection for intelligent farming but also brings some security concerns to the agriculture field. Though many studies exist on security in agriculture, particularly device identification and tracking, cryptography, blockchain, and other security approaches, the concerns about agricultural data integrity have not been fully explored in the current literature. Data integrity threats could cause huge losses to farmers, threatening food security, especially in developing countries. Given the early stage of development in the domain and the rapid adoption of IoT, ensuring the trustworthiness of data from various devices poses a significant challenge. This challenge is exacerbated in wireless sensor network scenarios, particularly in harsh environments where potential avenues for physical attacks exist. This article evaluates a data integrity threat detection technique in an IoT wireless network. It proposes an approach to identify potential data integrity failures or threats. The effectiveness of this approach is demonstrated through an empirical use-case study focusing on agriculture applications. Through experimental and trace-based simulations, we illustrate that threats can potentially be identified with a 91% accuracy rate and approximately 98% precision and recall. The proposed solution could be deployed in distributed and centralized digital agricultural systems to identify and predict real-time data integrity issues or threats. Keywords: Collaborative filtering, Data integrity threats, Local outlier factor, Smart agriculture.

Digital Twinning Stacks, a modular communication system approach for IOT implementation in Unity3D

Manufacturing scenarios have become more data-intense and displaying the vast amount of data for human oversight has become a major task in various applications. The growing need for real-time data visualization in industrial applications has led to the development of numerous communication frameworks, each often designed for specific methods and purposes. This specialization requires developers to master multiple frameworks and integrate them into cohesive applications, a process that can be both complex and time-consuming. Human oversight interface for users in manufacturing can be categorized into displaying the (aggregated) past, current or future state of robots or different manufacturing equipment. This publication look into realizing these industrial XR applications with Unity3D and introduces a modular, open-source framework designed to streamline the integration of real-time data communication pipelines. This framework supports inputs from a diverse range of sources, offering a unified solution that simplifies the development process for applications requiring real-time data integration and is available as an open-source solution for researchers and practitioners.

Real-Time data integration in diagnostic devices for predictive modeling of infectious disease outbreaks

The integration of real-time data in diagnostic devices is transforming the landscape of infectious disease management by enabling predictive modeling of outbreaks. Traditional diagnostic approaches, often constrained by delayed data processing and analysis, fall short in rapidly evolving scenarios like infectious disease outbreaks. Recent advancements in real-time data integration are addressing these limitations, offering significant improvements in predictive accuracy and timely response. Real-time data integration leverages continuous streams of information from various sources, including diagnostic devices, electronic health records, and environmental sensors, to provide a comprehensive view of disease dynamics. By incorporating this data into predictive models, healthcare professionals can better anticipate and manage outbreaks. For instance, integrating data from wearable sensors and mobile health applications allows for continuous monitoring of health indicators, which can signal early warning signs of disease spread. This approach enhances the ability to detect patterns and anomalies that might indicate an impending outbreak. Moreover, predictive modeling powered by real-time data facilitates proactive interventions. By analyzing trends and correlations in real-time, these models can forecast potential outbreak scenarios, optimize resource allocation, and guide public health responses. For example, predictive models can forecast the spread of diseases based on current infection rates and social behavior patterns, enabling targeted interventions and timely public health advisories. Despite the benefits, the integration of real-time data into diagnostic devices presents several challenges. Ensuring data accuracy and consistency, maintaining privacy and security, and managing the vast amounts of data generated are critical issues that need addressing. Advanced data analytics and machine learning techniques are essential for processing and interpreting this data effectively. Additionally, interoperability among various data sources and systems is crucial for a seamless integration process. In summary, the integration of real-time data in diagnostic devices offers a powerful tool for predictive modeling of infectious disease outbreaks. It enhances the accuracy of forecasts, improves response strategies, and ultimately contributes to more effective management of public health crises. As technology advances, addressing the associated challenges will be key to maximizing the potential of this innovative approach. Keywords: Real-Time, Data Integration, Diagnostic Devices, Predictive Modeling, Infectious Disease Outbreaks.

Dynamically updated digital twin for prognostics and health management: Application in permanent magnet synchronous motor

Current research on Digital Twin (DT) based Prognostics and Health Management (PHM) focuses on establishment of DT through integration of real-time data from various sources to facilitate comprehensive product monitoring and health management. However, there still exist gaps in the seamless integration of DT and PHM, as well as in the development of DT multi-field coupling modeling and its dynamic update mechanism. When the product experiences long-period degradation under load spectrum, it is challenging to describe the dynamic evolution of the health status and degradation progression accurately. In addition, DT update algorithms are difficult to be integrated simultaneously by current methods. This paper proposes an innovative dual loop DT based PHM framework, in which the first loop establishes the basic dynamic DT with multi-filed coupling, and the second loop implements the PHM and the abnormal detection to provide the interaction between the dual loops through updating mechanism. The proposed method pays attention to the internal state changes with degradation and interactive mapping with dynamic parameter updating. Furthermore, the Independence Principle for the abnormal detection is proposed to refine the theory of DT. Events at the first loop focus on accurate modeling of multi-field coupling, while the events at the second loop focus on real-time occurrence of anomalies and the product degradation trend. The interaction and collaboration between different loop models are also discussed. Finally, the Permanent Magnet Synchronous Motor (PMSM) is used to verify the proposed method. The results show that the modeling method proposed can accurately track the lifecycle performance changes of the entity and carry out remaining life prediction and health management effectively.

Feasibility and effects of GIS-based municipal solid waste vehicle routing for Bahrain

<p>In the realm of waste management, efficient route optimisation for municipal solid waste (MSW) collection is becoming increasingly crucial, particularly for developing nations with budgetary considerations. This study leverages the capabilities of the geographic information system (GIS) and integrates the Dijkstra algorithm to enhance route optimisation for MSW vehicles in Bahrain. Utilising comprehensive local vehicle routing data from Urbaser and applying GIS methodologies, three distinct areas in Bahrain were methodically analysed. The results revealed a notable 55% reduction in travel distance, a 17% decrease in time, and a yearly fuel cost saving of 6405 BHD (16,974 USD) in the optimal scenario. Given these findings, the potential applicability of this optimisation algorithm extends beyond Bahrain, suggesting significant benefits for regions with similar challenges. To further refine this approach, the integration of real-time traffic data into the routing algorithm is recommended. Other additions to the optimization process could include additional parameters such as safety.</p>

Design of Public Transportation System Scheduling and Optimization in the Internet of Things (IoT) Environment

This study aims to explore the scheduling and optimization of public transportation systems in the Internet of Things (IoT) environment. By establishing mathematical models, this research analyzes the current state of public transportation systems, proposes scheduling and optimization strategies based on mathematical models, and validates the effectiveness of these strategies through case studies. The research findings indicate that mathematical models have significant potential in improving public transportation efficiency, reducing costs, promoting environmental sustainability, and enhancing passenger experiences. Future research directions include real-time data integration, the application of machine learning, multi-modal transportation integration, sustainable practices, and the development of user-centric solutions.

A Scheduling Algorithm for Appliance Energy Consumption Optimization in a Dynamic Pricing Environment

This research delves into the intricate landscape of energy scheduling and optimization within microgrid and residential contexts, addressing pivotal aspects such as real-time scheduling systems, challenges in dynamic pricing, and an array of optimization strategies. This paper introduces a cutting-edge scheduling algorithm, harnessing the power of artificial neural networks driven by Long Short-Term Memory Networks, and highlights its exceptional performance, boasting a significantly lower Mean Absolute Error of 5.32 compared to conventional models. This heightened predictive accuracy translates into tangible improvements in both energy efficiency and cost savings. This study underscores the delicate balance between user satisfaction, cost reduction, and efficient scheduling for sustainable energy consumption, showcasing a remarkable 38% enhancement in optimized schedules. Further granularity revealed substantial gains in energy efficiency and cost reduction across different scheduling intensities: 11.11% in light schedules, 20.09% in medium schedules, and an impressive 38.85% in heavy schedules. However, this research does not shy away from highlighting challenges related to data quality, computational demands, and generalizability. Future research trajectories encompass the development of adaptive models tailored to diverse data qualities, enhancements in scalability for and adaptability to various microgrid configurations, the integration of real-time data, the accommodation of user preferences, the exploration of energy storage and renewables, and an imperative focus on enhancing algorithm transparency.

Enhancing routes selection with real-time weather data integration in spatial decision support systems

Enhancing routes selection with real-time weather data integration in spatial decision support systems

A Computational Geospatial Approach to Assessing Land-Use Compatibility in Urban Planning

Amidst rapid urbanization, sustainable development requires moving beyond subjective land-use planning techniques toward innovative computational geospatial models. This paper introduces a GIS-based quantitative framework to enable objective, rigorous land-use compatibility analysis. Uniquely, the model evaluates radial impacts and expert-defined criteria across multiple scales, overcoming the limitations of qualitative approaches. Cell-by-cell computation identifies emerging spatial conflicts with enhanced realism. A case study in Qaemshahr, Iran, demonstrated the model’s proficiency in revealing incompatibilities and hotspots, surpassing conventional methodologies. Quantitative analysis provided accurate, transparent insights for evidence-based planning and consistency in evaluation. Ongoing improvements through 3D, real-time data integration and machine learning will further the objectivity. While extensive testing across diverse urban contexts is still needed, this pioneering computational technique marks a transition from subjective to objective methodologies. Situated at the intersection of geographic information science and urban planning, this study serves as a launchpad for advancing robust geospatial models to shape more equitable, resilient urban futures amidst complex sustainability challenges. The development of rigorous computational techniques remains fundamental, and the present innovative model can be used to provide objective, scientifically grounded compatibility analyses to guide land-use planning.

A Conceptual Framework for Developing Intelligent Services (a Platform) for Transport Enterprises: The Designation of Key Drivers for Action

In the digital era, effective business management relies on dynamic risk analysis and real-time data integration, particularly amid the evolving landscape shaped by technological advancements and external factors such as climate change and global health crises. This study delves into the specific demands for digital services within the transportation sector, focusing on the crucial task of identifying an optimal data-driven management system (platform) to bolster transportation decision-making processes. The paper revolves around the formulation of a comprehensive conceptual framework for the development of intelligent services and platforms tailored explicitly to transport enterprises. Methodologically, a thorough analysis of critical infrastructure-related challenges was conducted, emphasizing the integration of a service-oriented approach to enhance overall functionality. Central to the paper’s approach is the careful navigation of conflicting user requirements, resource constraints, and the imperative of maintaining adaptability in service implementation. Additionally, a robust data flow analysis framework is presented, encompassing data collection, model building, and model extrapolation, which enables the generation of reliable outputs essential for informed decision-making. Notably, the study underscores the pivotal role played by the EN.I.R.I.S.S.T. research infrastructure in delivering essential services to the transportation domain, offering accessible data, user-friendly interfaces, and data analysis tools. The findings highlight the enthusiastic reception of the diverse services among potential users, indicating a strong willingness to engage and benefit from the proposed solutions. By emphasizing the integration of intelligent services, the paper seeks to present a systematic approach aimed at enhancing the efficiency, productivity, and competitive edge of transport enterprises through the strategic deployment of advanced technological solutions and proactive planning. This paper ultimately contributes cutting-edge research insights, empowering transportation managers, planners, and decision-makers with valuable resources for informed business intelligence and corporate strategy.