Real-time Data Management Research Articles

Addressing researcher fraud: retrospective, real-time, and preventive strategies-including legal points and data management that prevents fraud.

Researcher fraud is often easy and enticing in academic research, with little risk of detection. Cases of extensive fraud continue to occur. The amount of fraud that goes undetected is unknown and may be substantial. Three strategies for addressing researcher fraud are (a) retrospective investigations after allegations of fraud have been made, (b) sting operations that provide conclusive evidence of fraud as it occurs, and (c) data management practices that prevent the occurrence of fraud. Institutional and regulatory efforts to address researcher fraud have focused almost exclusively on the retrospective strategy. The retrospective approach is subject to controversy due to the limitations of post-hoc evidence in science, the difficulty in establishing who actually committed the fraud in some cases, the application of a legal standard of evidence that is much lower than the usual standards of evidence in science, and the lack of legal expertise by scientists investigating fraud. The retrospective strategy may be reliably effective primarily in cases of extensive, careless fraud. Sting operations can overcome these limitations and controversies, but are not feasible in many situations. Data management practices that are effective at preventing researcher fraud and unintentional errors are well-established in clinical trials regulated by government agencies, but appear to be largely unknown or unimplemented in most academic research. Established data management practices include: archiving secure copies of the raw data, audit trails, restricted access to the data and data collection processes, software validation, quality control checks, blinding, preregistration of data processing and analysis programs, and research audits that directly address fraud. Current discussions about data management in academic research focus on sharing data with little attention to practices that prevent intentional and unintentional errors. A designation or badge such as error-controlled data management could be established to indicate research that was conducted with data management practices that effectively address intentional and unintentional errors.

A Review of Modern Computational Techniques and Their Role in Power System Stability and Control

This paper attempts to elucidate the transformative integration of computational techniques within power systems, underscoring their critical role in enhancing system modeling, control, and the efficient integration of renewable energy. It breaks down the two-sided nature of technological progress, highlighting both gains in operational efficiency and new challenges such as real-time processing, data management, and cybersecurity. Through meticulous analysis of query-based research patterns and mathematical frameworks, this study delves into the balancing act between specificity and breadth in scholarly inquiries while evaluating the impact and evolution of research trends through citation analysis. The convergence of interests and transient research trends is evident, particularly in Artificial Intelligence and optimization. This comprehensive narrative anticipates a sophisticated trajectory for power systems, advocating for continuous innovation and strategic research to foster sustainable, resilient, and intelligent energy networks.

Integrated Framework for Virtual Tours and 3D Visualization of Cultural Tourism in Pattani, Thailand Based on WebGIS Platform

This paper presents a framework for developing a lightweight WebGIS platform as a web map service for publishing and servicing cultural tourism information. The archaeological sites, history, and cultural arts in Pattani Province were used as case studies in this article. It incorporates various digital technologies to create digital archives in providing web-based information services, including geospatial databases, virtual tours and virtual reality, 3D models, and online cultural maps for tourism. The framework describes the necessary infrastructure based on open-source code that is specially developed and provides libraries with free software interoperability, which can be considered as a real-time data management facility and simple completion. It is a relatively low-cost but a reasonably efficient system that is very useful and suitable for local authorities and small organizations with budget constraints to be able to implement practical cultural heritage management for a digital tourism experience that can be displayed on computer equipment, smartphones, and VR headgear efficiently. Furthermore, it also offers a conceptual framework in disclosing future research directions.

Assessment of port resilience using Bayesian network: A study of strategies to enhance readiness and response capacities

Ports, as critical infrastructure, are a crucial part of the maritime transportation system (MTS) and are increasingly vulnerable to disturbances and disasters in the post-pandemic era. Resilience plays a crucial role in building sustainable infrastructure, and developing a resilient port system is a priority in planning and developing MTS. This paper aims to develop measures to enhance port resilience that can cope with risks and uncertainties. A circular four-stage method is proposed to study port resilience. The major disturbances that are currently affecting ports are summarized and classified. Then, a port resilience assessment model using the Bayesian network is proposed, in which various resilience strategies are categorized into different metrics to assess resilience capabilities (i.e. readiness and response capacities). The model is constructed based on expert judgment and statistical analysis. The Shanghai Yangshan Deepwater Port in China is used as a case study. The results show that natural disasters are major disruptors plaguing ports. The overall resilience of automated terminals is higher than that of non-automated terminals. Strategies to enhance visibility, such as building real-time data management systems and data analysis programs, have the most significant impact on improving ports’ readiness. Strategies to enhance recovery such as facility restoration and technology restoration are the most important when improving ports’ response capability.

Design of Cloud-Based Real-Time Eye-Tracking Monitoring and Storage System

The rapid development of technology has led to the implementation of data-driven systems whose performance heavily relies on the amount and type of data. In the latest decades, in the field of bioengineering data management, among others, eye-tracking data have become one of the most interesting and essential components for many medical, psychological, and engineering research applications. However, despite the large usage of eye-tracking data in many studies and applications, a strong gap is still present in the literature regarding real-time data collection and management, which leads to strong constraints for the reliability and accuracy of on-time results. To address this gap, this study aims to introduce a system that enables the collection, processing, real-time streaming, and storage of eye-tracking data. The system was developed using the Java programming language, WebSocket protocol, and Representational State Transfer (REST), improving the efficiency in transferring and managing eye-tracking data. The results were computed in two test conditions, i.e., local and online scenarios, within a time window of 100 seconds. The experiments conducted for this study were carried out by comparing the time delay between two different scenarios, even if preliminary results showed a significantly improved performance of data management systems in managing real-time data transfer. Overall, this system can significantly benefit the research community by providing real-time data transfer and storing the data, enabling more extensive studies using eye-tracking data.

SMEARcore – modular data infrastructure for atmospheric measurement stations

Abstract. We present the SMEARcore data infrastructure framework: a collection of modular programs and processing workflows intended for measurement stations and campaigns as a real-time data analysis and management platform. SMEARcore enables new SMEAR (Station for Measuring Ecosystem–Atmosphere Relations) stations to be integrated in a way that is consistent with existing stations and transfers the existing data curation experience to the new station. It establishes robust data pipelines that allow easier diagnosis of problems. We show practical examples of how SMEARcore is utilized at operational measurement stations. This work differs from earlier similar concepts, such as those used at stations within ACTRIS (Aerosols, Clouds and Trace Gases Research Infrastructure) and ICOS (Integrated Carbon Observation System) networks, in three important aspects: firstly, by keeping all the processing under the control of the data owners; secondly, by providing tools for making data interoperable in general instead of harmonizing a particular set of instruments; and thirdly, by being extensible to new instruments. As such it is not meant as a replacement for these infrastructures but to be used in addition to them and to bring structured data curation to more measurement stations not yet using these practices.

Data quality issues in production planning and control – Linkages to smart PPC

In the era of Industry 4.0 and digitalization, planning solutions need to co-exist with each other and be able to manage higher complexity and with a higher performance. As the concept smart production planning and control is a part of industry 4.0, it is highly relevant to study and is in this paper explored on the four elements of smart PPC (real-time data management, dynamic production planning and re-planning, autonomous production control, and continuous learning). This paper provides a framework for linking the four elements of smart PPC with data quality issues in state-of-the-art production planning and control environments. Maintaining a high standard of data quality in the business processes aids the organization to stay competitive in its market. Hence, our assumption is that a high level of data quality is needed in production planning and control for a high-performance outcome. The empirical part of our study results in a bar-chart of seven data quality problems and their occurrences together with their causes in PPC. According to the empirical data results, inaccurate data entries is the most common data quality problem related to PPC. The causes of the inaccurate data entries can be linked to human resources and organizational control. Future research should strengthen the validity of the proposed linkages between data quality problems and elements of smart PPC and implications on strategic, tactical, and operational planning levels.

An IoT-based residential energy metering and protection system

The fairness of electricity services lies in the provider being able to deliver the expected quality power, and recover the returns on investment. Energy theft, metering lapses, billing errors, and cumbersome payment procedures constitute the bulk of the non-technical power losses and contribute majorly to the incapacity of electricity vendors to run a profitable business and serve customers effectively. A real-time approach has been identified as the innovative need at resolving the enumerated issues within the electricity value chain. This study is leveraging the Internet of Things (IoT) technology to propose an extended modelled system capable of providing real-time data management, residential power system control, and an interactive platform for vendors and consumers. The energy billing was modelled and developed from resourceful components. A website was developed with a user-friendly interface. The unique features of the system design are the possibility of customers loading their electricity credit online, and the supplier being able to lock or disconnect any defaulting customer remotely.

Cloud computing security in health cyber physical systems

The health industry as one of the Critical Infrastructures has rapidly grown in adopting the use of new ubiquitous technologies in advancing its critical services. The industry because of its high need and usage of new technologies is encountering a myriad of challenges including cyber-attacks. Among the technologies adopted by the health industry is the use of cloud computing services for managing Electronic Medical Records (EMR) on the cloud environment. Today’s medical devices use sensors that collect data from the diverse environments such as the human heart and such data is sent to the cloud for better analysis, monitoring, or management. However, attackers exploit every single development to advance health or medical services like all the other cyber systems. The scope of this paper is to investigate how cloud computing services adopted in Health Cyber Physical Systems can be secured from the prevalent cybersecurity challenges. The study followed a systematic literature review, where data was collected from online databases with set search constraints such as language, date of publication and specific keywords. This paper presents how cloud computing integrated with the medical cyber physical systems can impeccably securely manage the health systems. More specifically the collection, monitoring, and management of real-time data and as a result of this integration, different cyber-attacks are escalating.

A Review on Digital Twin Technology in Smart Grid, Transportation System and Smart City: Challenges and Future

With recent advances in information and communication technology (ICT), the bleeding edge concept of digital twin (DT) has enticed the attention of many researchers to revolutionize the entire modern industries. DT concept refers to a digital representation of a physical entity that is able to reflect its physical behavior by applying platforms and bidirectional interaction of data in real-time. The remarkable deployment of the internet of things in the power grid has led to reliable access to information that improves its performance and equips it with a powerful tool for real-time data management and analysis. This paper aims to trace the continuous investigation and propose practical ideas in originating and developing DT technology, according to various application domains of power systems, and also describes the proposed solutions to deal with the challenges associated with DT. Indeed, with the development of modern cities, different energy layers such as transportation systems, smart grids, and microgrids have emerged facing various issues that challenge the multi-dimensional energy management system. For example, in transportation systems, traffic is a major problem that requires real-time management, planning, and analysis. In power grids, remote data transfer within the grid and also various analyzes needing real data are just some of the current challenges in the field. These problems can be cracked by providing and analyzing a real twin framework in each section. All in all, this paper aims to survey different applications of DT in the development of the various aspects of energy management within a city including transportation systems, power grids, and microgrids. Besides, the security of DT technology based on ML is discussed. It also provides a complete view for the readers to be able to develop and deploy a DT technology for various power system applications.

Pelatihan Pendidikan Kognitif Anak Dengan Aplikasi Open Smart Rigester Platform

This community service reported the process on training of cognitive development using a mobile application for real-time data management information system in early childhood education, it can help tracking child education and assist teacher in monitoring and evaluation on child services. A community service was carried out to gather necessary information through a training directly entry in the field using mobile development, application testing and analysis that was conducted on for 50 children. To obtain a full picture on early childhood education, data on childhood education shall be mapped and linked in one application. We introduce a mobile app to systematically compile the individual as well as group data across different aspects of child life, ranging from early child education. Using a tablet PC or mobile phone, data could be easily entered at anytime by the person. Due to still poor infrastructure at the grass root level, the system also allows a safety store offline that could automatically link to server when network connection is available. The immediate data entry will provide real-time data report that could be accessed by any relevant stakeholders at any levels to response accordingly. However, to avoid misuse of data, the access will also be restricted with a secured login system. Based on the community service, this application is easily applicable for the training of real-time monitoring and evaluation on early childhood education.

PLUME Dashboard: A free and open-source mobile air quality monitoring dashboard

The deployment of a mobile air quality monitoring laboratory requires advanced real-time instrument monitoring and data management software, which can be prohibitively expensive. In this work we present the PLUME Dashboard: a software package built in Python designed specifically for mobile air quality monitoring purposes. It aims to provide a free and open-source alternative to comparable commercial packages, thus reducing the barrier to entry of conducting such research. This paper outlines the development of the PLUME Dashboard and justifies the design choices that were made while also providing thorough documentation and explanation for how the software works. Functionality includes real-time data display, real-time peak identification, baseline subtraction, real-time air quality and self-sampling alerts (based on wind direction and vehicle speed), and post-processing tools such as peak identification and map merging with GPS data. The functionality of PLUME Dashboard is tested using real-world data collected in Toronto and Vancouver Canada.

Modeling of spatiotemporal data about the environment in GIS

A large number of sensors are used to monitor the environment, and a large volume of spatio-temporal data on epizootic risks and the environment is processed in real time. To date, GIS data models are represented by static models and more modern time models. However, many of the epizootological and environmental data management systems do not meet the requirements of real-time data management. The purpose of the work is to propose, based on the analysis of foreign literature sources, a modern method for managing epizootological and environmental data based on a new GIS model in real time in comparison with the Sensor web service model.Two experiments were conducted in the urban environment and on the territories of livestock farms with different epizootic situations for potential risk management in zoonoses. Real-time monitoring of air quality and real-time monitoring of soil moisture was carried out in Wuhan (China). The circulation of pathogens of zoonoses and sapronoses in the environment, including in the soil, and their preservation in the form of spores and hard-to-cultivate forms, determines the ecological component of emergent epizootics and epidemics with the coverage of new areas. Experimental results have shown that the use of the proposed GIS data model on the Sensor web service platform for managing epizootological/epidemiological and environmental data in real time is reliable and effective.

A Reference Architecture for IoT-Enabled Smart Buildings

The management and analytics of big data generated from IoT sensors deployed in smart buildings pose a real challenge in today’s world. Hence, there is a clear need for an IoT focused Integrated Big Data Management and Analytics framework to enable the near real-time autonomous control and management of smart buildings. The focus of this paper is on the development and evaluation of the reference architecture required to support such a framework. The applicability of the reference architecture is evaluated by taking into account various example scenarios for a smart building involving the management and analysis of near real-time IoT data from 1000 sensors. The results demonstrate that the reference architecture can guide the complex integration and orchestration of real-time IoT data management, analytics, and autonomous control of smart buildings, and that the architecture can be scaled up to address challenges for other smart environments.

Using Python Modules in Real-Time Plasma Systems for Fusion.

One of the most important applications of sensors is feedback control, in which an algorithm is applied to data that are collected from sensors in order to drive system actuators and achieve the desired outputs of the target plant. One of the most challenging applications of this control is represented by magnetic confinement fusion, in which real-time systems are responsible for the confinement of plasma at a temperature of several million degrees within a toroidal container by means of strong electromagnetic fields. Due to the fast dynamics of the underlying physical phenomena, data that are collected from electromagnetic sensors must be processed in real time. In most applications, real-time systems are implemented in C++; however, Python applications are now becoming more and more widespread, which has raised potential interest in their applicability in real-time systems. In this study, a framework was set up to assess the applicability of Python in real-time systems. For this purpose, a reference operating system configuration was chosen, which was optimized for real time, together with a reference framework for real-time data management. Within this framework, the performance of modules that computed PID control and FFT transforms was compared for C++ and Python implementations, respectively. Despite the initial concerns about Python applicability in real-time systems, it was found that the worst-case execution time (WCET) could also be safely defined for modules that were implemented in Python, thereby confirming that they could be considered for real-time applications.

Nurses' knowledge of and attitude to nursing information systems.

The use of a nursing information system (NIS) provides significant benefit to the nurses in the management of real-time patient data and contributes to improving the operational effectiveness of nursing services. To assess the knowledge of, attitude towards and use of an NIS. A prospective cross-sectional study was carried out among 230 hospital nurses in India, using a validated questionnaire including demographic characteristics, and questions on knowledge of, attitude towards and use of an NIS. The study found that more than 70% of nurses had good computer skills and knowledge about the information system. About 75% of the nurses reported that the NIS could be a tool to support them in improving patient care, and medical documentation, although 42% claimed that the use of NIS depersonalises nursing care. Seventy-one per cent of the nurses expressed the need for formal education and training on using an NIS during and after their nursing education. Training and demonstration of NISs during nurse education would improve nurses' knowledge, build confidence and positive attitudes, and enhance the acceptability and sustainability of NISs in healthcare settings.

An Open Relation Extraction System for Web Text Information

Web texts typically undergo the open-ended growth of new relations. Traditional relation extraction methods lack automatic annotation and perform poorly on new relation extraction tasks. We propose an open-domain relation extraction system (ORES) based on distant supervision and few-shot learning to solve this problem. More specifically, we utilize tBERT to design instance selector 1, implementing automatic labeling in the data mining component. Meanwhile, we design example selector 2 based on K-BERT in the new relation extraction component. The real-time data management component outputs new relational data. Experiments show that ORES can filter out higher quality and diverse instances for better new relation learning. It achieves significant improvement compared to Neural Snowball with fewer seed sentences.

Towards smart production planning and control; a conceptual framework linking planning environment characteristics with the need for smart production planning and control

Rapid advances in Industry 4.0 have the potential to transform production planning and control (PPC) through the emerging concept of smart PPC. This paper provides a visionary perspective by addressing the gap in research on how the characteristics of a company's planning environment impact on the need for, and potential benefit of, smart PPC. The paper posits that the potential of smart PPC to improve PPC performance increases with the complexity of the planning environment. A set of propositions is developed for how 12 product, market, and process variables impact on the need for smart PPC. These are operationalized into a conceptual framework that can be used as a tool by practitioners and academics to assess a company's need for smart PPC. A case study from the food sector illustrates the applicability of the framework and describes three potential applications for how four elements of smart PPC (real-time data management, dynamic production planning and re-planning, autonomous production control, and continuous learning) can be used to address key PPC challenges and open new opportunities for improving PPC. Future research should strengthen the validity and applicability of the proposed framework through additional cases across industrial sectors and carry out case studies, surveys, and structural equation modeling to investigate the specific relationship between planning environment characteristics, smart technologies, and the elements of smart PPC.

A Comprehensive Review on Sustainable Aspects of Big Data Analytics for the Smart Grid

The role of energy is cardinal for achieving the Sustainable Development Goals (SDGs) through the enhancement and modernization of energy generation and management practices. The smart grid enables efficient communication between utilities and the end- users, and enhances the user experience by monitoring and controlling the energy transmission. The smart grid deals with an enormous amount of energy data, and the absence of proper techniques for data collection, processing, monitoring and decision-making ultimately makes the system ineffective. Big data analytics, in association with the smart grid, enable better grid visualization and contribute toward the attainment of sustainability. The current research work deals with the achievement of sustainability in the smart grid and efficient data management using big data analytics, that has social, economic, technical and political impacts. This study provides clear insights into energy data generated in the grid and the possibilities of energy theft affecting the sustainable future. The paper provides insights about the importance of big data analytics, with their effects on the smart grids’ performance towards the achievement of SDGs. The work highlights efficient real-time energy data management involving artificial intelligence and machine learning for a better future, to short out the effects of the conventional smart grid without big data analytics. Finally, the work discusses the challenges and future directions to improve smart grid technologies with big data analytics in action.

Guest Editorial Special Issue on Internet of Things for Smart Health and Emotion Care

As an information carrier, the Internet of Things (IoT) based on the Internet and sensing equipment makes all physical objects form an interconnected network. The 5th generation mobile networks (5G) technology has many advantages, such as high data rates, reduced latency, energy savings, reduced costs, increased system capacity and large-scale device connectivity, realize the real-time data collection, transmission, analysis, management, and application in the era of global Internet of Everything. In order to quickly respond to people’s daily requirements and provide the smart application based on artificial intelligence technology in various scenarios, the number of IoT devices will further increase. The integration of mobile-edge computing (MEC) and IoT is imperative, especially in industries needing real-time data computing, such as smart home, public security, automobile transportation, smart health, emotion care, etc. As a new form of IoT terminal combining 5G and MEC, wearable device based on intelligent fabrics plays an important role in smart health and emotion care, which is one of the potential development directions of the next generation of intelligent medical and rehabilitation systems.