Big Data Monitoring Research Articles

Comprehensiveness, Preciseness and Interconnectedness: How to Evaluate International Public Opinion Based on Cross-media Data Mining on the Internet

With emerging new media technologies, the analytic focus of research on international public opinions has switched from single content form, such as text, audio, image or video into multiple forms of media integrated together in cyberspace or in physical space. The depth and frequency of monitoring international public opinion on topics related to China are improving rapidly. This paper pinpoints the inadequacies of current monitoring activities and puts forward ways to analyze international public opinions on topics related to China based on technologies, such as web indexing, deep learning and data mining. Thematic discovery based on big data analytics, real-time surveillance and monitoring of trending topics, intelligent prediction of popularity, intelligent analysis of emotional valence, etc. can effectively support the establishment of a system using multimedia data to monitor and analyze international public opinion.

The Research Hotspots and Visualization Analysis Technology of Health Big Data

By understanding the international research hotspots in this field, we hope to provide reference information for health big data researchers to grasp the research hotspots and frontiers. By searching the relevant literature in the field of international health big data research in the Web of Science database, using the method of bibliometrics to draw a visual map. Results A total of 3850 articles were retrieved. Since 2013, the number of health big data research papers has shown a rapid growth. Harvard Medical School, University of Michigan and Stanford University are the research institutions with the largest number of papers in this field. The United States is the most active in the field of big data research, with 1506 papers, far higher than other countries, followed by China, the United Kingdom and South Korea. International health big data research hotspots include research on Intelligent wearable devices, design and development of health big data monitoring system, and application of health big data in precision medicine. The research reveals the characteristics and hotspots of international health big data research field, and provides some references for the development direction of this field in China.

Material analysis and big data monitoring of sports training equipment based on machine learning algorithm

Different machine learning algorithms predict the application effect of perovskite materials in sports training equipment. The sensitivity to material data is different on different ranges of data sets. Therefore, the algorithm needs to be selected according to specific material data samples. This study compares the prediction performance of neural network prediction algorithm (NN), genetic algorithm, and support vector machine-based machine learning algorithm (SVM) and uses statistical analysis to perform data analysis and draw corresponding curves. Moreover, this study uses a single perovskite material to verify the algorithm performance. In addition, based on the real data, the three machine learning algorithms of this study are applied to the related performance prediction, and the comparative analysis method is used to analyze the prediction performance of the machine learning algorithm. Through data analysis and chart analysis, we can see that machine learning algorithms have a certain effect in the application prediction of perovskite materials in sports training equipment. Among the three machine learning algorithms selected in this study, the performance of the machine learning algorithm based on support vector machine in all aspects is more excellent.

MULTIVERSE CROW CONSCIOUS CLUSTERING ROUTING AND COVERAGE OPTIMIZATION HEALTH MONITORING ALGORITHM DURING COVID-19 PANDEMIC

Covid-19 crisis has put the healthcare system under stress due to the shortage of healthcare workers in hospitals, the hour of the need is a highly flexible but dependable as well as an sys- tem which can alert hospital authorities immediately through centralized relay about the status of a patient if he/she becomes critical. Such a system may be built using a Big Data enabled Wireless Sensor Networks, which may be set up to monitor patients inside a hospital which shall be monitored through live channel feed by hospital monitoring authorities. The data from the sensors, through big data monitoring and analytics layer, will give crucial and timely results which will surely save many patients lives. In order to implement such a robust system, there is a need to build constant coverage and optimized clustering and routing algorithms which would solve this vital problem. This paper presents a novel energy-conscious clustering routing and coverage algorithm for both equal and unequal distribution in the wireless sensor system and is called as Multiverse Crow Conscious Clustering Routing and Coverage Optimization Algo- rithm. The algorithm has been able to successfully attain the equilibrium of Wireless Sensor Network community energy consumption, enhance energy efficiency, and extend lifespan of the entire network. From the coverage point of view, the algorithm has been able to attain the distribution optimization at a greater rate with a decrease cost, and increase the efficacy of this algorithm. To prove its effectiveness, we have compared the proposed algorithms to multiple Nature Inspired Optimized WSN scenarios.

RETRACTED: Big data monitoring of sports health based on microcomputer processing and BP neural network

Sports Health is the Ideal and effective medical care helps due to the possibility of sports health administration. Big Data Monitoring is based on the latest remote and wearable sensor innovations. Microcomputer processing based rapid development of innovation has surprisingly improved the scope of long-term well-being observation frameworks. Neural Network is an ongoing heart inspection framework created thinking about cost, ease of use, accuracy and data security. Sports health conceived to give an interface between specialist and patient for two-way correspondence. Health care's primary motivation behind this test is to encourage far-flung heart patients to obtain recent medical care administrations that cannot be the case due to the low specialist-tolerance ratio. An observational Big Data Monitoring created for people maturing somewhere between the ages using wearable sensors when possessing a device. The data security test shows that its proposed framework is fast and helpful. Tests have proven that the proposed health monitoring is helpful and reliable and effortlessly guarantees data security. The created Data Monitoring is ready to generate notice messages to the specialist and patient under basic conditions.

Design and Application of Intelligent Classroom Teaching Mode under the Blended Curriculum Reform

With the progress of network technology, under the magical fusion of network technology and education, intelligent classroom, a new education mode and education industry, has become the focus of current education research. Under the monitoring of big data, educators can create a comprehensive monitoring network for the curriculum, which makes a three-dimensional learners' learning effects, and also greatly extends learners' learning space and time. Smart classroom is also the inevitable product of the development of science and technology and education. In this paper, the author analyzes the concept and characteristics of smart classroom in the new era, and makes a detailed distinction between smart classroom, traditional classroom and flipped classroom, so as to clarify the concept of smart classroom. Then, according to the theory of blended learning and the theory of the elements of teaching mode, a new type of Higher Vocational English smart classroom is designed. Finally, relying on the questionnaire survey of students' attitude towards the teaching mode reform, teachers could get the teaching feedback of this experiment.

Artificial Intelligence‐ (AI‐) Enabled Internet of Things (IoT) for Secure Big Data Processing in Multihoming Networks

The automated techniques enabled with Artificial Neural Networks (ANN), Internet of Things (IoT), and cloud‐based services affect the real‐time analysis and processing of information in a variety of applications. In addition, multihoming is a type of network that combines various types of networks into a single environment while managing a huge amount of data. Nowadays, the big data processing and monitoring in multihoming networks provide less attention while reducing the security risk and efficiency during processing or monitoring the information. The use of AI‐based systems in multihoming big data with IoT‐ and AI‐integrated systems may benefit in various aspects. Although multihoming security issues and their analysis have been well studied by various scientists and researchers; however, not much attention is paid towards big data security processing in multihoming especially using automated techniques and systems. The aim of this paper is to propose an IoT‐based artificial network to process and compute big data processing by ensuring a secure communication multihoming network using the Bayesian Rule (BR) and Levenberg‐Marquardt (LM) algorithms. Further, the efficiency and effect on multihoming information processing using an AI‐assisted mechanism are experimented over various parameters such as classification accuracy, classification time, specificity, sensitivity, ROC, and F‐measure.

Possible future trajectory of COVID-19: emphasis on Africa.

Coronavirus disease 2019 (COVID-19) has caused morbidity and mortality in many countries. COVID-19 has also negatively affected the economy of several nations. The dynamics of interaction between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and host, and possible evolution of the virus into more virulent strains pose a threat to global eradication. With the advent of vaccination in most countries, vaccine hesitancy, especially in Africa, is expected to reduce. We also believe that the COVID-19 vaccine would have substantial impact on reducing incidence, hospitalizations, and deaths. A predictor model for COVID-19 infection pattern through to 2025 suggests that recurrent outbreaks are likely to occur. There is a prediction that Africa would not fully recover from the economic crises posed by the pandemic; nonetheless, we expect that economic activities on the continent will improve as countries undertake mass vaccinations and populace attain herd immunity. The growth of e-commerce has been remarkable during the pandemic and we don´t expect trend to decline anytime soon. The pandemic has led to technology and digital platform utilization and/or improvement, which invariably has the tendency to improve quality of lives in the future. These include effective big data monitoring, online shopping, among others. Our future trajectory for recurrent waves of COVID-19 is that these may occur in winter months in temperate climates. We believe that COVID-19 has strengthened Africa´s resilience to future pandemics.

Innovative Mobile Application for Measuring Big Data Maturity: Case of SMEs in Thailand

A Big Data maturity model (BDMM) is one of the key tools for Big Data assessment and monitoring, and a guideline for maximizing the usage and opportunity of Big Data in organizations. The development of a BDMM for SMEs is a new concept and is challenging in terms of development, application, and adoption. This article aims to create the novel online adaptive BDMM via responsive web application for SMEs. We develop the BDMM API and a responsive web application for easy access via mobile phone. We developed a model by analyzing the factors impacting the success of implementing Big Data Analytics (BDA) in SMEs based on literature reviews. The model was verified by conducting a survey of 180 SMEs in Thailand, interviewed against four extracted domains. Then, the scoring and classified levels for the model was developed through Latent Class Analysis (LCA) to depict four levels of each domain and four final maturity levels to create an adaptive model. As the experimental results with 33 users including executive officers, managers, IT and data analytic officers .The user acceptance for our mobile application using TAM indicates that executive officers group and non-executive group satisfied perceived usefulness, perceived ease of use, and intention to use factor. Use cases of the application include SMEs monitoring for their Big Data Analytics capability for improvement, and the Government Agency providing proper support on SMEs’ level of competency.

Smart cities in the time of climate change and Covid‐19 need digital twins

Smart cities in the time of climate change and Covid‐19 need digital twins

Research on the Personal Privacy in the Health Big Data

Data mining, data prediction and all-round digital monitoring of health big data make the dilemma of personal privacy control prominent. The weakening of the control of personal privacy by big data technology, the people’s data belief, the diversity of interests and the conflict of interests are the main causes of the problem of personal privacy in the context of the application of health big data. Therefore, in the application of health big data, the solution to the problem of personal privacy in the context of health big data application is to enhance the value transparency of big data technology, return and reshape humanism, and explore common values to reduce conflicts of interest.

Framework and Application of a Big Data Monitoring System for Mining with a Pillar-Free Self-Forming Roadway

The construction of a self-formed roadway without coal pillars is a new mining technology based on short-arm beam formation through roof cutting. With it, mining, tunneling and retaining roadway construction can be accomplished in a ‘three-in-one’ process that removes the need to dig in advance at the working face and to leave behind coal pillars. In order to realize real-time monitoring and warnings regarding rock pressure during this process, a big data monitoring system was developed based on quasi-distributed fiber Bragg grating (FBG) sensing technology and cloud technology. Firstly, real-time monitoring data on the stress and strain of the underground surrounding rock-support system are obtained by FBG sensor and transmitted to the main computer of the above-ground monitoring and early warning system by using an underground industrial ring network. These data are then sent to a big data remote online real-time monitoring system. Through the deployment of a cloud server, authorized users can observe changes in force and movement in the rock surrounding the supporting system during coal mining and roadway formation from anywhere and at any time. The successful application of the system in the S1201-II working face of the Ning Tiaota Mine shows that this remote real-time monitoring system can enable timely and accurate field data acquisition, feedback real-time production information and achieve good monitoring performance. This study thus provides a scientific basis for ensuring safe mining with the coal pillar-free self-forming roadway method.

The quality management ecosystem for predictive maintenance in the Industry 4.0 era

The Industry 4.0 era requires new quality management systems due to the ever increasing complexity of the global business environment and the advent of advanced digital technologies. This study presents new ideas for predictive quality management based on an extensive review of the literature on quality management and five real-world cases of predictive quality management based on new technologies. The results of the study indicate that advanced technology enabled predictive maintenance can be applied in various industries by leveraging big data analytics, smart sensors, artificial intelligence (AI), and platform construction. Such predictive quality management systems can become living ecosystems that can perform cause-effect analysis, big data monitoring and analytics, and effective decision-making in real time. This study proposes several practical implications for actual design and implementation of effective predictive quality management systems in the Industry 4.0 era. However, the living predictive quality management ecosystem should be the product of the organizational culture that nurtures collaborative efforts of all stakeholders, sharing of information, and co-creation of shared goals.

Big Data Processing and Monitoring System based on Vehicle Data

Big Data Processing and Monitoring System based on Vehicle Data

Application of a Continuous Terrestrial Photogrammetric Measurement System for Plot Monitoring in the Beijing Songshan National Nature Reserve

Monitoring sample areas is the basis of ecological management. Songshan National Nature Reserve is one of the most important components of the ecosystem of the central metropolitan area of Beijing, and has fallen behind in its monitoring technology and methods. So, updating the existing equipment and technology is necessary. The current system suffers from high equipment costs and is not convenient to carry, so the work efficiency is low. Furthermore, the data cannot be visualized in three dimensions (3D), and complex terrain conditions cannot be measured. Therefore, this study researched and developed a continuous terrestrial photogrammetric measurement system that is theoretically based on the principles of photogrammetry, image processing technology, and dendrometry. The system applies a self-developed personal digital assistant (PDA) photogrammetry-based dendrometer and software to continuously evaluate stand sampling areas. Through experimental verification, the relative root mean square error (RMSE) of the trunk diameter measurements was found to be 5.59%, and the relative RMSE of hypsometrical measurements was 3.93%, which are both higher than the accuracy required for traditional forestry surveys. Furthermore, the advantages of this system include its low cost, lightweight equipment, easy operation, high measurement efficiency, 3D visualization, and applicability under complex terrain conditions. Since it is highly accurate and efficient, the continuous terrestrial photogrammetric system can be easily applied to monitor stand sampling areas in Songshan National Nature Reserve. In addition, it can be applied to second-class forest surveys in China, thus guaranteeing the monitoring of big data for the ecological environment of China.

Clarifying the hydrological mechanisms and thresholds for rainfall-induced landslide: in situ monitoring of big data to unsaturated slope stability analysis

The development of early warning systems for landslide hazards has long been a challenge because the accuracy of such systems is limited by both the complicated underlying mechanisms of landslides and the lack of in situ data. In this study, we implemented a multivariate threshold criterion that integrates in situ monitoring data and data from unsaturated hydro-mechanical analyses as an early warning system for rainfall-induced landslides in the Wenchuan earthquake region of China. The results indicate that rainfall intensity is closely correlated with the probability of landslide occurrence. Variations in matric suction and suction stress were obtained from in situ measurements and used to quantify the soil water retention curve, which presented clear hysteresis characteristics. The impacts of rainfall infiltration on slope failure in post-earthquake landslide areas under transient rainfall conditions were quantified by hydro-mechanical modelling theories. Variations in the suction stress of unsaturated soil were used to calculate the safety factor. The influence of hydrological hysteresis processes on the slope failure mechanism was analysed. Multivariate threshold criteria that include the intensity-probability (I-P) threshold, soil moisture and matric suction based on in situ big data and unsaturated slope stability analysis benchmarks are proposed for use in an early warning system for rainfall-induced landslides.

Mobile-based Big Data Processing and Monitoring Technology in IoT Environment

Mobile-based Big Data Processing and Monitoring Technology in IoT Environment

Big Data, Sport, and the Digital Divide

This article considers the relationship between Big Data and the athlete. Where Beer and Hutchins have focused on Big Data and sport, this article concentrates on the athlete’s potential response to Big Data monitoring. Drawing on the work of Andrejevic, and Kennedy and Moss, the project speaks to the Big Data–athlete relation through the theoretical framework of the digital divide. It describes Big Data and its relation to the digital divide before tracing out how athletes might respond to Big Data monitoring by presenting concerns about privacy and/or embracing a quantified self. Considering these responses provides a starting point for further work on how athletes should treat Big Data and its implications for sport.

Grasping Popular Applications in Cellular Networks With Big Data Analytics Platforms

Internet access through cellular networks is rapidly growing, driven by the great success of the mobile apps paradigm and the overwhelming popularity of social-related multimedia services such as YouTube, Facebook, or even WhatsApp. Understanding the functioning, performance, and traffic generated by these applications is paramount for ISPs, especially for cellular operators, who must manage the huge surge of volume and number of users with the constraints and challenges of cellular networks. In this paper, we study important networking aspects of three popular applications in cellular networks: YouTube, Facebook, and WhatsApp. Our evaluations span the content delivery networks hosting these services, their traffic characteristics, and their performance. The analysis is performed on top of real cellular network traffic monitored at the nationwide cellular network of a major European ISP. Due to privacy issues and given the huge amount of data generated by these applications as well as the large number of monitored customers, the analysis has been done in an online fashion, using a customized big data analytics (BDA) platform called DBStream. We overview DBStream and discuss other potential solutions currently available for traffic monitoring and analysis of big networking data. To the best of our knowledge, this is the first paper providing a complete analysis of popular services in cellular networks, using BDA platforms.

Pattern Detection in Cyber-Physical Systems

A Cyber-Physical System (CPS) integrates physical devices (i.e., sensors) with cyber (i.e., informational) components to form a context sensitive system that responds intelligently to dynamic changes in real-world situations. A core element of CPS is the collection and assessment of information from noisy, dynamic, and uncertain physical environments that must be transformed into usable knowledge in real-time. Machine learning algorithms such as cluster analysis can be used to extract useful information and patterns from data generated from physical devices based on which novel applications of CPS can make informed decisions. In this paper we propose to use a density-based data stream clustering algorithm, built on the Multiple Species Flocking model, for the monitoring of big data, generated from numerous applications such as machine monitoring, health monitoring, sensor networks. In the proposed approach, approximate results are available on demand at anytime, so it is particularly apt for real life monitoring applications.