Big Data Visualization Research Articles

Decision support model to adopt big data analytics in higher education systems

Data science or specifically data analytics systems have become an emerging trend in information technology and have attracted many organizations, including higher education. Higher Education Systems (HES) involve very active entities (students, faculty members, researchers, employers) who generate and require large volumes of data that go beyond the structured data stored in the house. The collection, analysis, and visualization of such big data present a huge challenge for HES. Big data analysis could be the solution to this challenge. However, the rationale and decision process for the adoption of big data analytics can be difficult. Such a knowledge-driven process requires a multitude of technical and organizational aspects that must be accounted for to ensure informed decisions are made. Existing research and development indicates that the decision to adopt, although systematic research with a theoretical background is rare and none of the existing studies have considered diffusion of innovation (DOI) theory. This paper aims to support HES, by providing a systematic analysis of the determinants for the decision to adopt big data analytics. An integrated framework referred to as the Technology Organization Environment (TOE) framework is proposed. The proposed framework is validated using structural equation modeling. Eleven determinants are confirmed that influence the TOE-driven framework for data analytics in HES. The result is expected to contribute to on-going research that attempts to address the complex and multidimensional challenge that relates to data science and analytics implementation in HES.

Application and Implementation of Big Data Visualization Technology in Network Security System

In order to improve the safety and reliability of the information system of the State Grid, platforms that apply technologies such as big data and artificial intelligence to realize the safety monitoring and management of information systems have been widely used. At the same time, the application of visualization technology can fully display the current security status and situation of the network, as well as the intrusion and attack process of various attacks and threats. This paper proposes to apply big data visualization technology to realize asset visualization, behavior visualization, and attack visualization, and to show the risk status, intrusion view and process of the entire network.

Digital Technologies in the Analysis of the State of Crime in the Prosecutor's Office

The article is devoted to the perspectives of digital technologies in crime analysis. According to the author, the key role of such analysis performed by prosecutors is played by artificial intelligence technologies, extra-big data, and electronic voice assistants. The need for their implementation, appropriate material and technical support, solutions for information security issues, as well as the training of prosecutors in the use of digital technologies, including the basics of programming and visualization of big data, were emphasized.

DSSAE-BBOA: deep learning-based weather big data analysis and visualization

DSSAE-BBOA: deep learning-based weather big data analysis and visualization

Competitive advantage of data-driven analytical capabilities: the role of big data visualization and of organizational agility

PurposeIn the digital age, the use of data and analytical capabilities to guide business decisions and operations plays a strategic role for organizations to gain competitive advantage (CA). However, the paths by which analytical capabilities convey their effect to CA are not yet fully known and few studies address the role of behavioral and cultural aspects of related of analytical capabilities. The purpose of this paper is to analyze how data-driven culture (DDC) and business analytics (BA) affect CA, considering the mediating effects of big data visualization (BDV) and organizational agility (OA).Design/methodology/approachA survey was conducted with 173 managers who are BDV and BA users in Brazilian organizations of various economic segments. The data were analyzed through structural equation modeling and mediation tests.FindingsThe evidence indicates that DDC and BDV are antecedents of BA. The following complementary mediations were discovered: BDV in the relationship between DDC and BA; BA in the relationship between DDC and CA; and OA in the relationship between BA and CA. It was also discovered that OA explains the transmission of most of the effect of BA to CA.Practical implicationsThis study can help organizations to understand the importance of cultural and behavioral aspects related to the use of the analytical capabilities. Thereby, managers can establish policies and strategies to extract value from data and leverage business agility and competitiveness through use BDV and BA.Originality/valueThis study fills an important research gap by developing an original research model and discussing empirical evidence on how DDC and BA affect CA, considering the mediating effects of BDV and OA.

Hotspot and Frontier Analysis of Exercise Training Therapy for Heart Failure Complicated With Depression Based on Web of Science Database and Big Data Analysis.

Background: Exercise training has been extensively studied in heart failure (HF) and psychological disorders, which has been shown to worsen each other. However, our understanding of how exercise simultaneously protect heart and brain of HF patients is still in its infancy. The purpose of this study was to take advantage of big data techniques to explore hotspots and frontiers of mechanisms that protect the heart and brain simultaneously through exercise training.Methods: We studied the scientific publications on related research between January 1, 2003 to December 31, 2020 from the WoS Core Collection. Research hotspots were assessed through open-source software, CiteSpace, Pajek, and VOSviewer. Big data analysis and visualization were carried out using R, Cytoscape and Origin.Results: From 2003 to 2020, the study on HF, depression, and exercise simultaneously was the lowest of all research sequences (two-way ANOVAs, p < 0.0001). Its linear regression coefficient r was 0.7641. The result of hotspot analysis of related keyword-driven research showed that inflammation and stress (including oxidative stress) were the common mechanisms. Through the further analyses, we noted that inflammation, stress, oxidative stress, apoptosis, reactive oxygen species, cell death, and the mechanisms related to mitochondrial biogenesis/homeostasis, could be regarded as the primary mechanism targets to study the simultaneous intervention of exercise on the heart and brain of HF patients with depression.Conclusions: Our findings demonstrate the potential mechanism targets by which exercise interferes with both the heart and brain for HF patients with depression. We hope that they can boost the attention of other researchers and clinicians, and open up new avenues for designing more novel potential drugs to block heart-brain axis vicious circle.

Gynecology Meets Big Data in the Disruptive Innovation Medical Era: State-of-Art and Future Prospects.

Tremendous scientific and technological achievements have been revolutionizing the current medical era, changing the way in which physicians practice their profession and deliver healthcare provisions. This is due to the convergence of various advancements related to digitalization and the use of information and communication technologies (ICTs)—ranging from the internet of things (IoT) and the internet of medical things (IoMT) to the fields of robotics, virtual and augmented reality, and massively parallel and cloud computing. Further progress has been made in the fields of addictive manufacturing and three-dimensional (3D) printing, sophisticated statistical tools such as big data visualization and analytics (BDVA) and artificial intelligence (AI), the use of mobile and smartphone applications (apps), remote monitoring and wearable sensors, and e-learning, among others. Within this new conceptual framework, big data represents a massive set of data characterized by different properties and features. These can be categorized both from a quantitative and qualitative standpoint, and include data generated from wet-lab and microarrays (molecular big data), databases and registries (clinical/computational big data), imaging techniques (such as radiomics, imaging big data) and web searches (the so-called infodemiology, digital big data). The present review aims to show how big and smart data can revolutionize gynecology by shedding light on female reproductive health, both in terms of physiology and pathophysiology. More specifically, they appear to have potential uses in the field of gynecology to increase its accuracy and precision, stratify patients, provide opportunities for personalized treatment options rather than delivering a package of “one-size-fits-it-all” healthcare management provisions, and enhance its effectiveness at each stage (health promotion, prevention, diagnosis, prognosis, and therapeutics).

An Empirical Study on Big Data Model and Visualization of Internet+ Teaching

In this paper, we conduct an in-depth study and analysis of “Internet+ Business English” teaching through teaching big data model and visualization and elaborate on the change of educational objectives in the context of Internet education. From the perspective of individual value coordinates, the goal of education is to adhere to the people-oriented, personalized, and comprehensive free development of human beings; from the perspective of social value coordinates, the goal of education is to cultivate innovative talents for social innovation and development; through the multiple perspectives of the curriculum in the context of Internet education, we analyze the goal orientation and value reshaping of the curriculum in the context of educational change. The curriculum of the future will develop toward an intelligent curriculum and introduces the curriculum design and the form of curriculum organization in the context of Internet education. A comparison of the constructed regression and classification of a total of 27 data models reveals that the model constructed based on all data in the integrated education system is the most effective. The multiple linear regression model explained up to 66.5% of the variance in student academic performance; the explanatory power of the social and demographic characteristics dimension variables ranged from approximately 13% to 18%, the personal characteristics dimension variables ranged from 7% to 20%, and the student input dimension variables ranged from 10% to 17%. The highest correct prediction rate of the binary logistic regression model was 69%.

Data Visualization Model Based on the User Experience

The data characterization is effectively explored based on the human cognitive features of data visualization conforming to the characteristics and habits of human beings. It analyzes the principle and characteristics of human cognition, and interprets the advantage of the graphic symbols and visual thinking in cognitive process. Furthermore, a model of the big data visualization is proposed based on the human cognitive law, which includes four main parts: strategic level, scope layer, structural layer and data presentation layer. It gives an example to expatiate that the current visualization of the cognitive model can be applied to the related data visualization, and realize the mapping between the visual cognitive thinking and data visualization.

Sensing and making sense of tourism flows and urban data to foster sustainability awareness: a real-world experience

Tourism is one of the world’s largest industries fundamentally arising from mobility as a form of capital. In destination islands that have a delicate ecosystem to maintain, this source of income can become problematic in terms of sustainability. A difficulty in making people aware of this issue is also represented by the fact that such sustainability-related issues (and their causes) are often not “visible” to citizens. To foster awareness about the relationship between sustainability and tourism in well-known destinations, we design a platform that engages users at two levels of participation: i. at the IoT and sensors level, in order to let them becoming providers of big data, deploying and enlarging the pervasive infrastructure; ii. at the (big) data visualization level, with the aim of engaging them in making sense of large volumes of data related to sustainability. This paper presents the design and implementation of a real-world experience where a low-cost collaborative platform made it possible to sense and visualize tourist flows and urban data into a rich interactive map-based visualization, open to the local communities. We deployed our case study in the Madeira archipelago, engaging locals and visitors of the island in two exploratory studies focused on measuring the impact of providing users with meaningful representations of tourism flows and related unperceivable aspects that affect the environmental sustainability. Analysing the findings of the two studies, we discuss the potentiality of using such a system to make sense of big data, fostering awareness about sustainability issues, and we point to future open challenges about citizens’ participation in sensing and making sense of big data.

Development of the Guidelines for Expressing Big Data Visualization

Development of the Guidelines for Expressing Big Data Visualization

Research on Visual Logistics Big Data Information Service System

Big data visualization is a new technology, from the core functions of data collection, storage, calculation, scheduling, advanced search, and other core functions, and based on cloud computing to scientifically manage data to ensure data security and stability. How to use big data visualization in logistics management is a problem that needs to be solved now. The paper introduces the main functional modules of the urban logistics big data platform and its system design and implementation. The platform is an information service platform with “big data” as the core and “data analysis” as the center, using cloud computing, significant data tooling, and other information technologies are used to establish an urban logistics big data platform to realize comprehensive functions such as credit evaluation of logistics and warehousing enterprises, display of warehousing space distribution and analysis of warehousing resource usage, and improve the public service capabilities of logistics institutes.

Prevention and Control of Pathogens Based on Big-Data Mining and Visualization Analysis.

Morbidity and mortality caused by infectious diseases rank first among all human illnesses. Many pathogenic mechanisms remain unclear, while misuse of antibiotics has led to the emergence of drug-resistant strains. Infectious diseases spread rapidly and pathogens mutate quickly, posing new threats to human health. However, with the increasing use of high-throughput screening of pathogen genomes, research based on big data mining and visualization analysis has gradually become a hot topic for studies of infectious disease prevention and control. In this paper, the framework was performed on four infectious pathogens (Fusobacterium, Streptococcus, Neisseria, and Streptococcus salivarius) through five functions: 1) genome annotation, 2) phylogeny analysis based on core genome, 3) analysis of structure differences between genomes, 4) prediction of virulence genes/factors with their pathogenic mechanisms, and 5) prediction of resistance genes/factors with their signaling pathways. The experiments were carried out from three angles: phylogeny (macro perspective), structure differences of genomes (micro perspective), and virulence and drug-resistance characteristics (prediction perspective). Therefore, the framework can not only provide evidence to support the rapid identification of new or unknown pathogens and thus plays a role in the prevention and control of infectious diseases, but also help to recommend the most appropriate strains for clinical and scientific research. This paper presented a new genome information visualization analysis process framework based on big data mining technology with the accommodation of the depth and breadth of pathogens in molecular level research.

Веб-картографирование ресурсов солнечной энергии Якутии

It is cost effective and environmentally rational to develop renewable energy source in sthe territories of decentralized energy supply, since it allows replacing diesel power generation. Yakutia has a high potential for the development of solar energy and 64 % of its area is in the zone of decentralized energy supply. The features of web mapping—interactivity, multiscale and availability—are useful for assessment of big multi-temporal data on solar resources at the regional scale. Web mapping shows one parameter for Yakutia—all sky insolation incident on a horizontal surface per day per square meter. The parameter is used to assess the potential of solar energy resources in the territory. Initial data comes from the Surface meteorology and Solar Energy archive of the NASA Prediction of Worldwide Energy Resource—SSE NASA POWER project. It includes monthly and annual average global grids for each year from 1984 to 2018. The web application allows users to map the data by grid points, by administrative units and by watersheds. Users can interactively set a period with an accuracy of a month and a territorial division for drawing up a map. The web-based mapping application allows users to create about 50,000 web maps in total. The number based on query combinations available to the user. A large amount of data used for web mapping requires the development of an information system for the dynamic delivery of data at the user’s request. Data preprocessing algorithm helps efficiently aggregate data “on-the-fly” for various territorial units. The DeckGL cartographic library gives high-performance visualization of big spatial data in the browser. We use PostgreSQL and Flask software to develop the information system. Web mapping is useful for the assessment of solar energy resources specifically at the regional level. Interactive tools provided by the web-based mapping applications deepen the analytical content of the cartographic work. There are notable changes in the data preparation through the design of web-based mapping applications in comparison with regular maps.

Crime Data Analysis, Visualization and Prediction Using LSTM

Crimes are common social problems that can even affect the quality of life, even the economic growth of a country. Big Data Analytics (BDA) is used for analyzing and identifying different crime patterns, their relations, and the trends within a large amount of crime data. Here, BDA is applied to criminal data in which, data analysis is conducted for the purpose of visualization. Big data analytics and visualization techniques were utilized to analyze crime big data within the different parts of India. Here, we have taken all the states of Indian for analysis, visualization and prediction. The series of operations performed are data collection, data pre-processing, visualization and trends prediction, in which LSTM model is used. The data includes different cases of crimes with in different years and the crimes such as crime against women and children in which, kidnap, murder, rape. The predictive results show that the LSTM perform better than neural network models. Hence, the generated outcomes will benefit for police and law enforcement organizations to clearly understand crime issues and that will help them to track activities, predict the similar incidents, and optimize the decision making process.

Urban planning and design strategy based on ArcGIS and application method

GIS is a spatial information system combining geography, cartography, remote sensing and computer science, which is widely used in different fields. As a computer system, GIS can input, store, query, analyze and display geographic data. In the 1980s, GIS gradually began to be applied in the field of urban planning. This kind of digital technology meets people's requirement for regional geographic environment visualization. At the same time, the development of the network has also provided help for the popularization and development of this technology, making the dissemination and sharing of data possible. Nowadays, as an important technology, GIS has been widely used in all fields of urban planning and has gradually become an indispensable part. Arcgis as GIS platform building and applications will be treated as big data analysis and data visualization as an important part of planning, implementation of the software data correlation of traditional urban planning, break traditional design and drawing mode, through the spatial analysis function to generate new information, make decisions and solve problems efficiently, and make a prediction to the development of future city. Through the introduction of Arcgis data organization structure, CAD data loading, mapping and modeling under Arcgis platform and Arcgis spatial analysis function, this paper discusses how to use Arcgis platform to realize the application of urban planning and design.

Research on Monitoring System of Building Slope Deformation Based on Internet of Things

A building slope deformation monitoring system based on the Internet of Things technology was studied in this paper. Through the powerful data acquisition, storage, calculation and analysis capabilities of new technologies such as sensors, cloud computing, big data communication and visualization, and a variety of monitoring data acquisition instruments, sensors, communication terminals, cloud platform and other software and hardware resources as the carrier, it can realize real-time online equipment, continuous data acquisition, real-time calculation, data analysis, emergency warning, and the whole process of deformation monitoring services. Our research solves the deficiency of traditional technical means and effectively improves the timeliness, reliability and information level of construction slope deformation monitoring.

Research on the university intelligent learning analysis system based on AI

The integration of Artificial Intelligence technology and school education had become a future trend, and became an important driving force for the development of education. With the advent of the era of big data, although the relationship between students’ learning status data was closer to nonlinear relationship, combined with the application analysis of artificial intelligence technology, it could be found that students’ living habits were closely related to their academic performance. In this paper, through the investigation and analysis of the living habits and learning conditions of more than 2000 students in the past 10 grades in Information College of Institute of Disaster Prevention, we used the hierarchical clustering algorithm to classify the nearly 180000 records collected, and used the big data visualization technology of Echarts + iView + GIS and the JavaScript development method to dynamically display the students’ life track and learning information based on the map, then apply Three Dimensional ArcGIS for JS API technology showed the network infrastructure of the campus. Finally, a training model was established based on the historical learning achievements, life trajectory, graduates’ salary, school infrastructure and other information combined with the artificial intelligence Back Propagation neural network algorithm. Through the analysis of the training resulted, it was found that the students’ academic performance was related to the reasonable laboratory study time, dormitory stay time, physical exercise time and social entertainment time. Finally, the system could intelligently predict students’ academic performance and give reasonable suggestions according to the established prediction model. The realization of this project could provide technical support for university educators.

Grafovske strukture za vizuelizaciju podataka

Networks are all around us. Graph structures are established in the core of every network system therefore it is assumed to be understood as graphs as data visualization objects. Those objects grow from abstract mathematical paradigms up to information insights and connection channels. Essential metrics in graphs were calculated such as degree centrality, closeness centrality, betweenness centrality and page rank centrality and in all of them describe communication inside the graph system. The main goal of this research is to look at the methods of visualization over the existing Big data and to present new approaches and solutions for the current state of Big data visualization. This paper provides a classification of existing data types, analytical methods, techniques and visualization tools, with special emphasis on researching the evolution of visualization methodology in recent years. Based on the obtained results, the shortcomings of the existing visualization methods can be noticed.

User-Centred Scalable Big Data Visualizer for Power Consumption Data in the Electrical Secondary Distribution Network

Establishment of Smart Grids for electrical power has been practised worldwide for the purpose of bringing reliability, security, and efficient management of electrical power networks for enhancing quality service to the society. Apart from the potential aim, smart grid has been a challenge to developing countries, including Tanzania from cost and technology point of view. Due to the use of many smart equipment involved in smart grids like Advanced Metering Infrastructure (AMI) equipped with smart meters and sensors, handling and managing big data has been a challenge. Among the challenges is the issue of visualizing the Big Data due to big volume generated with high velocity. This paper is developing a user-centered scalable big data visualizer for the electrical secondary distribution network by making use of design process model by Akanmu et al. (2017) and design activity framework by McKenna et al. (2014). The approach involves three phases: pre- development, development and post-development phase. The paper reviews several approaches in visualization and demonstrates effective big data visualization. The paper managed to visualize households’ units purchased against power consumed as well as balancing visualization of transformer phases.