Visual Analytics Technologies Research Articles

Wireless Sensor Network‐Based Mathematical Modeling for Communication Intelligent Monitoring System

Knowing the behavioral patterns of city residents is of great value in formulating and adjusting urban planning strategies, such as urban road planning, urban commercial development, and urban pedestrian flow control. Based on the high penetration rate of cell phones, it is possible to indirectly understand the behavior of city residents based on the call records of users. However, the behavioral patterns of large‐scale users over a long period of time can present characteristics such as large dispersion, difficult to discover patterns, and difficult to explain behavioral patterns. In this paper, we design and implement a human behavior pattern analysis system based on massive mobile communication data based on serial data modeling method and visual analysis technology. For the problem that it is difficult to capture the behavioral patterns of residents in cities in call records, this paper constructs base station trajectories based on users’ cell phone call records and uses users’ long‐time base station trajectories to mine users’ potential behavioral patterns. Since users with similar activity characteristics will exhibit similar base station trajectories, this paper focuses on the similarity between text sequences and base station trajectory sequences and combines the word embedding method in natural language processing to build a Cell2vec model to identify the semantics of base stations in cities. In order to obtain the group behavior patterns of users from the base station trajectories of group users, a user clustering method based on users’ regional mobile preferences is proposed, and the results are projected using the Stochastic Neighbor Embedding (t‐SNE) algorithm to expose the clustering features of large‐scale cell phone users in the low‐dimensional space. To address the problem that user behavior patterns are difficult to interpret, a visual analysis model with group as well as regional semantics is designed for the spatial and temporal characteristics of user behavior. Among them, the clustering model uses the distance between scatter points to map the similarity between users, which helps analysts to explore the behavioral characteristics of group users.

Incorporating visual analytics with knowledge construction in problem-based learning: a qualitative study

ABSTRACT Visual analytics (VA) technology has become a significant tool for business decision-makers to explore novel insights. One of its key features is analytical reasoning through interactive visualization that users have direct sensemaking in problem-solving processes. Few studies have examined how VA can be applied in business education for improving educational purpose. Social cognitive theory grounded in the current study as a framework to guide a qualitative exploration. The investigation was guided by two research questions: (1) What are the benefits and challenges of VA used in business education? (2). How do students interact with VA to improve knowledge construction in problem-solving? In-depth interviews and focus groups were conducted to derive data for thematic analysis from 25 informants. Three researchers conducted coding and data analysis through Nvivo 11.0. Five themes were identified in the corpus of data: (1). Perceived benefits to learning, (2). Prerequisite conditions, (3). Application context, (4). Motivational engagement, and (5). Knowledge construction. The study suggests that incorporating VA technology with problem-solving facilitates students’ knowledge organisation and acquisition.

Infusing visual analytics technology with business education: An exploratory investigation in fostering higher-order thinking in China

ABSTRACT Visual analytics technology enjoys a growing trend in data analysis and visualisation science. The potential of visual analytics application in classroom learning remains opaque. We integrated visual analytics tool in learning to examine higher-order thinking in terms of technology’s innovation attributes, learning satisfaction, and epistemological beliefs advancement. A cross-sectional survey of 247 undergraduate business students was conducted in a south China university. Result suggests that three features of visual analytics shared variance in satisfactory learning. This satisfactory VA-assisted learning predicts business students’ higher-order thinking, while the change in epistemological beliefs only occurs with satisfactory evaluation. Implications for education practitioners to adopt technological transformation have been provided.

Visualization Analysis of Intelligent Vehicles Research Field Based on Mapping Knowledge Domain

This study combines applied mathematics, visual analysis technology, information science with an approach of Scientometrics to systematically analyze the development status, research distribution and future trend of intelligent vehicles research. A total number of 3933 published paper index by SCIE and SSCI from 2000 to 2019 are researched based on Mapping Knowledge Domain (MKD) and Scientometrics approaches. Firstly, this paper analyzes the literature content in the field of intelligent vehicles by including the literature number, literature productive countries, research organization, co-authorship of main research groups and the journals from which the articles are mainly sourced. Then, co-citation analysis is used to obtain five major research directions in the field of intelligent vehicles, which include “system framework”, “internet of vehicles”, “intersection control algorithms”, “influence on traffic flow”, and “policies and barriers”, respectively. The keyword co-occurrence analysis is applied to identify four dominant clusters: “planning and control system”, “autonomous vehicle questionnaire”, “sensor and vision”, and “connected vehicles”. Finally, we divide burst keywords into three phases according to the publication date to show more clearly the change of research focus and direction over time.

Research on visual analysis technology and application of urban road data

Many cities use the current wealth of multi-source data to create intelligent transportation systems, which visually process road data so that traffic consumers and traffic managers can better understand urban traffic conditions. Due to the heterogeneity, complexity and mass of these data, it is not easy to conduct effective analysis on them, and it is often necessary to integrate human perception in the analysis process, so as to cause extensive application of visualization. In this paper, we first briefly introduce the pre-processing of urban trajectory data, then systematically analyze the visualization of two data types of road traffic flow and traffic events, and finally briefly summarize the research trend of traffic visualization in recent years and put forward corresponding challenges. The results show that the early research focused on the visualization of road flow, including the main arrow diagram, Mosaic diagram, track wall, etc. With the deepening of visual analysis, the analysis of traffic flow increases to the analysis of traffic events. In recent years, there has been a new trend of mining and utilizing the attributes or semantics of traffic trajectories or traffic events. The research on traffic data visualization has been greatly expanded in breadth and depth.

Evaluating visual analytics for health informatics applications: a systematic review from the American Medical Informatics Association Visual Analytics Working Group Task Force on Evaluation.

This article reports results from a systematic literature review related to the evaluation of data visualizations and visual analytics technologies within the health informatics domain. The review aims to (1) characterize the variety of evaluation methods used within the health informatics community and (2) identify best practices. A systematic literature review was conducted following PRISMA guidelines. PubMed searches were conducted in February 2017 using search terms representing key concepts of interest: health care settings, visualization, and evaluation. References were also screened for eligibility. Data were extracted from included studies and analyzed using a PICOS framework: Participants, Interventions, Comparators, Outcomes, and Study Design. After screening, 76 publications met the review criteria. Publications varied across all PICOS dimensions. The most common audience was healthcare providers (n = 43), and the most common data gathering methods were direct observation (n = 30) and surveys (n = 27). About half of the publications focused on static, concentrated views of data with visuals (n = 36). Evaluations were heterogeneous regarding setting and measurements used. When evaluating data visualizations and visual analytics technologies, a variety of approaches have been used. Usability measures were used most often in early (prototype) implementations, whereas clinical outcomes were most common in evaluations of operationally-deployed systems. These findings suggest opportunities for both (1) expanding evaluation practices, and (2) innovation with respect to evaluation methods for data visualizations and visual analytics technologies across health settings. Evaluation approaches are varied. New studies should adopt commonly reported metrics, context-appropriate study designs, and phased evaluation strategies.

SINDVis: User-Centered Dynamic Interactive Visualization System for Space Information Networks

As an important national strategy infrastructure, the Space Information Network (SIN) is a powerful platform for future information support. In this paper, we design and implement a user-centered, dynamic, interactive visualization system (SINDVis), and aim to assist multi-class users to understand, build, develop, maintain, and manage the SIN. We introduce the concept and architecture of SIN, summarize the key technologies of dynamic visualization and visual analysis, and analyze the basic characteristics of three types of users. Combining the content above, we design the architecture of SINDVis from an input module, a core-processing module, an output module, and a user body. We also describe eight basic functions of the entity domain view (GeoView) and topology domain view (TopolView). Meanwhile, we analyze the implementation methods of the GeoView and TopolView, including an improved Force-Directed Algorithm (FDA) layout, Fusion of Animation and Timeline (FAT) visualization, and Panning and Zooming (P&Z) interactions. We analyze the experiment platforms and running environments of the GeoView and TopolView and realize the main contents of both views with a typical SIN. The results also verify the validity and feasibility of the theories and methods proposed. Finally, we discuss and analyze experimental results and the advantages and disadvantages of the SINDVis and look forward to future work. With the development of visualization and visual analysis technology, both application-driven and user-interaction features are gradually highlighted. We introduce the visualization technology into the field of SIN in order to provide new ideas for the basic theory and key technology research of SIN.

Determinants of visual analytics adoption in organizations

PurposeVisual analytics is increasingly becoming a prominent technology for organizations seeking to gain knowledge and actionable insights from heterogeneous and big data to support decision-making. Whilst a broad range of visual analytics platforms exists, limited research has been conducted to explore the specific factors that influence their adoption in organizations. The purpose of this paper is to develop a framework for visual analytics adoption that synthesizes the factors related to the specific nature and characteristics of visual analytics technology.Design/methodology/approachThis study applies a directed content analysis approach to online evaluation reviews of visual analytics platforms to identify the salient determinants of visual analytics adoption in organizations from the standpoint of practitioners. The online reviews were gathered from Gartner.com, and included a sample of 1,320 reviews for six widely adopted visual analytics platforms.FindingsBased on the content analysis of online reviews, 34 factors emerged as key predictors of visual analytics adoption in organizations. These factors were synthesized into a conceptual framework of visual analytics adoption based on the diffusion of innovations theory and technology–organization–environment framework. The findings of this study demonstrated that the decision to adopt visual analytics technologies is not merely based on the technological factors. Various organizational and environmental factors have also significant influences on visual analytics adoption in organizations.Research limitations/implicationsThis study extends the previous work on technology adoption by developing an adoption framework that is aligned with the specific nature and characteristics of visual analytics technology and the factors involved to increase the utilization and business value of visual analytics in organizations.Practical implicationsThis study highlights several factors that organizations should consider to facilitate the broad adoption of visual analytics technologies among IT and business professionals.Originality/valueThis study is among the first to use the online evaluation reviews to systematically explore the main factors involved in the acceptance and adoption of visual analytics technologies in organizations. Thus, it has potential to provide theoretical foundations for further research in this important and emerging field. The development of an integrative model synthesizing the salient determinants of visual analytics adoption in enterprises should ultimately allow both information systems researchers and practitioners to better understand how and why users form perceptions to accept and engage in the adoption of visual analytics tools and applications.

A bibliometric analysis and visualization of environmental damage research from 2000 to 2018

In recent years, the frequent eruptions of environmental pollution and environmental damage have caused great damage to the human life, property and the ecological environment, so more and more attention has been paid to the research on environmental damage. The study of environmental damage involves many disciplines, such as law, theoretical economics, applied economics, environmental science and engineering, political science, and so on. It is a comprehensive field of integration. In this paper, we used co-occurrence visualization analysis technology to make a detailed analysis of more than 1000 documents of Web of Science database. The co-occurrence of keywords can directly reflect the research hotspots in the field. Finally, we use the index of “emergence rate” to analyze the feasible direction of future research to provide references for future research.

The hidden architecture of higher education: building a big data infrastructure for the \u2018smarter university\u2019

Universities are increasingly organized and managed through digital data. The collection, processing and dissemination of Higher Education data is enabled by complex new data infrastructures that include both human and nonhuman actors, all framed by political, economic and social contingencies. HE data infrastructures need to be seen not just as technical programs but as practical relays of political objectives to reform the sector. This article focuses on a major active data infrastructure project in Higher Education in the United Kingdom. It examines the sociotechnical networks of organizations, software programs, standards, dashboards and visual analytics technologies that constitute the infrastructure, and how these technologies are fused to governmental imperatives of market reform. The analysis foregrounds how HE is being reimagined through the utopian ideal of the ‘smarter university’ while simultaneously being reformed through the political project of marketization.

Preface

The papers in this special issue were presented at IEEE VIS 2016, held during October 23-28, 2016 in Baltimore, MD. VIS contains three conferences, held concurrently: the IEEE Visual Analytics Science and Technology Conference (IEEE VAST 2016), the IEEE Information Visualization Conference (IEEE InfoVis 2016), and the IEEE Scientific Visualization Conference (IEEE SciVis2016).

Real-time grouting monitoring and visualization analysis system for dam foundation curtain grouting

A real-time monitoring and 3D visualization analysis system is proposed for dam foundation curtain grouting. Based on the real-time control technology, the optimization method and the set theory, a mathematical model of the system is established. The real-time collection and transmission technology of the grouting data provides a data foundation for the system. The real-time grouting monitoring and dynamic alarming method helps the system control the grouting quality during the grouting process, thus, the abnormalities of grouting, such as jacking and hydraulic uplift, can be effectively controlled. In addition, the 3D grouting visualization analysis technology is proposed to establish the grouting information model (GIM). The GIM provides a platform to visualize and analyze the grouting process and results. The system has been applied to a hydraulic project of China as a case study, and the application results indicate that the real-time grouting monitoring and 3D visualization analysis for the grouting process can help engineers control the grouting quality more efficiently.

State of the Journal

IEEE Transactions on Visualization and Computer Graphics (TVCG) has published more papers in 2013 than in any previous year. TVCG continues to be in an excellent state. For the first time, the entire proceedings of IEEE VAST 2013 papers became part of the VIS special issue of TVCG. At the start of October 2013, TVCG had received more than 265 regular submissions, more than last year at the same time. This year we also observed a healthy number of 150 and 402 submissions to the IEEE VR Conference issue and the VIS conference issue that contains the Proceedings of the IEEE Information Visualization, Scientific Visualization, and Visual Analytics Science and Technology 2013 Conferences, respectively. We are expecting a total of nearly 900 submissions to TVCG by the end of 2013. A total of 137 articles were published in the first 10 regular issues with 1,769 printed pages, and the VR and VIS special issues containing 21 and 101 conference papers, respectively. All submissions in both special issues went through a rigorous two-round journalquality review process. Practically all the 2012 papers have also been decided. From the 293 regular submissions (including 20 extended versions of Best Papers from several top venues in graphics and visualization), 76 regular papers and all 20 special section papers were eventually accepted; 86 out of 333 SciVis plus InfoVis conference submissions were published in the VIS special issue. TVCG continues to offer authors a remarkably effi cient processing of submitted manuscripts: The average time from submission to fi rst decision is about three months and the average time from submission to publication as a preprint in the digital library is about seven months. Its 2012 impact factor is 1.895 with the largest number of total publications appeared two years prior. During 2013, the authors of TVCG regular papers were invited to give an oral presentation of their recent work at TVCG’s partner conferences. A total of 35 TVCG papers were presented at the IEEE Virtual Reality Conference, ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Pacifi c Graphics, and IEEE VIS 2013.

Interactive Visualization for Patient-to-Patient Comparison.

A visual analysis approach and the developed supporting technology provide a comprehensive solution for analyzing large and complex integrated genomic and biomedical data. This paper presents a methodology that is implemented as an interactive visual analysis technology for extracting knowledge from complex genetic and clinical data and then visualizing it in a meaningful and interpretable way. By synergizing the domain knowledge into development and analysis processes, we have developed a comprehensive tool that supports a seamless patient-to-patient analysis, from an overview of the patient population in the similarity space to the detailed views of genes. The system consists of multiple components enabling the complete analysis process, including data mining, interactive visualization, analytical views, and gene comparison. We demonstrate our approach with medical scientists on a case study of childhood cancer patients on how they use the tool to confirm existing hypotheses and to discover new scientific insights.

Visual analytics, cognition and archival arrangement and description: studying archivists’ cognitive tasks to leverage visual thinking for a sustainable archival future

This paper explores the possibility of using visual analysis to support archival functions. It first reviews the current state of experimentation with the use of interactive visual interfaces in the archival domain, noting several gaps including the absence of formal studies of cognitive and perceptual tasks. The paper then reports on preliminary results from a cognitive task analysis of archival arrangement and description, providing a broad brush description of archival analysis during arrangement and description as an example of archival sense-making. It then suggests some possible leverage points where visual analytics technology might be applied to support archival arrangement and description and concludes by reflecting on some theoretical and practical aspects of visualization technologies.

Guest Editors' Introduction: Special Section on the IEEE Conference on Visual Analytics Science and Technology (VAST)

Visual Analytics (VA) is an evolving field that, at its core, is directed to the science of analytical reasoning supported by highly interactive visual interfaces. The IEEE Conference on Visual Analytics Science and Technology (IEEE VAST), founded in 2006 as the IEEE Symposium on Visual Analytics Science and Technology, is the first international conference dedicated to advances in Visual Analytics Science and Technology. The IEEE Transactions on Visualization and Computer Graphics (TVCG) has recognized and honored the importance of Visual Analytics from the beginning, and invites the authors of the best conference papers to submit substantively extended versions of VAST papers to the journal. For these papers, TVCG applies the usual standard in asking for more than 30% new material and insights compared to the conference paper. This special section presents the extended versions of the best papers of IEEE VAST 2011, which took place in October 2011 in Providence, Rhode Island, USA. These papers were selected together with the best paper award selection committee, which was composed of three members who reviewed the top papers and their peer reviews. The three selected papers went through the regular and standard reviewing process of TVCG. The papers presented here reflect the diversity of the growing field of visual analytics. Collectively, the set of papers exemplify three components that are central to visual analytics as a field. the Guest Editors then provide an overview of the technical articles and features presented in this issue.

Concern level assessment: Building domain knowledge into a visual system to support network-security situation awareness

Information officers and network administrators require tools to help them achieve situation awareness about potential network threats. We describe a response to mini-challenge 1 of the 2012 IEEE Visual Analytics Science and Technology challenge in which we developed a visual analytic solution to a network-security situation awareness problem. To support conceptual design, we conducted a series of knowledge elicitation sessions with domain experts. These provided an understanding of the information they needed to make situation awareness judgements as well as a characterisation of those judgements in the form of production rules, which define a parameter we called the ‘concern level assessment’. The concern level assessment was used to provide heuristic guidance within a visual analytic system called Middlesex Spatial Interactive Visualisation Environment. An analysis of Visual Analytics Science and Technology challenge assessment sessions using Middlesex Spatial Interactive Visualisation Environment provides some evidence that intelligent heuristics like these can provide useful guidance without unduly dominating interaction and understanding.

Evaluation of visual analytics environments: The road to the Visual Analytics Science and Technology challenge evaluation methodology

Evaluation of software can take many forms ranging from algorithm correctness and performance to evaluations that focus on the value to the end user. This article presents a discussion of the development of an evaluation methodology for visual analytics environments. The Visual Analytics Science and Technology Challenge was created as a community evaluation resource. This resource is available to researchers and developers of visual analytics environments and allows them to test out their designs and visualization and compare the results with the solution and the entries prepared by others. Sharing results allows the community to learn from each other and to hopefully advance more quickly. In this article, we discuss the original challenge and its evolution during the 7 years since its inception. While the Visual Analytics Science and Technology Challenge is the focus of this article, there are lessons for many involved in setting up a community evaluation program, including the need to understand the purpose of the evaluation, decide upon the right metrics to use, and the appropriate implementation of those metrics including datasets and evaluators. For ongoing evaluations, it is also necessary to track the evolution and to ensure that the evaluation methodologies are keeping pace with the science being evaluated. The discussions on the Visual Analytics Science and Technology Challenge on these topics should be pertinent to many interested in community evaluations.

Visual Analysis of Social Media Data

The application of visual analytics, which combines the advantages of computational knowledge discovery and interactive visualization, to social media data highlights the many benefits of this integrated approach. The Web extra at http://youtu.be/nhoq71gqyXE is a video demonstrating a prototype system for visual-interactive analysis of large georeferenced microblog datasets, describing the design of the system, and detailing its application to the VAST 2011 Challenge dataset. The dataset models an epidemic outbreak in a fictitious metropolitan area. The video shows how the system can detect the epidemic and analyze its development over time. The system was implemented by Juri Buchmueller, Fabian Maass, Stephan Sellien, Florian Stoffel, and Matthias Zieker at the University of Konstanz (they also produced this video). Further information on the system and the VAST challenge dataset can be found in E. Bertini et al., Analytics of Terrorist Activities Related to Epidemics, Proc. IEEE Conf. Visual Analytics Science and Technology (VAST 11), IEEE CS, pp. 329-330, 2011.

Augmenting the educational curriculum with the Visual Analytics Science and Technology Challenge: Opportunities and pitfalls

With its mission to move science into practice, the Visual Analytics Science and Technology Challenge has become an integrated part of the annual Visual Analytics Science and Technology Conference since its inception in 2006. In this article, we discuss how we can transfer this objective into a classroom setting by using the Visual Analytics Science and Technology Challenge datasets and by encouraging student submissions to the challenge. By means of Bloom’s Taxonomy of Educational Objectives for Knowledge-Based Goals, we show how the Visual Analytics Science and Technology Challenge enables the integration of additional learning objectives into two types of courses: a dedicated course that focuses on the contest participation and an integrated course that uses the contest data to emphasize practical course elements. The core contribution of this article is that we assess the opportunities and pitfalls that we experienced at the University of Konstanz in Germany and Virginia Tech in the United States when augmenting the educational curriculum with the Visual Analytics Science and Technology Challenge.