Application of Mathematical Optimization in Data Visualization and Visual Analytics: A Survey

Visualization and visual analytics are powerful concepts for exploring data from various application domains. The endless number of possible parameters and the many ways to combine visual variables as well as algorithms and interaction techniques create lots of possibilities for building such techniques and tools.The major goal of those tools is to include the human users with their tasks at hand, their hypotheses, and research questions to provide ways to find solutions to their problems or at least to hint them in a certain direction to come closer to a problem solution. However, due to the sheer number of design variations, it is unclear which technique is suitable for those tasks at hand, requiring some kind of user evaluation to figure out how the human users perform while solving their tasks.The technology of eye tracking has existed for a long time; however, it has only recently been applied to visualization and visual analytics as a means to provide insights to the users’ visual attention behavior. This generates another kind of dataset that has a spatio-temporal nature and hence demands for advanced data science and visual analytics concepts to find insights into the recorded eye movement data, either as a post process or even in real-time.This book describes aspects from the interdisciplinary field of visual analytics, but also discusses more general approaches from the field of visualization as well as algorithms and data handling. A major part of the book covers research on those aspects under the light and perspective of eye tracking, building synergy effects between both fields – eye tracking and visual analytics – in both directions, i.e. eye tracking applied to visual analytics and visual analytics applied to eye tracking data. Technical topics discussed in the book include: • Visualization; • Visual Analytics; • User Evaluation; • Eye Tracking; • Eye Tracking Data Analytics;Eye Tracking and Visual Analytics includes more than 500 references from the fields of visualization, visual analytics, user evaluation, eye tracking, and data science, all fields which have their roots in computer science.Eye Tracking and Visual Analytics is written for researchers in both academia and industry, particularly newcomers starting their PhD, but also for PostDocs and professionals with a longer research history in one or more of the covered research fields. Moreover, it can be used to get an overview about one or more of the involved fields and to understand the interface and synergy effects between all of those fields. The book might even be used for teaching lectures in the fields of information visualization, visual analytics, and/or eye tracking.

The article explores theoretical and practical issues of developing skills in data visualization and visual analytics in informatics course (including examples of constructing analytical, tabular and visual models using diagrams).This study includes a review of bibliographic sources on the problems of developing visualization and visual analytics skills in the course of educational and scientific activities. It also includes development of a visualization and visual analysis competency structure. In the course of the study were applied methods of modeling (analytical and graphical models), methods of visualization and data systematization. In particular, such components of the competency of visualization and visual analysis as analytical, critical, abstract-logical, visual-figurative, spatial-figurative, associative, systemic, algorithmic were proposed and analyzed. The methods of forming this competency in the course of informatics were also considered.The article not only describes but also analyzes the problems, connected with formation of visualization and visual analysis competency. The study also presents methodological guidelines for the laboratory practical work, developed to teach the skills of graphic analytics in the informatics course, intended for students of the Faculty of Economics of the Bashkir State Agrarian University.The article is aimed at a wide range of specialists in the field of data analytics and visualization, including IT teachers, researchers, etc.

The focus of computer systems in the field of visual analytics is to make the results clear and understandable. However, enhancing human-computer interaction (HCI) in the field is less investigated. Data visualization and visual analytics (VA) are usually performed using traditional desktop settings and mouse interaction. These methods are based on the window, icon, menu, and pointer (WIMP) interface, which often results in information clutter and is difficult to analyze and understand, especially by novice users. Researchers believe that introducing adequate, natural interaction techniques to the field is necessary for building effective and enjoyable visual analytics systems. This work introduces a novel virtual reality (VR) module to perform basic visual analytics tasks and aims to explore new interaction techniques in the field. A pilot study was conducted to measure the time it takes students to perform basic tasks for analytics using the developed VR module and compares it to the time it takes them to perform the same tasks using a traditional desktop to assess the effectiveness of the VR module in enhancing student’s performance. The results show that novice users (Participants with less programming experience) took about 50% less time to complete tasks using the developed VR module as a comrade to a programming language, notably R. Experts (Participants with advanced programming experience) took about the same time to complete tasks under both conditions (R and VR).

In the current era of big data, a huge amount of data has been generated and collected from a wide variety of rich data sources. Embedded in these big data are useful information and valuable knowledge. An example is healthcare and epidemiological data such as data related to patients who suffered from epidemic diseases like the coronavirus disease 2019 (COVID-19). Knowledge discovered from these epidemiological data helps researchers, epidemiologists and policy makers to get a better understanding of the disease, which may inspire them to come up ways to detect, control and combat the disease. As “a picture is worth a thousand words”, having methods to visualize and visually analyze these big data makes it easily to comprehend the data and the discovered knowledge. In this paper, we present a big data visualization and visual analytics tool for visualizing and analyzing COVID-19 epidemiological data. The tool helps users to get a better understanding of information about the confirmed cases of COVID-19. Although this tool is designed for visualization and visual analytics of epidemiological data, it is applicable to visualization and visual analytics of big data from many other real-life applications and services.

Image and video data analysis has become an increasingly important research area with applications in different domains such as security surveillance, healthcare, augmented and virtual reality, video and image editing, activity analysis and recognition, synthetic content generation, distance education, telepresence, remote sensing, sports analytics, art, non-photorealistic rendering, search engines, and social media. Recent advances in Artificial Intelligence (AI) and particularly deep learning have sparked new research challenges and led to significant advancements, especially in image and video analysis. These advancements have also resulted in significant research and development in other areas such as visualization and visual analytics, and have created new opportunities for future lines of research. In this survey article, we present the current state of the art at the intersection of visualization and visual analytics, and image and video data analysis. We categorize the visualization articles included in our survey based on different taxonomies used in visualization and visual analytics research. We review these articles in terms of task requirements, tools, datasets, and application areas. We also discuss insights based on our survey results, trends and patterns, the current focus of visualization research, and opportunities for future research.

With the development of production technology and social needs, sectors of manufacturing are constantly improving. The use of sensors and computers has made it increasingly convenient to collect multimedia data in manufacturing. Targeted, rapid, and detailed analysis based on the type of multimedia data can make timely decisions at different stages of the entire manufacturing process. Visualization and visual analytics are frequently adopted in multimedia data analysis of manufacturing because of their powerful ability to understand, present, and analyze data intuitively and interactively. In this paper, we present a literature review of visualization and visual analytics specifically for manufacturing multimedia data. We classify existing research according to visualization techniques, interaction analysis methods, and application areas. We discuss the differences when visualization and visual analytics are applied to different types of multimedia data in the context of particular examples of manufacturing research projects. Finally, we summarize the existing challenges and prospective research directions.

The application such as video surveillance for traffic control in smart cities needs to analyze the large amount (hours/days) of video footage in order to locate the people who are violating the traffic rules. The traditional computer vision techniques are unable to analyze such a huge amount of visual data generated in real-time. So, there is a need for visual big data analytics which involves processing and analyzing large scale visual data such as images or videos to find semantic patterns that are useful for interpretation. In this paper, we propose a framework for visual big data analytics for automatic detection of bike-riders without helmet in city traffic. We also discuss challenges involved in visual big data analytics for traffic control in a city scale surveillance data and explore opportunities for future research.

As high volumes of wide variety of valuable data of different veracities can be easily generated or collected at high velocity nowadays, big data visualisation and visual analytics are in demand in various real-life applications. Musical data are examples of big data. Embedded in these big data are useful information and valuable knowledge. Many existing big data mining algorithms return useful information and valuable knowledge in textual or tabular forms. Knowing that a picture is worth thousand words, big data visualisation and visual analytics are also in demand. In this paper, we present system for visualising and analysing big data. In particular, our system focuses on the big data science task of the discovery and exploration of frequent patterns (i.e., collections of items that frequently occurring together) from musical data. Evaluation results show the applicability of our system in big data visualisation and visual analytics for music data mining.

Guest editorial: Special issue on visualization and visual analytics

Deep-learning-based visual data analytics for smart construction management

© 2014 by IGI Global. All rights reserved.Due to rapid advances in hardware and software technologies, network infrastructure and data have become increasingly complex, requiring efforts to more effectively comprehend and analyze network topologies and information systems. Innovative Approaches of Data Visualization and Visual Analytics evaluates the latest trends and developments in force-based data visualization techniques, addressing issues in the design, development, evaluation, and application of algorithms and network topologies. This book will assist professionals and researchers working in the fields of data analysis and information science, as well as students in computer science and computer engineering, in developing increasingly effective methods of knowledge creation, management, and preservation.

Many systems for exploratory and visual data analytics require platform-dependent software installation, coding skills, and analytical expertise. The rapid advances in data-acquisition, web-based information, and communication and computation technologies promoted the explosive growth of online services and tools implementing novel solutions for interactive data exploration and visualization. However, web-based solutions for visual analytics remain scattered and relatively problem-specific. This leads to per-case re-implementations of common components, system architectures, and user interfaces, rather than focusing on innovation and building sophisticated applications for visual analytics. In this paper, we present the Statistics Online Computational Resource Analytical Toolbox (SOCRAT), a dynamic, flexible, and extensible web-based visual analytics framework. The SOCRAT platform is designed and implemented using multi-level modularity and declarative specifications. This enables easy integration of a number of components for data management, analysis, and visualization. SOCRAT benefits from the diverse landscape of existing in-browser solutions by combining them with flexible template modules into a unique, powerful, and feature-rich visual analytics toolbox. The platform integrates a number of independently developed tools for data import, display, storage, interactive visualization, statistical analysis, and machine learning. Various use cases demonstrate the unique features of SOCRAT for visual and statistical analysis of heterogeneous types of data.

As online learning and the use of learning management systems grows rapidly, large amounts of educational data are made available for learning analytics. Data visualization plays an important role within learning analytics, as it generates complex analysis and data into a visual representation for easy comprehension by the user to support better decision-making among stakeholders (e.g. students, educators, administrators). The purpose of this paper is to review the existing studies to provide an overview of data visualization in learning analytics within the educational communities. First we analyze the use of data visualization and learning analytics purposes in the contexts of its stakeholders. Second, we examine the relationship between data visualization and learning analytics in the development of learning management systems and then we discuss the challenges found. A systematic literature review was conducted based on the research keywords from reputable databases and 62 journal articles published from 2011 to 2021 were identified. The results from this review suggest there is an indispensable synergetic relationship between data visualization and learning analytics for successful learning management systems. Also there is a lack of studies that consider data visualization and learning analytics from the technological theories perspective.

Understanding huge multimedia collections is a huge challenge. Given a set of hundreds of thousands or millions of images, how to to understand its contents and how to find the images that are relevant for the task at hand? Using a combination of automated methods, visualization, and interaction, known as visual analytics, is probably the only way to go, combining the strengths of man and machine. An overview is given of trends in data visualization and visual analytics is given, and examples of recent work in multimedia analytics are presented. Exploiting meta-data, using interaction with relatively simple visual representations, and alignment with the work flow of users are promising routes, but scalability and evaluation are still challenging.

Mining digitalisation have been receiving significant attention due to the utilisation of advanced technologies, such as IoT, automation, and sensing. However, maximising the potential value of collected data in the mining industry remains a challenge. Therefore, this paper aims to review timely concern topics to facilitate the fusion implementation in mining engineering. Specifically, this review covers recent popular topics, such as, data visualisation, data management, data analytics, data fusion, visual analytics, and mining digital twin construction. In this paper, we aim to draw a comprehensive picture about the fusion of data visualisation and analytics in the big data context, by examining the recent academic research related to these topics. Therefore, this paper reviews the visualisation domain by conventional classification, including scientific visualisation, information visualisation, and visual analytics, associated with the analysis of current digital twin development. Next, according to the challenges and issues related to visualisation development, this paper reviews the data management and data analytics domains as well. Incorporating with the fusion concept, machine learning-oriented fusion applications and potential scenarios in the mining industry have been discussed. In addition, based on the observation across various domains, this paper presents challenges and future potentials of data fusion in mining.