Supporting the Analytical Reasoning Process in Maritime Anomaly Detection: Evaluation and Experimental Design

Bibliographic data such as collections of scientific articles and citation networks have been studied extensively in information visualization and visual analytics research. Powerful systems have been built to support various types of bibliographic analysis, but they require some training and cannot be used to disseminate the insights gained. In contrast, we focused on developing a more accessible visual analytics system, called SurVis, that is ready to disseminate a carefully surveyed literature collection. The authors of a survey may use our Web-based system to structure and analyze their literature database. Later, readers of the survey can obtain an overview, quickly retrieve specific publications, and reproduce or extend the original bibliographic analysis. Our system employs a set of selectors that enable users to filter and browse the literature collection as well as to control interactive visualizations. The versatile selector concept includes selectors for textual search, filtering by keywords and meta-information, selection and clustering of similar publications, and following citation links. Agreement to the selector is represented by word-sized sparkline visualizations seamlessly integrated into the user interface. Based on an analysis of the analytical reasoning process, we derived requirements for the system. We developed the system in a formative way involving other researchers writing literature surveys. A questionnaire study with 14 visual analytics experts confirms that SurVis meets the initially formulated requirements.

Analytical Reasoning is the foundation of visual analytics, assisted via interactive and dynamic visualization representation. The main concern of visual analytics is the analytics process itself, it is important to facilitate the human mental space during the analysis process by embedding the analytical reasoning in the visual analytics representation. This paper aims to introduce and describe the essential analytical reasoning features within visual analytics representation. The framework describes analytical reasoning features from three parts of visual analytics representation which are higher-level structure, interconnection and lower-level structure. For higher-level structure, we proposed the features of big picture, analytics goal and insights through storytelling to ensure the analytics output becomes knowledge and applicable to facilitate the business decision. For interconnection, the features of trend, pattern and relevancy induce a relationship between higher and lower-level structures. Finally, analytical reasoning features for lower-level structure are quite straightforward which are benchmarking, ranking, decluttering, clueing and filtering. It is hoped that this framework could help to shed some light in terms of understanding analytical reasoning features that can facilitate the business decision.

This special issue is devoted to the new research addressing challenges in the areas of visualization and visual analytics. Visualization and visual analytics are closely related research areas, both concentrating on developing visual techniques to reveal meaningful information out of various data in real-life applications. Visualization as a field has its roots in Computer Graphics and has become a popular research area over the years. The field of visual analytics is relatively young with a concentration on analytical reasoning facilitated by interactive visual interfaces. In general, visualization and visual analytics research is tightly connected with certain types of data or applications and researchers in both fields strive to discover known or unknown data patterns for domain users.

The 21st century will be heavily impacted by the capability to create value from recorded data of all kinds. In this regard, industrial manufacturing intuitions increasingly rely on new data-driven technologies, such as the Internet of Things or Machine Learning. In terms of data collection, manufacturing processes increasingly include sophisticated sensor equipment, which results in interconnected networks of manufacturing all assembling parts and producing data. However, manufacturing institutions currently face two challenges. First, large amounts of parts and hence data are produced during fully automated manufacturing processes. Second, due to the overwhelming amount of recorded data, it is particularly challenging to efficiently analyze manufacturing data. Hence, it is important to efficiently store and share gained knowledge from performed data analyses. Information visualization and Visual Analytics are two prolific branches of data analysis exploiting sophisticated visualization techniques to support the execution of analytical tasks and to store gained knowledge. This thesis looks at how data visualization approaches can help industrial manufacturing organizations to create value from large amounts of manufacturing data, as well as how to efficiently store and share knowledge from manufacturing data analyses. The goal is to understand how Visual Analytics can improve manufacturing processes in the context of knowledge management. Five Visual Analytics systems were designed, developed, and evaluated to tackle different domain problems that emerged from manufacturing setups. Findings from each system were used to carry out additional studies to enhance established theories of knowledge management. As a result, five success stories are provided of how Visual Analytics can significantly improve manufacturing processes and how knowledge can be efficiently created, formalized, and shared in an organization with Visual Analytics.

Highlighting was the basic viewing control mechanism in computer graphics and visualization to guide users' attention in reading diagrams, images, graphs and digital texts. As the rapid growth of theory and practice in information visualization, highlighting has extended its role that acts as not only a viewing control, but also an interaction control and a graphic recommendation mechanism in knowledge visualization and visual analytics. In this work, we attempt to give a formal summarization and classification of the existing highlighting methods and techniques that can be applied in Information Visualization, Visual Analytics and Knowledge Visualization. We propose a new three-layer model of highlighting. We discuss the responsibilities of each layer in the different stage of the visual information processing.

Dr. Yingjie Chen is an assistant professor in the Department of Computer Graphics Technology of Purdue University. He received his Ph.D. degree in the areas of human-computer interaction, information visualization, and visual analytics from the School of Interaction Arts and Technology at Simon Fraser University (SFU) in Canada. He earned the Bachelor degree of Engineering from the Tsinghua University in China, and a Master of Science degree in Information Technology from SFU. His research covers interdisciplinary domains of information visualization, visual analytics, digital media, and human computer interaction. He seeks to design, model, and construct new forms of interaction in visualization and system design, by which the system can minimize its influence on design and analysis, and become a true free extension of human’s brain and hand.

Cyber Security Operations Centers (CSOCs) have been widely built in organizations for intrusion detection and incident response. Cyber security analysts play a critical role in CSOCs as they continuously monitor network activities to achieve cyber situational awareness. These analysts are tasked with poring over massive network monitoring data collected by various cyber defense technologies, such as Intrusion Detection System (IDS) alerts and firewall logs. To achieve awareness of the potential attacks and to have an effective real-time response, cyber security analysts need to work in teams and must be able to quickly exchange their findings and divide tasks among each other. Currently the information sharing and task division are done inefficiently which negatively impacts the response and decision-making time. To this end, we propose a collaborative analysis support system that can record, represent and reveal the analytical reasoning process of analysts. The system consists of tracing modules, a trace parser and an interactive visual map, named “AOH-Map”. This map integrates all the captured traces of analysts’ analytical reasoning process, and analysts can directly interact with the AOH-Map to review past analysis, share their findings and divide tasks in a timely manner. The system has been implemented and evaluated in a two-phase human subject experiment. The results show that the proposed system is effective in supporting collaboration among cyber analysts in terms of information sharing and task division.

Collaborative visual analytics (CVA) involves sensemaking activities within teams of analysts based on coordination of work across team members, awareness of team activity, and communication of hypotheses, observations, and insights. We introduce a new type of CVA tools based on the notion of “team-first” visual analytics, where supporting the analytical process and needs of the entire team is the primary focus of the graphical user interface before that of the individual analysts. To this end, we present the design space and guidelines for team-first tools in terms of conveying analyst presence, focus, and activity within the interface. We then introduce InsightsDrive, a CVA tool for multidimensional data, that contains team-first features into the interface through group activity visualizations. This includes (1) in-situ representations that show the focus regions of all users integrated in the data visualizations themselves using color-coded selection shadows, as well as (2) ex-situ representations showing the data coverage of each analyst using multidimensional visual representations. We conducted two user studies, one with individual analysts to identify the affordances of different visual representations to inform data coverage, and the other to evaluate the performance of our team-first design with exsitu and in-situ awareness for visual analytic tasks. Our results give an understanding of the performance of our team-first features and unravel their advantages for team coordination.

Information visualization as a field is growing rapidly in popularity since the first information visualization conference in 1995. However, as a consequence of its growth, it is increasingly difficult to follow the growing body of literature within the field. Survey papers and literature reviews are valuable tools for managing the great volume of previously published research papers, and the quantity of survey papers in visualization has reached a critical mass. To this end, this survey paper takes a quantum step forward by surveying and classifying literature survey papers in order to help researchers understand the current landscape of Information Visualization. It is, to our knowledge, the first survey of survey papers (SoS) in Information Visualization. This paper classifies survey papers into natural topic clusters which enables readers to find relevant literature and develops the first classification of classifications. The paper also enables researchers to identify both mature and less developed research directions as well as identify future directions. It is a valuable resource for both newcomers and experienced researchers in and outside the field of Information Visualization and Visual Analytics.

Visual analysis and data delivery in the form of visualizations are of great importance in health care, as such forms of presentation can reduce errors and improve care and can also help provide new insights into long-term disease progression. Information visualization and visual analytics also address the complexity of long-term, time-oriented patient data by reducing inherent complexity and facilitating a focus on underlying and hidden patterns. This review aims to provide an overview of visualization techniques for time-oriented data in health care, supporting the comparison of patients. We systematically collected literature and report on the visualization techniques supporting the comparison of time-based data sets of single patients with those of multiple patients or their cohorts and summarized the use of these techniques. This scoping review used the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. After all collected articles were screened by 16 reviewers according to the criteria, 6 reviewers extracted the set of variables under investigation. The characteristics of these variables were based on existing taxonomies or identified through open coding. Of the 249 screened articles, we identified 22 (8.8%) that fit all criteria and reviewed them in depth. We collected and synthesized findings from these articles for medical aspects such as medical context, medical objective, and medical data type, as well as for the core investigated aspects of visualization techniques, interaction techniques, and supported tasks. The extracted articles were published between 2003 and 2019 and were mostly situated in clinical research. These systems used a wide range of visualization techniques, most frequently showing changes over time. Timelines and temporal line charts occurred 8 times each, followed by histograms with 7 occurrences and scatterplots with 5 occurrences. We report on the findings quantitatively through visual summarization, as well as qualitatively. The articles under review in general mitigated complexity through visualization and supported diverse medical objectives. We identified 3 distinct patient entities: single patients, multiple patients, and cohorts. Cohorts were typically visualized in condensed form, either through prior data aggregation or through visual summarization, whereas visualization of individual patients often contained finer details. All the systems provided mechanisms for viewing and comparing patient data. However, explicitly comparing a single patient with multiple patients or a cohort was supported only by a few systems. These systems mainly use basic visualization techniques, with some using novel visualizations tailored to a specific task. Overall, we found the visual comparison of measurements between single and multiple patients or cohorts to be underdeveloped, and we argue for further research in a systematic review, as well as the usefulness of a design space.

Crowdsourcing-based user studies have become increasingly popular in information visualization (InfoVis) and visual analytics (VA). However, it is still unclear how to deal with some undesired crowdsourcing workers, especially those who submit random responses simply to gain wages (random clickers, henceforth). In order to mitigate the impacts of random clickers, several studies simply exclude outliers, but this approach has a potential risk of losing data from participants whose performances are extreme even though they participated faithfully. In this paper, we evaluated the degree of randomness in responses from a crowdsourcing worker to infer whether the worker is a random clicker. Thus, we could reliably filter out random clickers and found that resulting data from crowdsourcing-based user studies were comparable with those of a controlled lab study. We also tested three representative reward schemes (piece-rate, quota, and punishment schemes) with four different levels of compensations ($0.00, $0.20, $1.00, and $4.00) on a crowdsourcing platform with a total of 1,500 crowdsourcing workers to investigate the influences that different payment conditions have on the number of random clickers. The results show that higher compensations decrease the proportion of random clickers, but such increase in participation quality cannot justify the associated additional costs. A detailed discussion on how to optimize the payment scheme and amount to obtain high-quality data economically is provided.

IntroductionThis book is intended to give an overview of important issues concerning Human-Computer Interaction and information visualization. It is based on an IFIP [4] workshop that took place during the Interact’09 conference in Uppsala [3]. This workshop, co-organized by IFIP WG TC13.7 on “HCI and Visualization” [2] and the European VisMaster Coordinated Action [5], surveyed and expanded our understanding of the cognitive and perceptual issues of interactive visualization and visual analytics and brought together researchers interested in these issues. It outlined the research required to understand what aspects of analysis match human capabilities most closely, and how interactive visual support should be designed and adapted to make optimal use of human capabilities in terms of information perception and processing.

The Information Visualization community has begun to pay attention to visualization literacy; however, researchers still lack instruments for measuring the visualization literacy of users. In order to address this gap, we systematically developed a visualization literacy assessment test (VLAT), especially for non-expert users in data visualization, by following the established procedure of test development in Psychological and Educational Measurement: (1) Test Blueprint Construction, (2) Test Item Generation, (3) Content Validity Evaluation, (4) Test Tryout and Item Analysis, (5) Test Item Selection, and (6) Reliability Evaluation. The VLAT consists of 12 data visualizations and 53 multiple-choice test items that cover eight data visualization tasks. The test items in the VLAT were evaluated with respect to their essentialness by five domain experts in Information Visualization and Visual Analytics (average content validity ratio = 0.66). The VLAT was also tried out on a sample of 191 test takers and showed high reliability (reliability coefficient omega = 0.76). In addition, we demonstrated the relationship between users' visualization literacy and aptitude for learning an unfamiliar visualization and showed that they had a fairly high positive relationship (correlation coefficient = 0.64). Finally, we discuss evidence for the validity of the VLAT and potential research areas that are related to the instrument.

This book discusses semantic interaction, a user interaction methodology for visual analytic applications that more closely couples the visual reasoning processes of people with the computation. This methodology affords user interaction on visual data representations that are native to the domain of the data. User interaction in visual analytics systems is critical to enabling visual data exploration. Interaction transforms people from mere viewers to active participants in the process of analyzing and understanding data. This discourse between people and data enables people to understand aspects of their data, such as structure, patterns, trends, outliers, and other properties that ultimately result in insight. Through interacting with visualizations, users engage in sensemaking, a process of developing and understanding relationships within datasets through foraging and synthesis. The book provides a description of the principles of semantic interaction, providing design guidelines for the integration of semantic interaction into visual analytics, examples of existing technologies that leverage semantic interaction, and a discussion of how to evaluate these technologies. Semantic interaction has the potential to increase the effectiveness of visual analytic technologies and opens possibilities for a fundamentally new design space for user interaction in visual analytics systems.

The visual analytics community has long aimed to understand users better and assist them in their analytic endeavors. As a result, numerous conceptual models of visual analytics aim to formalize common workflows, techniques, and goals leveraged by analysts. While many of the existing approaches are rich in detail, they each are specific to a particular aspect of the visual analytic process. Furthermore, with an ever‐expanding array of novel artificial intelligence techniques and advances in visual analytic settings, existing conceptual models may not provide enough expressivity to bridge the two fields. In this work, we propose an agent‐based conceptual model for the visual analytic process by drawing parallels from the artificial intelligence literature. We present three examples from the visual analytics literature as case studies and examine them in detail using our framework. Our simple yet robust framework unifies the visual analytic pipeline to enable researchers and practitioners to reason about scenarios that are becoming increasingly prominent in the field, namely mixed‐initiative, guided, and collaborative analysis. Furthermore, it will allow us to characterize analysts, visual analytic settings, and guidance from the lenses of human agents, environments, and artificial agents, respectively.