An Evaluation Framework for Business Intelligence Visualization

Nowadays, data visualization is becoming an essential part of data analysis. Business Intelligence Visualization (BIV) is a powerful tool that helps modern business flows faster and smoother than ever before. However, studies on BIV evaluation are severely lacking; most evaluation studies for BIV is guided by general principles of usability, which have limited aspects covered for customers? needs. The purpose of this research is to develop a framework that evaluates BIV, including decision-making experience. First, we did a literature review for good understanding of research progress on related fields, and established a conceptual framework. Second, we performed a user study that implemented this framework with a set of questionnaires to demonstrate how our framework can be used in real business. Our result proved that this framework can catch differences among different designs of BIV from the users? standpoints. This can help design BIV and promote better decision-makings on business affairs

Attacking Information Visualization System Usability Overloading and Deceiving the Human

Information visualization is an effective way to easily comprehend large amounts of data. For such systems to be truly effective, the information visualization designer must be aware of the ways in which their system may be manipulated and protect their users from attack. In addition, users should be aware of potential attacks in order to minimize or negate their effect. These attacks target the information visualization system as well as the perceptual, cognitive and motor capabilities of human end users. To identify and help counter these attacks we present a framework for information visualization system security analysis, a taxonomy of visualization attacks and technology independent principles for countering malicious visualizations. These themes are illustrated with case studies and working examples from the network security visualization domain, but are widely applicable to virtually any information visualization system

Suspicion Modeling in Support of Cyber-Influence Operations/Tactics

Understanding the cognitive process of IT user suspicion may assist organizations in development of network protection plans, personnel training, and tools necessary to identify and mitigate nefarious intrusions of IT systems. Exploration of a conceptual common ground between psycho-social and technology-related concepts of suspicion are the heart of this investigation. The complexities involved in merging these perspectives led to the overall research question: What is the nature of user suspicion toward IT: The research problem/phenomenon was addressed via extensive literature review, and use of the Interactive Qualitative Analysis problem/phenomenon. Analysis of the system led to the development of a model of IT suspicion as a progenitor for future experimental constructs that measure or assess behavior as a result of cyber attacks

Perception in statistical graphics

There has been quite a bit of research on statistical graphics and visualization, generally focused on new types of graphics, new software to create graphics, interactivity, and usability studies. Our ability to interpret and use statistical graphics hinges on the interface between the graph itself and the brain that perceives and interprets it, and there is substantially less research on the interplay between graph, eye, brain, and mind than is sufficient to understand the nature of these relationships. The goal of the work presented here is to further explore the interplay between a static graph, the translation of that graph from paper to mental representation (the journey from eye to brain), and the mental processes that operate on that graph once it is transferred into memory (mind). Understanding the perception of statistical graphics should allow researchers to create more effective graphs which produce fewer distortions and viewer errors while reducing the cognitive load necessary to understand the information presented in the graph. Taken together, these experiments should lay a foundation for exploring the perception of statistical graphics. There has been considerable research into the accuracy of numerical judgments viewers make from graphs, and these studies are useful, but it is more effective to understand how errors in these judgments occur so that the root cause of the error can be addressed directly. Understanding how visual reasoning relates to the ability to make judgments from graphs allows us to tailor graphics to particular target audiences. In addition, understanding the hierarchy of salient features in statistical graphics allows us to clearly communicate the important message from data or statistical models by constructing graphics which are designed specifically for the perceptual system

Cognitive Foundations for Visual Analytics

In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions