Deriving a holistic cognitive fit model for an optimal visualization of data for management decisions

Research shows that managerial decision making is directly correlated to both, the swift availability, and subsequently the ease of interpretation of the relevant information. Visualizations are already widely used to transform raw data into a more understandable format and to compress the constantly growing amount of information produced. However, research in this area is highly fragmented and results are contradicting. This paper proposes a preliminary model based on an extensive literature review including top current research on cognition theory. Furthermore an early stage validation of this model by experimental research using structural equation modeling is presented. The authors are able to identify task complexity as one of the most important predicting variables for information perception of visual data, however, other influences are significant as well (data density, domain expertise, working memory capacity and subjective visual complexity

Tag clouds algorithm with the inclusion of personality traits

Tag clouds have emerged as the latest technique in information visualization using text analysis methods in a variety of situations to interpret unstructured data types. Literature review emphasizes that information visualization development techniques should include the personality traits of humans to provide effective and meaningful information. However, in the field of tag clouds, no published studies have investigated the role of personality traits to guide the design of tag cloud visualization. Furthermore, the algorithm to generate tag cloud visualization based on personality traits has not been explored. Therefore, the main objective of this study is to develop an algorithm that can adapt visual features of tag cloud layout styles based on personality traits of the user. This study focuses on two visual features associated with personality traits, which are colors and shapes. To achieve the aim of this study, Design Science methodology was used through three main phases: problem identification, design of solution, and evaluation. The algorithm was developed based on three theories of personality traits, namely Myers-Briggs Type Indicator (MBTI), Shape, and Multiple Intelligence (MI). The algorithm was then tested through a black box testing. In addition, a prototype was developed to evaluate the proposed algorithm. Then, user satisfaction was conducted in order to evaluate this prototype using Q-SAFI instruments. Notable findings suggest that users are highly satisfied with colors and shapes of tag cloud as well as the overall tag cloud layout styles. The main contribution of this research is the tag cloud layout styles algorithm, which combines the concept of personality traits and characteristics of colors and shapes. This algorithm is beneficial for decision making using information visualization in which personality traits of the user are heavily inclined. Moreover, the tag cloud user’s satisfaction instrument, Q-SAFI, provides measurements for evaluating tag cloud visualization

Improving Information Perception of Graphical Displays – an Experimental Study on the Display of Column Graphs

Due to the fact that the quality of decisions is linked to the availability of information and to the ability of the human brain to process this in an effective and efficient way, its selection and representation are of major importance in business communication. Graphs and tables are widely used to transform raw data into a more understandable format, but there are not any empirically tested guidelines that consider the cognition and perception abilities of humans. This paper therefore explores how specific visual designs applied to column graphs influence effectiveness and efficiency by applying the technique of eye-tracking to make an accurate assessment of what the observer is looking at. The tested design elements show significant results and allow the deduction of the following design guidelines for column graphs: do not use a 3D view for depicting two dimensional data, do not use non-zero or broken axes, do show label values, do not use horizontal gridlines or the label axis when showing label values and do align the label values depending on the available space (either horizontally or vertically)

Inferring Intent from Interaction with Visualization

Today\u27s state-of-the-art analysis tools combine the human visual system and domain knowledge, with the machine\u27s computational power. The human performs the reasoning, deduction, hypothesis generation, and judgment. The entire burden of learning from the data usually rests squarely on the human user\u27s shoulders. This model, while successful in simple scenarios, is neither scalable nor generalizable. In this thesis, we propose a system that integrates advancements from artificial intelligence within a visualization system to detect the user\u27s goals. At a high level, we use hidden unobservable states to represent goals/intentions of users. We automatically infer these goals from passive observations of the user\u27s actions (e.g., mouse clicks), thereby allowing accurate predictions of future clicks. We evaluate this technique with a crime map and demonstrate that, depending on the type of task, users\u27 clicks appear in our prediction set 79\% -- 97\% of the time. Further analysis shows that we can achieve high prediction accuracy after only a short period (typically after three clicks). Altogether, we show that passive observations of interaction data can reveal valuable information about users\u27 high-level goals, laying the foundation for next-generation visual analytics systems that can automatically learn users\u27 intentions and support the analysis process proactively

Survey on Individual Differences in Visualization

Developments in data visualization research have enabled visualization systems to achieve great general usability and application across a variety of domains. These advancements have improved not only people's understanding of data, but also the general understanding of people themselves, and how they interact with visualization systems. In particular, researchers have gradually come to recognize the deficiency of having one-size-fits-all visualization interfaces, as well as the significance of individual differences in the use of data visualization systems. Unfortunately, the absence of comprehensive surveys of the existing literature impedes the development of this research. In this paper, we review the research perspectives, as well as the personality traits and cognitive abilities, visualizations, tasks, and measures investigated in the existing literature. We aim to provide a detailed summary of existing scholarship, produce evidence-based reviews, and spur future inquiry

Vertical Search Behavior and Preference of Users with Different Visual Memory and Perceptual Speed Abilities

Vertical search in Information Retrieval (IR) represents display opportunities for searcher interaction in the form of blended and non-blended results. Search behavior and preference in interacting with these results can be influenced by both design and personal, cognitive abilities. This study evaluates the relationship between cognitive ability and vertical search behavior and preference. In this lab study cognitive tests measuring perceptual speed and visual memory were administered to sixteen participants who subsequently performed four search tasks on two search engines, one with a blended display and one with a non-blended display. Cognitive tests, search logs and participant questionnaires were used to evaluate vertical search behavior and preference in cognitively high and low performers. The findings suggest that cognitive ability influences vertical search engagement and preference. The value in this research is its ability to contribute to issues of result merging, display, and interaction at a personal level in vertical search.Master of Science in Information Scienc

Unterstützung adaptiver Benutzungsschnittstellen mittels Eye-Tracking zur Erkennung von Expertise oder Verstehen

Studien zeigen, dass Erledigen von Aufgaben am Computer von der Wahrnehmungsfähigkeit des Anwenders abhängt. Die Kommunikation zwischen Anwender und Computersystemen erfordert hohe Anforderungen an die Benutzerschnittstelle, die für eine Interaktion zwischen Benutzer und Software verantwortlich ist. Eine adaptive Benutzerschnittstelle vereinfacht und verbessert die Interaktionsmöglichkeit und passt sich automatisch an die Bedürfnisse und Fähigkeiten des Anwenders. Ein wichtiger Schritt zur Realisierung von adaptiven Systeme, ist die automatische Erkennung der Benutzerfähigkeiten, um eine Anpassung der Benutzungsschnittstelle an den Benutzer vornehmen zu können. Das Ziel dieser Bachelorarbeit ist es, festzustellen, ob bzw. wie sich die Analyse der Augenbewegung (Eye-Tracking) dazu eignet, die Fähigkeiten des Anwenders bezüglich Verständnis und Expertise anhand des jeweiligen Blickverhaltens zu erkennen, um diese Information für eine adaptive Benutzungsschnittstelle verwenden zu können. In dieser Arbeit werden Experimente zur Erkennung von Benutzerfähigkeiten anhand der Blickdaten analysiert und Erkenntnisse für eine adaptive Benutzerschnittstelle ermittelt. Die Ergebnisse der Studien zeigen, dass keine Unterschiede zwischen Benutzern bezüglich der Augenbewegungsdaten erkannt werden.Studies showed that completing a task with a computer depends on the perception of the user. The communication between user and computer systems requires high demands to the user interface, which is responsible for interaction between users and software. An adaptive user interface simplifies and improves the interactions and automatically adapts to the needs and abilities of the user. An important step towards the realization of such adaptive systems is the automatic recognition of the user skills to adapt the user interface to the user. The aim of this thesis is to determine whether and how the analysis of eye movements (Eye-Tracking) can be used, to recognize the skills of the user with respect to comprehension and expertise based on the respective eye gaze to use this information for an adaptive user interface. In this work experiments for the detection of user skills based on the gaze data are analyzed and findings for an adaptive user interface are determined. The results of the studies show, that there are no differences between users with respect to the eye movement data