Briteback Explore is a service provided by the company Briteback. The service provides a survey tool to its users that allows access to results through the downloading of a comma-separated values (CSV) -file. What was built was a visualization model that provides the survey tool’s users with a graphical representation of the data directly through the service. Providing an overview of the survey results that lessens the amount of information overload perceived by the user while giving the data a structure. A survey based off of the System Usability Scale (SUS) evaluation method was then performed to display to what degree users found the new feature usable. Results showed a satisfactory high score on the SUS adjective rating scale.

Visual analytics of large amounts of spatio-temporal data is challenging due to the overlap and clutter from movements of multiple objects. A common approach for analyzing such data is to consider how groups of items cluster and move together in space and time. However, most methods for showing Spatio-temporal Cluster (STC) properties, concentrate on a few dimensions of the cluster (e.g. the cluster movement direction or cluster density) and many other properties are not represented. Furthermore, while representing multiple attributes of clusters in a single view existing methods fail to preserve the original shape of the cluster or distort the actual spatial covering of the dataset. In this thesis, I propose a simple yet effective visualization, FlockViz, for showing multiple STC data dimensions in a single view by preserving the original cluster shape. To evaluate this method I develop a framework for categorizing the wide range of tasks involved in analyzing STCs. I conclude this work through a controlled user study comparing the performance of FlockViz with alternative visualization techniques that aid with cluster-based analytic tasks. Finally the exploration capability of FlockViz is demonstrated in some real life data sets such as fish movement, caribou movement, eagle migration, and hurricane movement. The results of the user studies and use cases confirm the advantage and novelty of the novel FlockViz design for visual analytic tasks.

Les réseaux ferroviaires en milieu dense font face à des saturations importantes. Et l'adéquation entre l'offre théorique et la demande croissante impose des contraintes d'exploitabilités fortes. Un déséquilibre générera des points conflictuels comme des goulets d'étranglement avec pour effet des retards sur les trains amonts. Comme le facteur humain, parmi une multitude, influence l'exploitation ; le prendre en compte plus finement devrait améliorer la compréhension et la modélisation des lignes pour en accroître la capacité sans sacrifier le confort des passagers. Pour répondre à cet objectif, nos travaux reposent sur une visualisation adaptée des données remontées de l'exploitation et sur leur fouille automatisée. Elles ont été adaptées et appliquées au domaine ferroviaire notamment aux lignes des réseaux ferrés exploités par la RATP. Le processus « Visual Analytics », mis en œuvre dans nos travaux pour répondre à ces besoins, englobe les étapes nécessaires à la valorisation de la donnée, allant de leur préparation à l’analyse experte en passant par leur représentation graphique et par l’utilisation d'algorithmes de fouille de données. Parmi ces derniers, le CorEx et le Sieve nous ont permis par un apprentissage non supervisé basé sur une mesure de l'information mutuelle multivariée d'analyser les données d'exploitation pour en extraire des caractéristiques du comportement humain. Enfin, nous proposons aussi une visualisation intuitive d'une grande quantité de données permettant leur intégration et facilitant le diagnostic global du comportement des lignes ferroviaires. / Dense railway networks face significant saturation. And the balance between the theoretical offer and the growing demand imposes strong operability constraints. An imbalance will generate conflicting points such as bottlenecks with the effect of delays on the following trains. As the human factor influences the operation performance; taking it into account more accurately should improve understanding and modeling of railway lines to increase capacity without reducing passenger comfort. To fulfill this objective, we are working on an adapted visualization of the operating data and on their automated mining. These two solutions have been adapted and applied to the railway sector, particularly to the lines of rail networks operated by RATP. The "Visual Analytics" process, implemented in our work to meet these needs, encompasses the steps required to value the data, going from the preparation of the data to the expert analysis. This expert analysis is made through graphic representation and the use of data mining algorithms. Among these data mining algorithms, CorEx and Sieve allowed us to analyze operating data and then extract characteristics human behavior thanks to unsupervised learning based on a multivariate mutual information measure to. Finally, we propose an intuitive visualization of a large amount of data allowing their global integration and facilitating the overall diagnosis of the railway lines behavior.

Organizations can reduce carbon emissions by collecting data on what their emissions are and replace activities that are emission dense. To aid exploration, emission data can be visualized using a data visualization tool. A basic principle of designing a data visualization tool is the Visual Information Seeking Mantra (VISM): Overview first, zoom and filter, then details-on-demand. The company Measure & Change, which created a tool automating the retrieval and calculation of an organization’s emission data, has also designed a visualization tool for exploring such data. However, this tool was not created with the VISM in mind. This thesis discusses and evaluate (1) How can the data visualization tool be optimized using the design principle of Overview first, zoom and filter, details-on-demand? and (2) What is the impact of re-designing the data visualization tool in such a way? Through analysis of the dataset, user tasks, and the Visual Information Seeking Mantra, a new prototype was created. Both prototypes were then evaluated in A/B tests, with thinking aloud, a semi-structured interview, and the standardized ICE-T survey. The results suggest that the changes improved the tool, and that the user value increased. The ICE-T scores put the old implementation at 4.7, and the new prototype at 6. Visualizations scoring 5 or higher are generally accepted as valuable.

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International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / Recent years have seen a sharp rise in the size of satellite constellations. The monitoring and analysis tools in use today, however, were developed for smaller constellations and are ill-equipped to handle the increased volume of telemetry data. A new technology that can accommodate vast quantities of data is 3-D visualization. Data is abstracted to show the degree to which it deviates from normal, allowing an analyst to absorb the status of thousands of parameters in a single glance. Trend alarms notify the user of dangerous trends before data exceeds normal limits. Used appropriately, 3-D visualization can extend the life of a satellite by ten to twenty percent.

We are entering an era of business intelligence and big data where simple tables and other traditional means of data display cannot deal with the vast amounts of data required to meet the decision-making needs of businesses and their clients. Graphical figures constructed with modern visualization software can convey more information than a table because there is a limit to the table size that is visually usable. Contemporary decision performance is influenced by the task domain, the user experience, and the visualizations themselves. Utilizing data visualization in task performance to aid in decision making is a complex process. We develop and test a decision-making framework to examine task performance in a visual and non-visual aided decision-making by using three experiments to test this framework. Studies 1 and 2 investigate DV formats and how complexity and design affects the proposed visual decision making framework. The studies also examine how DV formats affect task performance, as measured by accuracy and timeliness, and format preference. Additionally, these studies examine how DV formats influence the constructs in the proposed decision making framework which include information usefulness, decision confidence, cognitive load, visual aesthetics, information seeking intention, and emotion. Preliminary findings indicate that graphical DV allows individuals to respond faster and more accurately, resulting in improved task fit and performance. Anticipated implications of this research are as follows. Visualizations are independent of the size of the data set but can be increasingly complex as the data complexity increases. Furthermore, well designed visualizations let you see through the complexity and simultaneously mine the complexity with drill down technologies such as OLAP.

Proximity scaling methods (e.g., multidimensional scaling) represent objects in a low dimensional configuration so that fitted distances between objects optimally approximate multivariate proximities. Next to finding the optimal configuration the goal is often also to assess groups of objects from the configuration. This can be difficult if the optimal configuration lacks clusteredness (coined c-clusteredness). We present Cluster Optimized Proximity Scaling (COPS), which attempts to solve this problem by finding a configuration that exhibts c-clusteredness. In COPS, a flexible scaling loss function (p-stress) is combined with an index that quantifies c-clusteredness in the solution, the OPTICS Cordillera. We present two variants of combining p-stress and Cordillera, one for finding the configuration directly and one for metaparameter selection for p-stress. The first variant is illustrated by scaling Californian counties with respect to climate change related natural hazards. We identify groups of counties with similar risk profiles and find that counties that are in high risk of drought are socially vulnerable. The second variant is illustrated by finding a clustered nonlinear representation of countries according to their history of banking crises from 1800 to 2010. (authors' abstract) / Series: Discussion Paper Series / Center for Empirical Research Methods

The overall goal of the thesis is to explore new approaches to debugging managed code, namely visualization of data in the program being debugged. Particular goals of the work are: (a) to build an object graph visualizer, which displays selected data structure used in the program as directed graph, (b) improve visualization of object collections by providing an overview of collection contents and supporting broad range of collection types. The work is implemented for the SharpDevelop open source IDE for .NET. The author cooperates with the SharpDevelop team and the results of the work have been already incorporated into the new version of the IDE.

Analyzing modern day's information systems that produce humongous multi-dimensional data in form of logs, traces or events that unfold over time can be tedious without adequate visualization, thereby, advocating the need for an intelligible visualization. This thesis researched and developed a visualization framework that represents multi-dimensional dynamic and temporal process data in a potentially intelligible and actionable form. A prototype showing four different views using notional malware data abstracted from Normal Sandbox behavioral traces were developed. In particular, the B-matrix view representing the DLL files used by the malware to attack a system. This representation is aimed at visualizing large data sets without losing emphasis on the process unfolding over multiple dimensions.