Waltz - An exploratory visualization tool for volume data, using multiform abstract displays

Although, visualization is now widely used, misinterpretations still occur. There are three primary solutions intended to aid a user interpret data correctly. These are: displaying the data in different forms (Multiform visualization); simplifying (or abstracting) the structure of the viewed information; and linking objects and views together (allowing corresponding objects to be jointly manipulated and interrogated). These well-known visualization techniques, provide an emphasis towards the visualization display. We believe however that current visualization systems do not effectively utilise the display, for example, often placing it at the end of a long visualization process. Our visualization system, based on an adapted visualization model, allows a display method to be used throughout the visualization process, in which the user operates a 'Display (correlate) and Refine' visualization cycle. This display integration provides a useful exploration environment, where objects and Views may be directly manipulated; a set of 'portions of interest' can be selected to generate a specialized dataset. This may subsequently be further displayed, manipulated and filtered

Exploring Road Incident Data with Heat Maps

This research seeks to determine whether heat mapping is an effective technique for the visual exploration of road incident data. Four software prototypes, which adopted map, treemap and spatial treemap layouts, were developed using open source software. Whilst the visualization process described by Fry (2007) informed the development effort, the evaluation methodology was based on the Nested Process Model (Munzner, 2009). The results of two evaluation methods-a design study and the presentation and discussion of results with domain experts-confirm heat mapping's validity and provide requirements for further software development

EVM: Incorporating Model Checking into Exploratory Visual Analysis

Visual analytics (VA) tools support data exploration by helping analysts quickly and iteratively generate views of data which reveal interesting patterns. However, these tools seldom enable explicit checks of the resulting interpretations of data -- e.g., whether patterns can be accounted for by a model that implies a particular structure in the relationships between variables. We present EVM, a data exploration tool that enables users to express and check provisional interpretations of data in the form of statistical models. EVM integrates support for visualization-based model checks by rendering distributions of model predictions alongside user-generated views of data. In a user study with data scientists practicing in the private and public sector, we evaluate how model checks influence analysts' thinking during data exploration. Our analysis characterizes how participants use model checks to scrutinize expectations about data generating process and surfaces further opportunities to scaffold model exploration in VA tools

New visualization model for large scale biosignals analysis

Benefits of long-term monitoring have drawn considerable attention in healthcare. Since the acquired data provides an important source of information to clinicians and researchers, the choice for long-term monitoring studies has become frequent. However, long-term monitoring can result in massive datasets, which makes the analysis of the acquired biosignals a challenge. In this case, visualization, which is a key point in signal analysis, presents several limitations and the annotations handling in which some machine learning algorithms depend on, turn out to be a complex task. In order to overcome these problems a novel web-based application for biosignals visualization and annotation in a fast and user friendly way was developed. This was possible through the study and implementation of a visualization model. The main process of this model, the visualization process, comprised the constitution of the domain problem, the abstraction design, the development of a multilevel visualization and the study and choice of the visualization techniques that better communicate the information carried by the data. In a second process, the visual encoding variables were the study target. Finally, the improved interaction exploration techniques were implemented where the annotation handling stands out. Three case studies are presented and discussed and a usability study supports the reliability of the implemented work

Visual Analytics and Interactive Machine Learning for Human Brain Data

Indiana University-Purdue University Indianapolis (IUPUI)This study mainly focuses on applying visualization techniques on human brain data for data exploration, quality control, and hypothesis discovery. It mainly consists of two parts: multi-modal data visualization and interactive machine learning. For multi-modal data visualization, a major challenge is how to integrate structural, functional and connectivity data to form a comprehensive visual context. We develop a new integrated visualization solution for brain imaging data by combining scientific and information visualization techniques within the context of the same anatomic structure. For interactive machine learning, we propose a new visual analytics approach to interactive machine learning. In this approach, multi-dimensional data visualization techniques are employed to facilitate user interactions with the machine learning process. This allows dynamic user feedback in different forms, such as data selection, data labeling, and data correction, to enhance the efficiency of model building

Segment Streaming for Efficient Pipelined Televisualization

The importance of scientific visualization for both science and engineering endeavors has been well recognized. Televisualization becomes necessary because of the physical distribution of data, computation resources, and users involved in the visualization process. However, televisualization poses a number of challenges to the designers of communication protocols. A pipelined televisualization (PTV) model is proposed for efficient implementation of a class of visualization applications. Central to the proposed research is the development of a segment of streaming IPC (interprocess communication) mechanism in support of efficient pipelining across very high speed internetworks. This requires exploration of special issues arising from extending a pipeline across networks with errors and high latency, determination of alternative solutions, and evaluation of such solutions. The novel aspects of the proposed segment streaming mechanism include a two-level flow control method and an intelligent error control mechanism

RULEBENDER: INTEGRATED MODELING, SIMULATION, AND VISUALIZATION FOR RULE-BASED INTRACELLULAR BIOCHEMISTRY

Rule-based modeling (RBM) is a powerful and increasingly popular approach to modeling cell signaling networks. However, novel visual tools are needed in order to make RBM accessible to a broad range of users, to make specification of models less error prone, and to improve workflows. We introduce RuleBender, a novel visualization system for the integrated visualization, modeling and simulation of rule-based intracellular biochemistry. We present the user requirements, visual paradigms, algorithms and design decisions behind RuleBender, with emphasis on visual global/local model exploration and integrated execution of simulations. The support of RBM creation, debugging, and interactive visualization expedites the RBM learning process and reduces model construction time; while built-in model simulation and results with multiple linked views streamline the execution and analysis of newly created models and generated networks. RuleBender has been adopted as both an educational and a research tool and is available as a free open source tool at http://www.rulebender.org. A development cycle that includes close interaction with expert users allows RuleBender to better serve the needs of the systems biology community