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

Combining Multiple View Components for Exploratory Visualization

The analysis of structured complex data, such as clustered graph based datasets, usually applies a variety of visual representation techniques and formats. The majority of currently available tools and approaches to exploratory visualization are built on integrated schemes for simultaneous displaying of multiple aspects of studying objects and processes. Usually, such schemes partition screen space that is composed of multiple views and adopt interaction patterns to focus on data-driven items. Widely known concepts as overview plus-detail and focus-plus-context are ambiguous in interpretation by means of technical terms. Therefore, their implementation by UI design practitioners need reviews and a classification of the basic approaches to visual composition of graphical representation modules. We propose a description of basic components of the view and focus and an overview of their multiple combinations

Towards Coordination-Intensive Visualization Software

Most coordination realizations in current visualization systems are ''last-minute'' ad-hoc and rely on the richness of the chosen implementation language. Moreover, very few visualization models implicitly consider coordination. If coordination is contemplated from the design point of view, it is usually only regarded as part of the communication protocol and is generally dealt with within that restricted domain. Coordinated multiple views are beneficial and a flexible model for coordination will ensure easy embedding of coordination in such exploratory environments. This paper compares different approaches to coordination in exploratory visualization (EV). We recognize the need for a coordination model and for that we formalize aspects of coordination in EV. Furthermore, our work draws on the findings of the interdisciplinary study of coordination by various researchers

Coordinating views for data visualisation and algorithmic profiling

A number of researchers have designed visualisation systems that consist of multiple components, through which data and interaction commands flow. Such multistage (hybrid) models can be used to reduce algorithmic complexity, and to open up intermediate stages of algorithms for inspection and steering. In this paper, we present work on aiding the developer and the user of such algorithms through the application of interactive visualisation techniques. We present a set of tools designed to profile the performance of other visualisation components, and provide further functionality for the exploration of high dimensional data sets. Case studies are provided, illustrating the application of the profiling modules to a number of data sets. Through this work we are exploring ways in which techniques traditionally used to prepare for visualisation runs, and to retrospectively analyse them, can find new uses within the context of a multi-component visualisation system

EXPLORATORY VISUALIZATION OF GRAPHS BASED ON COMMUNITY STRUCTURE

Communities, also called clusters or modules, are groups of nodes which probably share common properties and/or play similar roles within a graph. They widely exist in real networks such as biological, social, and information networks. Allowing users to interactively browse and explore the community structure, which is essential for understanding complex systems, is a challenging yet important research topic. My work has been focused on visualization approaches to exploring the community structure in graphs based on automatic community detection results. In this dissertation, we first report a formal user study that investigated the essen- tial influence factors, benefits, and constraints of a community based graph visual- ization system in a background application of seeking information from text corpora. A general evaluation methodology for exploratory visualization systems has been proposed and practiced. The evaluation methodology integrates detailed cognitive load analysis and users’ prior knowledge evaluation with quantitative and qualitative measures, so that in-depth insights can be gained. The study revealed that visual exploration based on the community structure benefits the understanding of real net- works. A literature review and a set of interviews were then conducted to learn tasks facing such graph exploration and the state-of-the-arts. This work led to commu- nity related graph visualization task taxonomy. Our examination of existing graph visualization systems revealed that a large number of community related graph visualization tasks are poorly supported in existing approaches. To bridge the gap, several novel visualization techniques are proposed. In these approaches, graph topology information is mapped to a multidimensional space where the relationships between the communities and the nodes can be explicitly explored. Several user studies and case studies have been conducted to demonstrate the usefulness of these systems in real-world applications

SigTools: An exploratory visualization tool for genomic signals

With the advancement of sequencing technologies, genomic data sets are constantly being expanded by high volumes of different data types. One recently introduced data type in genomic science is genomic signals, with genomic coordinates associated with a score or probability indicating some form of biological activity. An example of genomic signals isEpigenomicmarkswhich represent short-read coverage measurements over the genome, and are utilized to locate functional and nonfunctional elements in genome annotation studies. To understand and evaluate the results of such studies, one needs to explore and analyze the characteristics of the input data. Information visualization is an effective approach that leverages human visual ability in data analysis. Several visualization applications have been deployed for this purpose such as the UCSC genome browser, Deeptools, and Segtools. However, we believe there is room for improvement in terms of programming skills requirements and proposed visualizations. Sigtools is an R-based exploratory visualization package, designed to enable the users with limited programming experience to produce statistical plots of continuous genomic data. It consists of several statistical visualizations such as value distribution, correlation, and autocorrelation that provide insights regarding the behavior of a group of signals in larger regions – such as a chromosome or the whole genome – as well as visualizing them around a specific point or short region. To demonstrate Sigtools utilization, first, we visualize five histone modifications downloaded from Roadmap Epigenomics data portal and show that Sigtools accurately captures their characteristics. Then, we visualize five chromatin state features, probabilistic generated genome annotations, to display how sigtools can assist in the interpretation of new and unknown signals

Effects of candidate position on ballot papers: Exploratory visualization of voter choice in the London local council elections 2010

The relationship between candidates’ position on a ballot paper and vote rank is explored in the case of London local council elections. A design study uses information visualization techniques to identify patterns and generate hypotheses. Using clustered bar-charts and spatial treemaps, the effects of ballot ordering are shown. Visual evidence is presented to suggest that the order of placement of the names of candidates acts to bias voters towards those whose names are towards the top of the ballot paper. The findings of this research have significant implications for the design of ballot papers and the conduct of fair elections