Electronic games are starting to incorporate in-game telemetry that collects data about player, team, and community performance on a massive scale, and as data begins to accumulate, so does the demand for effectively analyzing this data. In this paper, we use examples from both old and new games of different genres to explore the theory and design space of visualization for games. Drawing on these examples, we define a design space for this novel research topic and use it to formulate design patterns for how to best apply visualization technology to games. We then discuss the implications that this new framework will potentially have on the design and development of game and visualization technology in the future.

This paper presents a 2D flow visualization user study that we conducted using new methodologies to increase the objectiveness. We evaluated grid-based variable-size arrows, evenly spaced streamlines, and line integral convolution (LIC) variants (basic, oriented, and enhanced versions) coupled with a colorwheel and/or rainbow color map, which are representative of many geometry-based and texture-based techniques. To reduce data-related bias, template-based explicit flow synthesis was used to create a wide variety of symmetric flows with similar topological complexity. To suppress task-related bias, pattern-based implicit task design was employed, addressing critical point recognition, critical point classification, and symmetric pattern categorization. In addition, variable-duration and fixed-duration measurement schemes were utilized for lightweight precision-critical and heavyweight judgment intensive flow analysis tasks, respectively, to record visualization effectiveness. We eliminated outliers and used the Ryan REGWQ post-hoc homogeneous subset tests in statistical analysis to obtain reliable findings. Our study shows that a texture-based dense representation with accentuated flow streaks, such as enhanced LIC, enables intuitive perception of the flow, while a geometry-based integral representation with uniform density control, such as evenly spaced streamlines, may exploit visual interpolation to facilitate mental reconstruction of the flow. It is also shown that inappropriate color mapping (e.g., colorwheel) may add distractions to a flow representation.

BACKGROUND: Functional genomics technologies that measure genome expression at a global scale are accelerating biological knowledge discovery. Generating these high throughput datasets is relatively easy compared to the downstream functional modelling necessary for elucidating the molecular mechanisms that govern the biology under investigation. A number of publicly available 'discovery-based' computational tools use the computationally amenable Gene Ontology (GO) for hypothesis generation. However, there are few tools that support hypothesis-based testing using the GO and none that support testing with user defined hypothesis terms.Here, we present GOModeler, a tool that enables researchers to conduct hypothesis-based testing of high throughput datasets using the GO. GOModeler summarizes the overall effect of a user defined gene/protein differential expression dataset on specific GO hypothesis terms selected by the user to describe a biological experiment. The design of the tool allows the user to complement the functional information in the GO with his/her domain specific expertise for comprehensive hypothesis testing. RESULTS: GOModeler tests the relevance of the hypothesis terms chosen by the user for the input gene dataset by providing the individual effects of the genes on the hypothesis terms and the overall effect of the entire dataset on each of the hypothesis terms. It matches the GO identifiers (ids) of the genes with the GO ids of the hypothesis terms and parses the names of those ids that match to assign effects. We demonstrate the capabilities of GOModeler with a dataset of nine differentially expressed cytokine genes and compare the results to those obtained through manual analysis of the dataset by an immunologist. The direction of overall effects on all hypothesis terms except one was consistent with the results obtained by manual analysis. The tool's editing capability enables the user to augment the information extracted. GOModeler is available as a part of the AgBase tool suite (http://www.agbase.msstate.edu). CONCLUSIONS: GOModeler allows hypothesis driven analysis of high throughput datasets using the GO. Using this tool, researchers can quickly evaluate the overall effect of quantitative expression changes of gene set on specific biological processes of interest. The results are provided in both tabular and graphical formats.

Abstract A perceptual study of four tensor glyphs for symmetric, real, traceless tensors was performed. Each glyph encodes three properties of the system: Orientation, uniaxiality (alignment along the direction of orientation), and biaxiality (alignment along a vector orthogonal to the orientation). Thirty users over two studies were asked to identify these three properties for each glyph type under a variety of permutations in order to evaluate the effectiveness of visually communicating the properties; response time was also measured. We discuss the significant differences found between the methods as guidance to the use of these glyphs for traceless tensor visualization.

We describe the development of a set of tools for analyzing the textual contents of digital forensic evidence for the purpose of enhancing an investigator's ability to discover information quickly and efficiently. By examining the textual contents of files and unallocated space, relationships between sets of files and clusters can be formed based on the information that they contain. Using the information gathered from the evidence through the analysis tool, the visualization tool can be used to search through the evidence in an organized and efficient manner. The visualization depicts both the frequency of relevant terms and their location on disk. We also discuss a task analysis with forensics officers to motivate the design.

BACKGROUND: Multiple alignment of protein sequences can provide insight into sequence conservation across many species and thus allow identification of those sections of the sequence most critical to protein function. This insight can be augmented by joint display of conserved domains along the sequences. By fusing this metadata visually, biologists can analyze sequence conservation and functional motifs simultaneously and efficiently. RESULTS: We present MSAVis, a new approach combining luminance and hue for simultaneous visualization of conserved motifs and sequence alignment. Input for the algorithm is a multiple sequence alignment in a standard format. The NCBI Conserved Domain Database (CDD) is used for finding conserved domains along the alignment. The visualization quickly identifies conserved domains, and allows both macro (sequence-long) and micro (small amino-acid neighborhood) views. CONCLUSION: MSAVis utilizes two visual cues, luminance and hue, to facilitate at-a-glance summary of the conservation of a user-provided protein alignment while enabling multiple comparisons among functional domains. These visual cues are preattentive and separable so that the relationship between conservation strength and domain membership can be understood. The MSAVis software, written in Python and using BioPython and OpenGL, can be found at http://agbase.msstate.edu/tools/MSAVis.html.

Visualization exploration is the process of extracting insight from data via interaction with visual depictions of that data. Visualization exploration is more than presentation; the interaction with both the data and its depiction is as important as the data and depiction itself. Significant visualization research has focused on the generation of visualizations (the depiction); less effort has focused on the exploratory aspects of visualization (the process). However, without formal models of the process, visualization exploration sessions cannot be fully utilized to assist users and system designers. Toward this end, we introduce the P-Set Model of Visualization Exploration for describing this process and a framework to encapsulate, share, and analyze visual explorations. In addition, systems utilizing the model and framework are more efficient as redundant exploration is avoided. Several examples drawn from visualization applications demonstrate these benefits. Taken together, the model and framework provide an effective means to exploit the information within the visual exploration process.

A glyph-based method for visualizing the nematic liquid crystal alignment tensor is introduced. Unlike previous approaches, the glyph is based upon physically-linked metrics, not offsets of the eigenvalues. These metrics, combined with a set of superellipsoid shapes, communicate both the strength of the crystal's uniaxial alignment and the amount of biaxiality. With small modifications, our approach can visualize any real symmetric traceless tensor.

A method for the semi-automatic detection and visualization of defects in models of nematic liquid crystals (NLCs) is introduced; this method is suitable for unstructured models, a previously unsolved problem. The detected defects-also known as disclinations-are regions were the alignment of the liquid crystal rapidly changes over space; these defects play a large role in the physical behavior of the NLC substrate. Defect detection is based upon a measure of total angular change of crystal orientation (the director) over a node neighborhood via the use of a nearest neighbor path. Visualizations based upon the detection algorithm clearly identifies complete defect regions as opposed to incomplete visual descriptions provided by cutting-plane and isosurface approaches. The introduced techniques are currently in use by scientists studying the dynamics of defect change.

Visualizing hierarchical data is one of the core areas of information visualization. Most of these techniques focus on single hierarchies—hierarchies with a single root element and a single path to each element. In contrast, this work focuses on the browsing of multi-hierarchies—hierarchies with multiple roots or multiple paths per element. A radial focus+context display algorithm and interaction methods are introduced to explore such multi-hierarchical data. A series of examples demonstrate the effectiveness of our new visualization. Keywords: information visualization, focus+context, radial layout, hierarchical visualization

The current state of the art in visualization research places strong emphasis on different techniques to derive insight from disparate types of data. However, little work has investigated the visualization process itself. The information content of the visualization process - the results, history, and relationships between those results - is addressed by this work. A characterization of the visualization process is discussed, leading to a general model of the visualization exploration process. The model, based upon a new parameter derivation calculus, can be used for automated reporting, analysis, or visualized directly. An XML-based language for expressing visualization sessions using the model is also described. These sessions can then be shared and reused by collaborators. The model, along with the XML representation, provides an effective means to utilize information within the visualization process to further data exploration.

Exploring complex, very large data sets requires interfaces to present and navigate through the visualization of the data. Two types of audience benefit from such coherent organization and representation: first, the user of the visualization system can examine and evaluate their data more efficiently; second, collaborators or reviewers can quickly understand and extend the visualization. The needs of these two groups are addressed by the spreadsheet-like interface described in this paper. The interface represents a 2D window in a multidimensional visualization parameter space. Data is explored by navigating this space via the interface. The visualization space is presented to the user in a manner that clearly identifies which parameters correspond to which visualized result. Operations defined on this space can be applied which generate new parameters or results. Combined with a general-purpose interpreter, these functions can be utilized to quickly extract desired results. Finally, by encapsulating the visualization process, redundant exploration is eliminated and collaboration is facilitated. The efficacy of this novel interface is demonstrated through examples using a variety of data sets in different domains.

As the size and complexity of data sets continues to increase, the development of user interfaces and interaction techniques that expedite the process of exploring that data must receive new attention. Regardless of the speed of rendering, it is important to coherently organize the visual process of exploration: this information both grants insights about the data to a user and can be used by collaborators to understand the results. To fulfil these needs, we present a spreadsheet-like interface to data exploration. The interface displays a 2-dimensional window into visualization parameter space which users manipulate as they search for desired results. Through tabular organization and a clear correspondence between parameters and results, the interface eases the discovery, comparison and analysis of the underlying data. Users can utilize operators and the integrated interpreter to further explore and automate the visualization process; using a method introduced in this paper, these operations can be applied to cells in different stacks of the interface. Via illustrations using a variety of data sets, we demonstrate the efficacy of this novel interface.