Assessing the use of slicing-based visualizing techniques on the understanding of large metamodels

ContextMetamodels are cornerstones of various metamodeling activities. Such activities consist of, for instance, transforming models into code or comparing metamodels. These activities thus require a good understanding of a metamodel and/or its parts. Current metamodel editing tools are based on standard interactive visualization features, such as physical zooms.ObjectiveHowever, as soon as metamodels become large, navigating through large metamodels becomes a tedious task that hinders their understanding. So, a real need to support metamodel comprehension appears.MethodIn this work we promote the use of model slicing techniques to build interactive visualization tools for metamodels. Model slicing is a model comprehension technique inspired by program slicing. We show how the use of Kompren, a domain-specific language for defining model slicers, can ease the development of such interactive visualization features.ResultsWe specifically make four main contributions. First, the proposed interactive visualization techniques permit users to focus on metamodel elements of interest, which aims at improving the understandability. Second, these proposed techniques are developed based on model slicing, a model comprehension technique that involves extracting a subset of model elements of interest. Third, we develop a metamodel visualizer, called Explen, embedding the proposed interactive visualization techniques. Fourth, we conducted experiments. showing that Explen significantly outperforms EcoreTools, in terms of time, correctness, and navigation effort, on metamodeling tasks.ConclusionThe results of the experiments, in favor of Explen, show that improving metamodel understanding can be done using slicing-based interactive navigation features.     

A method for modeling and visualizing the three-dimensional organization of neuron populations from replicated data: Properties, implementation and illustration

Understanding how the architecture of neuronal populations contributes to brain function requires three-dimensional representations and analyses. Neuroanatomical techniques are available to locate neurons in animal brains. Repeating an experiment in different individuals yields a collection of point patterns from which common organization principles are generally difficult to extract. We recently addressed the problem of generating statistical density maps to integrate replicated point pattern data into meaningful, interpretable representations. Applications to different neuroanatomical systems illustrated the ability of our method to reveal organization rules that cannot be perceived directly on raw data. To make the method practicable for further applications, the aim of the present paper is to establish general guidelines for appropriate parameter tuning, valid result interpretation as well as efficient implementation. Accordingly, we characterize the method by analyzing the role of its main parameter, by reporting results on its statistical properties and by demonstrating its robustness, using both simulated and real neuroanatomical data.     

WebQuery: searching and visualizing the Web through connectivity

Finding information located somewhere on the World-Wide Web is an error-prone and frustrating task. The WebQuery system offers a powerful new method for searching the Web based on connectivity and content. We do this by examining links among the nodes returned in a keyword-based query. We then rank the nodes, giving the highest rank to the most highly connected nodes. By doing so, we are finding “hot spots” on the Web that contain information germane to a user's query. WebQuery not only ranks and filters the results of a Web query, it also extends the result set beyond what the search engine retrieves, by finding “interesting” sites that are highly connected to those sites returned by the original query. Even with WebQuery filtering and ranking query results, the result sets can be enormous. So, we need to visualize the returned information. We explore several techniques for visualizing this information—including cone trees, 2D graphs, 3D graphs, lists, and bullseyes-and discuss the criteria for using each of the techniques.     

SparkClouds: visualizing trends in tag clouds

Tag clouds have proliferated over the web over the last decade. They provide a visual summary of a collection of texts by visually depicting the tag frequency by font size. In use, tag clouds can evolve as the associated data source changes over time. Interesting discussions around tag clouds often include a series of tag clouds and consider how they evolve over time. However, since tag clouds do not explicitly represent trends or support comparisons, the cognitive demands placed on the person for perceiving trends in multiple tag clouds are high. In this paper, we introduce SparkClouds, which integrate sparklines into a tag cloud to convey trends between multiple tag clouds. We present results from a controlled study that compares SparkClouds with two traditional trend visualizations—multiple line graphs and stacked bar charts—as well as Parallel Tag Clouds. Results show that SparkClouds ability to show trends compares favourably to the alternative visualizations.     

GraphSplatting: visualizing graphs as continuous fields

This paper introduces GraphSplatting, a technique which transforms a graph into a two-dimensional scalar field. The scalar field can be rendered as a color coded map, a height field, or a set of contours. Splat fields allow for the visualization of arbitrarily large graphs without cluttering. They provide density information which can be used to determine the structure of the graph. The construction, visualization, and interaction with splat fields is discussed. Two applications illustrate the usage of GraphSplatting.     

Visualization blackboard: visualizing the visible human

Visualization of the human body and its inner structure has challenged artists and scientists for centuries. For 500 years, since Leonardo da Vinci, drawings have been the main resource for learning anatomy because they allow the mixture of realism and abstraction suitable for didactic purposes. With the discovery of X-rays 100 years ago, it became possible to look into the living body. Only since the 1970s have computer tomography (CT) and magnetic resonance imaging (MRI) made it possible to acquire image data in three dimensions. Based on these techniques, 3D computer graphics generated the first models of the living body. These represent a tremendous advance for diagnosis and surgical planning, but the resolution is still poor when viewed from an anatomist's point of view. The National Library of Medicine's Visible Human Project provided much more realistic data-the Visible Human data set, created at the University of Colorado School of Medicine. This project produced transverse cross sectional photographic images of a male cadaver with a resolution of 0.33 mm and slice distance of 1 mm     

H-BloX: visualizing alignment block entropies.

H-BloX is a web-based JavaScript application that allows the calculation and visualization of Shannon information content or relative entropy (Kullback-Leibler 'distance') within sequence alignment blocks. The application was designed for use in both teaching and research. Amino acid, nucleic acid sequences, or any other type of aligned chemical structures may serve as the input. Various interpretations of the meaning of 'entropy' or 'information content' are possible, including treatment as a chemical diversity measure or the degree of feature conservation. For analysis of numerical data by H-BloX, values must be converted to a user-defined character alphabet before computation of entropy or information content. H-BloX was successfully applied to feature identification in Escherichia coli signal peptides and their cleavage sites. Characteristics known features became visible, e.g., the hydrophobic core region and the well-known '-3,-1' cleavage site pattern. Based on the H-BloX analysis, the hydrophobic core is centered at amino acid residue position 13, counting from the N-terminal end of the protein precursor sequence. This result was obtained by using a built-in feature of H-BloX that enables conversion of amino acid sequences to a different alphabet that is based on hydrophobicity assignments. H-BloX can be accessed online or downloaded as HTML/JavaScript at http://bopwww.biologie.uni-freiburg.de/~bioinfo/HBloX/html/index.html.     

Computing and visualizing popular places

Data analysis and knowledge discovery in trajectory databases is an emerging field with a growing number of applications such as managing traffic, planning tourism infrastructures, analyzing professional sport matches or better understanding wildlife. A well-known collection of patterns which can occur for a subset of trajectories of moving objects exists. In this paper, we study the popular places pattern, that is, locations that are visited by many moving objects. We consider two criteria, strong and weak, to establish either the exact number of times that an object has visited a place during its complete trajectory or whether it has visited the place, or not. To solve the problem of reporting popular places, we introduce the popularity map. The popularity of a point is a measure of how many times the moving objects of a set have visited that point. The popularity map is the subdivision, into regions, of a plane where all the points have the same popularity. We propose different algorithms to efficiently compute and visualize popular places, the so-called popular regions and their schematization, by taking advantage of the parallel computing capabilities of the graphics processing units. Finally, we provide and discuss the experimental results obtained with the implementation of our algorithms.     

Ray tracing interference color: visualizing Newton's rings

Generalized Fresnel's reflectance formulas provide the basis for this ray tracing method, which simulates the natural phenomenon of interference color. A visualization of Newton's rings demonstrates its accuracy     

ThemeRiver: visualizing thematic changes in large documentcollections

The ThemeRiver visualization depicts thematic variations over time within a large collection of documents. The thematic changes are shown in the context of a time-line and corresponding external events. The focus on temporal thematic change within a context framework allows a user to discern patterns that suggest relationships or trends. For example, the sudden change of thematic strength following an external event may indicate a causal relationship. Such patterns are not readily accessible in other visualizations of the data. We use a river metaphor to convey several key notions. The document collection's time-line, selected thematic content and thematic strength are indicated by the river's directed flow, composition and changing width, respectively. The directed flow from left to right is interpreted as movement through time and the horizontal distance between two points on the river defines a time interval. At any point in time, the vertical distance, or width, of the river indicates the collective strength of the selected themes. Colored "currents" flowing within the river represent individual themes. A current's vertical width narrows or broadens to indicate decreases or increases in the strength of the individual theme     

Viz-A-Vis: toward visualizing video through computer vision

In the established procedural model of information visualization, the first operation is to transform raw data into data tables [1]. The transforms typically include abstractions that aggregate and segment relevant data and are usually defined by a human, user or programmer. The theme of this paper is that for video, data transforms should be supported by low level computer vision. High level reasoning still resides in the human analyst, while part of the low level perception is handled by the computer. To illustrate this approach, we present Viz-A-Vis, an overhead video capture and access system for activity analysis in natural settings over variable periods of time. Overhead video provides rich opportunities for long-term behavioral and occupancy analysis, but it poses considerable challenges. We present initial steps addressing two challenges. First, overhead video generates overwhelmingly large volumes of video impractical to analyze manually. Second, automatic video analysis remains an open problem for computer vision.     

MGV: a system for visualizing massive multidigraphs

Describes MGV (Massive Graph Visualizer), an integrated visualization and exploration system for massive multidigraph navigation. It adheres to the visual information-seeking mantra: overview first, zoom and filter, then details on demand. MGV's only assumption is that the vertex set of the underlying digraph corresponds to the set of leaves of a pre-determined tree T. MGV builds an out-of-core graph hierarchy and provides mechanisms to plug in arbitrary visual representations for each graph hierarchy slice. Navigation from one level to another of the hierarchy corresponds to the implementation of a drill-down interface. In order to provide the user with navigation control and interactive response, MGV incorporates a number of visualization techniques like interactive pixel-oriented 2D and 3D maps, statistical displays, color maps, multi-linked views and a zoomable label-based interface. This makes the association of geographic information and graph data very natural. To automate the creation of the vertex set hierarchy for MGV, we use the notion of graph sketches. They can be thought of as visual indices that guide the navigation of a multigraph too large to fit on the available display. MGV follows the client-server paradigm and it is implemented in C and Java-3D. We highlight the main algorithmic and visualization techniques behind the tools and, along the way, point out several possible application scenarios. Our techniques are being applied to multigraphs defined on vertex sets with sizes ranging from 100 million to 250 million vertices     

Mobrex: visualizing users' mobile browsing behaviors.

This paper deals with the Mobile Browsing Explorer (Mobrex) to give analysts a set of interactive visualizations that highlight various aspects of how users browse an information space. Here, we describe the tool and demonstrate its support of a user study of three browsing techniques for mobile maps. Although we mainly focus here on PDAs and mobile map browsing, Mobrex can easily support analysts studying user interaction with other information spaces and other devices, including mobile phones and desktop computers.     

Railroad online: acquiring and visualizing route panoramas of rail scenes

A patrol type of surveillance has been performed everywhere from police city patrol to railway inspection. Different from static cameras or sensors distributed in a space, such surveillance has its benefits of low cost, long distance, and efficiency in detecting infrequent changes. However, the challenges are how to archive daily recorded videos in the limited storage space and how to build a visual representation for quick and convenient access to the archived videos. We tackle the problems by acquiring and visualizing route panoramas of rail scenes. We analyze the relation between train motion and the video sampling and the constraints such as resolution, motion blur and stationary blur etc. to obtain a desirable panoramic image. The route panorama generated is a continuous image with complete and non-redundant scene coverage and compact data size, which can be easily streamed over the network for fast access, maneuver, and automatic retrieval in railway environment monitoring. Then, we visualize the railway scene based on the route panorama rendering for interactive navigation, inspection, and scene indexing.     

Detecting and visualizing cohesive activity-travel patterns: A network analysis approach

This article presents a network analytical framework to detect individual-based activity-travel patterns (ATPs) in space and time. Compared to many existing classification methods (e.g., hot-spot detection, sequential alignment method), the network method substantiates the social meanings underlying the interconnectedness and similarities of people's activity trajectories and better integrates spatial interaction (colocation or distance-decay) and temporal connections (concurrence or sequence) of daily lives in the measure of similarity. This approach enables us to detect variant community structures, with individuals in the same community interacting relatively more than individuals belonging to different communities, by decomposing the complex trajectories into different meaningful events (e.g., activities, trips, tours, and subsequences). We also demonstrate the practicality and scientific merit of the network analysis approach in a case study of household travel behavior in Charlotte, North Carolina. Results derived from disaggregated survey data establish the effectiveness and flexibility of the network methods to detect cohesive communities of individuals and ATPs by different narratives of everyday-life events. This study also suggests that the network analysis approach has great potential to classify large datasets of other space-time trajectories and to discover policy-sensitive activity, trip, and tour patterns that help us develop policy and planning alternatives for sustainable communities and mobility.     

On visualizing uncertainty in volumetric data: techniques and their evaluation

Four new techniques for the visualization of uncertainty in volumetric data are introduced, including three glyph-based techniques (i.e., cylinder, cone, and multi-point glyphs) and one non-glyph-based approach. The non-glyph-based approach uses an aliasing mechanism. Four of the existing techniques for visualization of uncertainty in volumetric data are also described, including transparency, color mapping, and ball and arrow glyph techniques. These new and existing techniques are analyzed via a usability study that considers four aspects of the techniques’ effectiveness (identification of the data and of the uncertainty, visual overload, and brightness contrast) for one typical volume visualization scenario. The analysis suggests that while each technique has some utility for a scenario such as the tested one, the new multi-point glyph and the existing ball and arrow glyph techniques appear to be most advantageous.     

Computational dynamics: Modeling and visualizing trajectory flows in phase space

This paper describes a computational technique for modeling and visualizing dynamical behaviors of complex systems in phase space. The technique employs a novel idea offlow pipes to model trajectory bundles that exhibit the same qualitative features. It parses a continuous phase space of a dynamical system, consisting of an infinite number of individual trajectories, into a manageable discrete collection of flow pipes that a computer can efficiently reason about. The technique provides a computational way for both machines and humans to visualize and manipulate dynamics of a physical system. The flow-pipe modeling technique is implemented in a program called MAPS. The technique has been applied to the automatic control synthesis in which programs automatically analyze and design high-performance, global controllers.