Cube maze (original) (raw)
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2017
Data Visualisation and Analytics plays a key role in providing a complete view and discovering the global/local patterns hidden in the data. Conventional data visualization methods as well as the extension of some conventional method are very narrow in terms of the data type on which it is applicable. We present a novel way of visualising data which can be generalized to any kind of data format. Data Units Multi Digraph Model can encompass all varieties of data and will be able give global/local view unlike others where data is mapped to nodes in a graph or shown in charts. This research project is a novel way of representing abstract data on the facets of a cube. It involves visualization and navigation of abstract data mapped to the facets of a cube. I. PROJECT DESCRIPTION We view a multimedial data collection as a “labeled multidigraph” over a finite set of ranked “data units”. Each data unit is an ordered set of data components each of which posses an identifier, a string name, ...
ClusterVis: Visualizing Nodes Attributes in Multivariate Graphs
HAL (Le Centre pour la Communication Scientifique Directe), 2017
Many computing applications imply dealing with network data, for example, social networks, communications and computing networks, epidemiological networks, among others. These applications are usually based on multivariate graphs, i.e., graphs in which items and relationships have multiple attributes. Most of the visualization techniques described in the literature for dealing with multivariate graphs focus either on problems associated with the visualization of topology or on problems associated with the visualization of the items' attributes. The integration of these two components (topology and multiple attributes) in a single visualization turns into a challenge due to the necessity of simultaneously representing the connections and mapping attributes possibly generating overlapping elements. Among usual strategies to overcome this legibility problem we find filtering and aggregation that makes possible a simplified representation with reduced size and density providing a general view. However, this simplification may lead to a reduction of the amount of information being displayed, while in several applications the graph details still need to be represented in order to make possible in-depth data analysis. In face of that, we propose ClusterVis, a visualization technique aiming at exploring nodes attributes pertaining to sub-graphs, which are either obtained from clustering algorithms or some user-defined criteria. The technique allows comparing attributes of nodes while keeping the representation of the relationships among them. The technique was implemented within a visualization framework and evaluated by potential users. CCS Concepts •Human-centered Computing ➝ Visualization application domains ➝ Information Visualization.
GraphScape: integrated multivariate network visualization
2007 6th International Asia-Pacific Symposium on Visualization, 2007
In this paper, we introduce a new method, GraphScape, to visualize multivariate networks, i.e., graphs with multivariate data associated with their nodes. GraphScape adopts a landscape metaphor with network structure displayed on a 2D plane and the surface height in the third dimension represents node attribute. More than one attribute can be visualized simultaneously by using multiple surfaces. In addition, GraphScape can be easily combined with existing methods to further increase the total number of attributes visualized. One of the major goals of GraphScape is to reveal multivariate graph clustering, which is based on both network structure and node attributes. This is achieved by a new layout algorithm and an innovative way of constructing attribute surface, which also allows visual clustering at different scales through interaction. A simplified attribute surface model is also proposed to reduce computation requirement when visualizing large networks. GraphScape is applied to networks of three different size (20, 100, and 1500) to demonstrate its effectiveness.
Multidimensional information visualization using attribute rods
2000
In this paper we propose new visual interface technology to address multidimensional data exploration and browsing tasks. MultiNav, a prototype from GTE Laboratories, is based upon a multidimensional information model that affords new data exploration and semantically structured browsing interactions. The primary visual metaphor is based on sliding rods, each of which is associated with an information dimension from the underlying model. Users can interactively select value ranges along the rods in order to reveal hidden relationships as well as query and restrict the set through direct manipulation. A novel focus+context view is afforded in which detail about individual items is revealed within the context of the global multidimensional attribute space. We propose a novel interaction technique to change focus, which is based on dragging rods from side to side. We relate this work on multidimensional information visualization to other research in the area, including Parallel Coordinates, Dynamic Histograms, Dynamic Queries, and focus+context tables. *
Hierarchy-driven Visual Exploration of Multidimensional Data Cubes
2007
Analysts interact with OLAP data in a predominantly "drill-down" fashion, i.e. gradually descending from a coarsely grained overview towards the desired level of detail. Analysis tools enable visual exploration as a sequence of navigation steps in the data cubes and their dimensional hierarchies. However, most state-of-the-art solutions are limited either in their capacity to handle complex multidimensional data or in the ability of their visual metaphors to provide an overview+details context. This work proposes an explorative framework for OLAP data based on a simple but powerful approach to analyzing data cubes of virtually arbitrary complexity. The data is queried using an intuitive navigation in which each dimension is represented by its hierarchy schema. Any granularity level can be dragged into the visualization to serve as an disaggregation axis. The results of the iterative exploration are mapped to a specified visualization technique. We favor hierarchical layouts for their natural ability to show step-wise decomposition of aggregate values. The power of the tool to support various application scenarios is demonstrated by presenting use cases from different domains and the visualization techniques suitable for solving specific analysis tasks.
Semantic Blossom Graph: A New Approach for Visual Graph Exploration
2014 18th International Conference on Information Visualisation, 2014
Graphs are widely used to represent relationships between entities. Indeed, their simplicity in depicting connectedness backed by a mathematical formalism, make graphs an ideal metaphor to convey relatedness between entities irrespective of the domain. However, graphs pose several challenges for visual analysis. A large number of entities or a densely connected set quickly render the graph unreadable due to clutter. Typed relationships leading to multigraphs cannot clearly be represented in hierarchical layout or edge bundling, common clutter reduction techniques. We propose a novel approach to visual analysis of complex graphs based on two metaphors: semantic blossom and selective expansion. Instead of showing the whole graph, we display only a small representative subset of nodes, each with a compressed summary of relations in a semantic blossom. Users apply selective expansion to traverse the graph and discover the subset of interest. A preliminary evaluation showed that our approach is intuitive and useful for graph exploration and provided insightful ideas for future improvements.
IEEE Transactions on Visualization and Computer Graphics, 2000
Few existing visualization systems can handle large datasets with hundreds of dimensions, since high dimensional datasets cause clutter on the display and large response time in interactive exploration. In this paper, we present a significantly improved multi-dimensional visualization approach named Value and Relation (VaR) display that allows users to effectively and efficiently explore large datasets with several hundred dimensions. In the VaR display, data values and dimension relationships are explicitly visualized in the same display by using dimension glyphs to explicitly represent values in dimensions and glyph layout to explicitly convey dimension relationships. In particular, pixel-oriented techniques and density-based scatterplots are used to create dimension glyphs to convey values. Multi-dimensional scaling, Jigsaw map hierarchy visualization techniques, and an animation metaphor named Rainfall are used to convey relationships among dimensions. A rich set of interaction tools have been provided to allow users to interactively detect patterns of interest in the VaR display. A prototype of the VaR display has been fully implemented. The case studies presented in this paper show how the prototype supports interactive exploration of datasets of several hundred dimensions. A user study evaluating the prototype is also reported in this paper.
Value and relation display for interactive exploration of high dimensional datasets
2004
Traditional multi-dimensional visualization techniques, such as glyphs, parallel coordinates and scatterplot matrices, suffer from clutter at the display level and difficult user navigation among dimensions when visualizing high dimensional datasets. In this paper, we propose a new multi-dimensional visualization technique named a Value and Relation (VaR) display, together with a rich set of navigation and selection tools, for interactive exploration of datasets with up to hundreds of dimensions. By explicitly conveying the relationships among the dimensions of a high dimensional dataset, the VaR display helps users grasp the associations among dimensions. By using pixel-oriented techniques to present values of the data items in a condensed manner, the VaR display reveals data patterns in the dataset using as little screen space as possible. The navigation and selection tools enable users to interactively reduce clutter, navigate within the dimension space, and examine data value details within context effectively and efficiently. The VaR display scales well to datasets with large numbers of data items by employing sampling and texture mapping. A case study on a real dataset, as well as the VaR displays of multiple real datasets throughout the paper, reveals how our proposed approach helps users interactively explore high dimensional datasets with large numbers of data items.
Hierarchy-driven exploration of multidimensional data cubes
2007
Analysts interact with OLAP data in a predominantly "drill-down" fashion, i.e. gradually descending from a coarsely grained overview towards the desired level of detail. Analysis tools enable visual exploration as a sequence of navigation steps in the data cubes and their dimensional hierarchies. However, most state-of-the-art solutions are limited either in their capacity to handle complex multidimensional data or in the ability of their visual metaphors to provide an overview+details context. This work proposes an explorative framework for OLAP data based on a simple but powerful approach to analyzing data cubes of virtually arbitrary complexity. The data is queried using an intuitive navigation in which each dimension is represented by its hierarchy schema. Any granularity level can be dragged into the visualization to serve as an disaggregation axis. The results of the iterative exploration are mapped to a specified visualization technique. We favor hierarchical layouts for their natural ability to show step-wise decomposition of aggregate values. The power of the tool to support various application scenarios is demonstrated by presenting use cases from different domains and the visualization techniques suitable for solving specific analysis tasks.
RadViz Deluxe: An Attribute-Aware Display for Multivariate Data
Processes, 2017
Modern data, such as occurring in chemical engineering, typically entail large collections of samples with numerous dimensional components (or attributes). Visualizing the samples in relation of these components can bring valuable insight. For example, one may be able to see how a certain chemical property is expressed in the samples taken. This could reveal if there are clusters and outliers that have specific distinguishing properties. Current multivariate visualization methods lack the ability to reveal these types of information at a sufficient degree of fidelity since they are not optimized to simultaneously present the relations of the samples as well as the relations of the samples to their attributes. We propose a display that is designed to reveal these multiple relations. Our scheme is based on the concept of RadViz, but enhances the layout with three stages of iterative refinement. These refinements reduce the layout error in terms of three essential relationships-sample to sample, attribute to attribute, and sample to attribute. We demonstrate the effectiveness of our method via various real-world domain examples in the domain of chemical process engineering. In addition, we also formally derive the equivalence of RadViz to a popular multivariate interpolation method called generalized barycentric coordinates.