DATA VISUALIZATION

By

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V Sowjanya

Sr. Assistant Professor

Dept of Artificial Intelligence & Data Science

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LAKIREDDY BALI REDDY COLLEGE OF ENGINEERING

At the end of this unit, Student will be able to:

• CO3 Apply structured approach to create effective visualization. (Apply-L3)

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Unit-III Outcomes

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DATA VISUALIZATION

• In this chapter an overview of a number of data and information visualization systems and toolkits is provided.
• Concentration primarily on software that is freely available, to enable students interested in exploring further in the field of visualization to try out existing technology.

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Chapter Preface

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Types of Systems

• Systems based on data types
• Systems based on analysis type
• Text Analysis and Visualization
• Toolkits

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Systems Based on Data Type

• Scientific Data
• Multivariate Data
• Graph Data

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Scientific Data

• OpenDX which was formerly marketed as IBM Visualization Data Explorer, is an extensible visualization environment primarily used for the analysis of scientific and engineering data.
• What separates it from most other visualization platforms is the visual programming process used to create custom visualizations.
• Its Network Editor allows users to drag-and-drop components onto a canvas and create links between components for communicating data of specified types.
• The modules fall into several distinct classes, including:

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DATA VISUALIZATION

• import and export—modules to load and save data in different formats;
• flow control—modules to create loops and conditional execution;
• realization— modules to map the data to renderable entities, such as isosurfaces, grids, and streamlines
• rendering—modules to control display attributes, such as lighting, cameras, and clipping
• transformation—functions to apply to the data, such as filtering, mathematical functions, and sorting
• interactor—widgets such as file selectors, menus, dials/sliders, and button boxes.

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Scientific Data

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DATA VISUALIZATION

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Scientific Data

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DATA VISUALIZATION

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Scientific Data

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DATA VISUALIZATION

• A large range of existing modules have been developed to date, and visualizations can be created that combine different data and different mappings into a single visualization.
• Users can either restrict themselves to using the existing library of components/modules, or they may write their own, using a software development kit that contains libraries to access data structures and interface components.

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Scientific Data

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DATA VISUALIZATION

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Scientific Data

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DATA VISUALIZATION

• XmdvTool, developed at Worcester Polytechnic Institute (WPI) by Matthew Ward, Elke Rundensteiner, and their students, is a public domain visualization software package that integrates five common methods for multivariate data visualization into a single exploration application.
• This tool includes standard scatterplot matrices, scatterplots of star glyphs, parallel coordinates, dimensional stacking, and pixel-oriented techniques.
• These visualizations are linked together using a simple selection and highlighting mechanism called an N-dimensional brush, which defines a hyperbox in the data space.
• Selected data in one view are also selected in other views, and the resulting selection can be highlighted, masked, deleted, or analyzed separate from the rest of the data.

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Multivariate Data

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• Beyond its original inception, XmdvTool has been extended to include additional architectural features for supporting large data sets.
• First, YingHuey Fua et al. introduced hierarchical parallel coordinates for the exploration of data sets containing many records.
• Data is hierarchically clustered and the results displayed in a summarized parallel coordinates display using variable opacity bands.

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Multivariate Data

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Multivariate Data

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• This was followed by the addition of a structure-based brush, an associated user interface for browsing, and brushing within this hierarchical data structure.
• Jing Yang et al. generalized the application of this hierarchical data structure to XmdvTool’s other visualizations and defined the interactive hierarchical displays (IHD) framework.
• Furthermore, XmdvTool provides a visual hierarchical dimension reduction (VHDR) framework that groups and organizes the space of dimensions, providing meaningful subspaces of the dimensions for analysis .
• XmdvTool also includes the distance quantification classing (DQC) approach to handle nominal variables and tools to reorder dimensions to reduce visual clutter

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Multivariate Data

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DATA VISUALIZATION

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Multivariate Data

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• GraphViz is a library of graph layout algorithms developed at AT&T Research.
• The architecture and philosophy of GraphViz is rather unique compared to other visualization tools.
• It supports a range of graph specification methods, layout methods, and rendering methods.
• While some interactive components have been integrated with the system, it is primarily a script-driven system.
• One selects a graph description file and feeds it to a layout engine, along with a desired output format and any other parameters to the layout technique.
• The output formats supported are vast, to enable easy integration of results into documents, web pages, and applications.

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Graph Data

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DATA VISUALIZATION

• All GraphViz programs accept input files in the DOT language, which is defined by an abstract grammar. A simple example of a graph specified in DOT is as follows: digraph G { size="6,6"; node [shape=circle,fontsize=8]; rankdir=LR; st9 -> st9 [label="11/1"]; st9 -> st10 [label="10/1"]; st8 -> st8 [label="10/1"]; st8 -> st0 [label="00/-"]; st7 -> st8 [label="10/1"]; st7 -> st7 [label="00/1"]; st6 -> st6 [label="01/1"]; st6 -> st0 [label="00/-"]; st5 -> st6 [label="01/1"]; st5 -> st5 [label="11/1"]; st4 -> st4 [label="01/1"]; st4 -> st0 [label="00/-"]; st3 -> st4 [label="01/1"]; st3 -> st3 [label="00/1"]; st2 -> st9 [label="11/1"]; st2 -> st7 [label="00/1"]; st2 -> st2 [label="01/1"]; st10 -> st10 [label="10/1"]; st10 -> st0 [label="00/-"]; st1 -> st5 [label="11/1"]; st1 -> st3 [label="00/1"]; st1 -> st1 [label="10/1"]; st0 -> st2 [label="01/-"]; st0 -> st1 [label="10/-"]; st0 -> st0 [label="00/0"]; }

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Graph Data

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DATA VISUALIZATION

• Each line defines attributes of a graph as a whole, a node, or a link.
• Both directed and undirected graphs are allowed (just switch -> to - for undirected).

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Graph Data

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Graph Data

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• Each of the above diagrams are generated by techniques
• dot—a layered approach that attempts to aim edges in the same direction;
• neato—a spring model based on multidimensional scaling;
• circo—a circular layout that is often effective for communications networks;
• fdp—a force-directed method that uses multigrid heuristics to enable handling of large graphs.

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Graph Data

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DATA VISUALIZATION

• Unit-III Outcomes
• Chapter Preface
• Types of Systems
• Systems Based on Data Types
• Scientific Data
• Multivariate Data
• Graph Data

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Summary

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