DATA VISUALIZATION

By

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S.V.V.D.Jagadeesh

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)

S.V.V.D.Jagadeesh

Tuesday, August 19, 2025

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.

S.V.V.D.Jagadeesh

Tuesday, August 19, 2025

Chapter Preface

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S.V.V.D.Jagadeesh

Tuesday, August 19, 2025

Types of Systems

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

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S.V.V.D.Jagadeesh

Tuesday, August 19, 2025

Systems Based on Data Type

• Scientific Data
• Multivariate Data
• Graph Data

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S.V.V.D.Jagadeesh

Tuesday, August 19, 2025

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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• 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.

S.V.V.D.Jagadeesh

Tuesday, August 19, 2025

Scientific Data

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DATA VISUALIZATION

S.V.V.D.Jagadeesh

Tuesday, August 19, 2025

Scientific Data

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DATA VISUALIZATION

S.V.V.D.Jagadeesh

Tuesday, August 19, 2025

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.

S.V.V.D.Jagadeesh

Tuesday, August 19, 2025

Scientific Data

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Tuesday, August 19, 2025

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.

S.V.V.D.Jagadeesh

Tuesday, August 19, 2025

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.

S.V.V.D.Jagadeesh

Tuesday, August 19, 2025

Multivariate Data

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S.V.V.D.Jagadeesh

Tuesday, August 19, 2025

Multivariate Data

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DATA VISUALIZATION

• 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

S.V.V.D.Jagadeesh

Tuesday, August 19, 2025

Multivariate Data

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S.V.V.D.Jagadeesh

Tuesday, August 19, 2025

Multivariate Data

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DATA VISUALIZATION

• 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.

S.V.V.D.Jagadeesh

Tuesday, August 19, 2025

Graph Data

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• 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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S.V.V.D.Jagadeesh

Tuesday, August 19, 2025

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).

S.V.V.D.Jagadeesh

Tuesday, August 19, 2025

Graph Data

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Tuesday, August 19, 2025

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.

S.V.V.D.Jagadeesh

Tuesday, August 19, 2025

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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S.V.V.D.Jagadeesh

Tuesday, August 19, 2025

Summary

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DATA VISUALIZATION