91.541 data visualization spring 2006rosane/haim_lecture1_2006-08-08_2ppg.pdf · • pipeline •...

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Haim Levkowitz & Georges Grinstein

Olsen 301

{haim, grinstein}@cs.uml.edu

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Data Visualization

Spring 2006

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One Look is Worth a Thousand WordsOne Look is Worth a Thousand Words

• Fred R. Barnard, in Printers' Ink, 8 Dec., 1921, p. 96

• He changed it to "One picture is worth a thousandwords" in Printers' Ink, 10 March 1927, p. 114, andcalled it "a Chinese proverb, so that people wouldtake it seriously."

• It was immediately credited to Confucious

• This establishes the link between the two ads, butmany sources misquote the 1927 advertisement bycopying "a thousand" from the 1921 advertisementinstead of replacing it by "ten thousand"

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• Part 1 - Visualization techniques (2 weeks)

– Introduction and goals

– History of visualization and techniques

– Computer Graphics

– Graphics and Visualization Pipelines

• Part 2 – The User (1-2 weeks)

– Perception (visual, aural, tactile, haptic, …)

– Illusions

OutlineOutline

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• Part 3 – Reference Models (2 weeks)

– Visualization pipeline

– Data, metadata, operations, mappings

– Visualization taxonomies and reference

models

– Visualization Theory

OutlineOutline

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• Part 4 – Techniques and Tools (5 weeks)

– Spatial

– Non-spatial

– Graphs and Networks

– Special

– Very high-dimensional

and some of their interactions

and their computations (operators)

– Data manipulation and mining

– Custom domain systems example

OutlineOutline

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• Part 6 – Interaction Theory (1 week)

– Operators

– Styles

– Techniques

• Part 7 – Utility, Usability and Effectiveness (1 week)

– Design process

– Evaluation

• Part 7 – Frameworks (2 weeks)

– Components, features, limitations, assumptions

– Application examples

– Futures

OutlineOutline

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Introduction and GoalsIntroduction and Goals

• Look at history of Computer Graphics and

Visualization (ScDV, InfoVis, mDV, EDA)

• Understand the issues in interactive data

visualization

• Examine numerous visualization

techniques, interactions, and systems

• Be able to implement visualizations within

a variety of frameworks and systems

• Explore the future of visualization

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Why Graphics or Visualization?Why Graphics or Visualization?

• To help the user

– See (understand)

– Remember

– Compute

– Analyze

– Discover

– Enjoy

– …

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VocabularyVocabulary

• Data

• Information

• Knowledge

• Visualization

• Data exploration

• Databases

• Data analysis

• Knowledge discovery

• Data mining

• Computer vision

• Perception & cognition

• Graphics

• Display list

• Frame buffer

• Rendering

• Imaging

• Filtering

• Pipeline

• Input/output devices

• Human interface

• Multimedia

• Virtual reality

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BackgroundBackground

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Goals of Visualization TechniquesGoals of Visualization Techniques

Have no

hypotheses about

the data

Have some

hypotheses about

the data

Facts to be

presented are

known (these may

not represent the

truth)

Start

Exploratory

Analysis

Confirmatory

Analysis

Presentation

ResultProcess

Visualization of

data to lead to

hypotheses about

the data

Interactive usually

undirected search

for structures,

trends, patterns or

anomalies

Visualization of

data to confirm,

accept or reject the

hypotheses

Goal oriented

examination of the

hypotheses

High-quality

visualization of the

data and analysis

to present facts

(often without the

author’s presence)

Choose and tune

appropriate

visualization

technique

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The Knowledge Discovery ProcessThe Knowledge Discovery Process

Decisions

Tools

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• Data Exploration is the process of

searching and analyzing databases to

discover implicit but potentially useful

information

Data ExplorationData Exploration

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• Convey information

• Discover new knowledge

• Identify structure, patterns, anomalies,

trends, relationships

Data Information Knowledge

Goals of Data ExplorationGoals of Data Exploration

For decision

support!

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Data Mining

Database

TechnologyStatistics

Other

Disciplines

Information

Science

Machine

Learning (AI)Visualization

A Confluence of Multiple DisciplinesA Confluence of Multiple Disciplines

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Final Model

User Requirements

and

User Interactions

Data

Visualization

Parameter

Visualization

Pattern

Visualization

Model

Visualization

Algorithm Engineering

Algorithm Selection

Data Engineering

Problem Formulation

Model Validation

Pattern Evaluation

Model Testing

Model Enhancement

Raw Data

Transformed Dataset

Selected Algorithm

Induced Model

Patterns, Statistics

Measure of Goodness

Patterns, Statistics

User Interactions

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Data Mining Tasks & TechniquesData Mining Tasks & Techniques

Major Techniques

• Linear Regression Trees

• Non-Linear Regression

• MARS

• Naïve Bayes

• K-Means and K-Median

• Neural Networks

• Association Rules

• Decision Trees

• Principal Curve Analysis

• Support Vector Machines

• Genetic Algorithms

Major Data Mining Tasks

• Summarization

• Association

• Classification

• Prediction

• Clustering

• Time-Series Analysis

using

based onStatistical Tools

• Missing Value Imputation

• Normalizations

• Error & Variational Analysis

• Confidence Estimates

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Why so many?Why so many?

• Almost all tasks are NP-hard!

• KDD2001 CUP

– Thrombosis data set

– Over 200 submissions

– Over 100 different techniques

– Many combined techniques

• KDD2002 CUP

– Creativity

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Pure• 2D and 3D Scatterplots

• Matrix of Scatterplots

• Statistical Charts

• Line and Multi-line Graphs

• Parallel Coordinates

• Circle Segment

• Polar Charts

• Survey Plots

• Heatmaps

• Height Maps

• Iconographic Displays

• RadViz

• PolyViz

Integrated with Analysis• Projection Pursuit

• Dimensional Stacking

• Sammon Plots

• Multi-Dimensional Scaling

• PCA and Principal Curves

• Self Organizing Maps

Interactions• Selection

• Probing, Querying

• Grand Tours

• Non-linear Zooms

Visualization TechniquesVisualization Techniques

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The Visualization ProblemThe Visualization Problem

• Massive amounts of data from

–databases

–simulations

–sensors

–decision systems

• Limited screen space

• Little is known about the human

perceptual system and information

transfer

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What is Visualization?What is Visualization?

• Visualization is a method of computing. It

transforms the symbolic into the geometric,

enabling researchers to observe their

simulations and computations.

Visualization offers a method for seeing the

unseen. (from McCormick87)

• Visualization now includes other data

representations

–Aural (auditory), haptic and tactile, …

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• It is the Visual Interface to the Data and the Mining tools

• It is a method of interacting with the data and algorithms

— supports the user through all the knowledge

discovery steps

— uses selections, queries, probes, and view

transformations

• It is completely separable from the analysis methods

— Data can be analyzed using many different algorithms

— Each result can be viewed in a different visualization

— Each visualization provides a different view of the results

A Definition of VisualizationA Definition of Visualization

Galileo

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• Very large number of parameters

–more than 100

• Very large data sets

–more than 107

• Multiple data types

–discrete and continuous

• Noisy data

–often not uniform

• Missing values

–could be important

• Lots of different tasks

What are the Key Data Factors?What are the Key Data Factors?

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The Great Demand for VisualizationThe Great Demand for Visualization

• Fueled by technological advancements

–Displays

–High performance computers

–Large storage systems

–Personal computers

–Sensor technology

• Fueled by user awareness

–Interfaces

–Programming tools

–Flexibility

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Global Computing ApplicationsGlobal Computing Applications

• 48-hour Weather Forecast

• 2D Airfoil

• Oil Reservoir Model

• Climate Monitoring

• Vehicle Signature

• Plasma Modeling

• Chemical Dynamics

• Stock Market Prediction

• WWW

• Drug Discovery

• Security (data and human)

1980s

1990s

2000s

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Very High > 1000

High 1000

Medium 100

Low 10

Dimensionality# of Variables

What is High Dimensional?What is High Dimensional?

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Low DimensionalLow Dimensional High Dimensional

A Complete Data ViewA Complete Data View

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DatabaseMetaData

DatabaseView Table

mapping and

display functions

querypreprocessor

Databases

Retrieved database subsets

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Visualization

Subsystem

Database

Visualization Interface

Database ManagementSubsystem

Visualization ArchitectureVisualization Architecture

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simulated or

sampled data

derived or

massaged data

logical data

representation

data transformations -

interpolation, filtering, etc.

representation mappings -

geometry, color, sound, etc.

Image

rendering -

viewing, shading,

device transforms, etc.

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B

M

S

USER

queries and probes

The Visualization PipelineThe Visualization Pipeline

Interactions with a DBMS ViewInteractions with a DBMS View

UserUser

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• Exploratory Visualization– Dynamic, relatively

unpredictable

– User searches for structure,trends, etc.

– Generating hypotheses

• Confirmatory Visualization– More stable and predictable

– Predetermined systemparameters

– Confirm or refute hypotheses

• Production Visualization– Most stable and predictable

– Fine-tune system parameters

– Already Validated hypothesesFocusVisualization DBMS

Visualization Interaction StylesVisualization Interaction Stylesand the integration of database and visualization technologiesand the integration of database and visualization technologies


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History of VisualizationHistory of Visualization

And Techniques

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• Pictures

– From hieroglyphics to spreadsheets

– From lines to surface and volumes

– From scatterplots to HDVs

– From static to dynamic images

– From simple to complex integratedanalysis

• Slides

5000 BC

2000 AD

A History of VisualizationA History of Visualization


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1-10 Variables1-10 Variables

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MapsMaps

• Valuable

– Save time, money, lives

• Anchoring image

– Experience base

– Reasoning base

• Understandable

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Snow’s Map of

Cholera Deaths

in London

2 Dimensions

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Visualization FuelsVisualization Fuels

• Military

• Aerospace and Automotive

• Entertainment

• Scientific Data Visualization

• GIS

• Floods of Data


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NASA MovieNASA Movie

Classic Science

– Build Model

– Validate Model using Real Data

– Repeat

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Aircraft Data• Velocity = 165 knots

• Wing Area = 29 m2

• Wing Span = 16 m

• Mean Aerodynamic Chord = 2 m

• Weight = 8000 kg

• Chord Reynolds Number = 1.18x107

AerospaceAerospace

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Computer-Aided DesignComputer-Aided Design

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Computational Support and StatisticsComputational Support and Statistics

• Support tools for scientific visualization

• Support tools for CAD, CAM, CAE, …

• Statistics for social science data files

• Statistics for databases

• Modeling data

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Computational SupportComputational Support

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Statistics for Files and DatabasesStatistics for Files and Databases

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New AreasNew Areas

• Entertainment

• Medicine

• Architecture

• Art

• Internet

• Public Demand

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Film and EntertainmentFilm and Entertainment

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• Head, including cerebellum

• Cerebral cortex, brainstem

• Nasal passages from Head subset

Section of the Visible HumanSection of the Visible Human

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HIV-I TargetHIV-I Target

IBM, Data Explorer Binding of the drug TIBO-R86183

to specific pocket of HIV-I enzyme

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DNA Electron MicroscopyDNA Electron Microscopy

Bacterial RecA and eukaryotic Rad51

Proteins form similar filaments on DNA

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ESP

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0.05

0.00

- 0.05

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Gram of Fat

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Homework linksHomework links

the aesthetics + computation group

http://acg.media.mit.edu/

Processing language and environment

http://processing.org/

91.541 data visualization spring 2006rosane/haim_lecture1_2006-08-08_2ppg.pdf · • pipeline •...

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