Information Visualization:Information Visualization:Ten Years in ReviewTen Years in Review

Xia Lin

Drexel University

Before 1990Before 1990

Static graphical representation– Graphics are made, not generated– Graphics do not support interactions – Graphics illustrate the organization of information – Graphics are used to help the analysis of information structures

Examples:– Maps based on citation analysis– Semantic term relationships

– Semantic Net representation

Around 1990Around 1990

Scientific data visualizationPopularity of Macintosh and WindowsAvailability of computational power

MotivationsMotivations For data analysis

– Visual inspection of data properties – Dimensional deduction

For graphical representation of large amount of data– Clustering and grouping– Discovery of hidden internal structures

For visual interaction with the data– interactive online searching – browse large amount of information

MotivationsMotivations To utilize human perception for information seeking

– Human can apprehend relationships on graphics fast and sometime intuitively

– Human can understand graphical relationships that otherwise difficult to represent

To understand/reveal information structures– Understand information structures help online

searching and retrieval– Reveal semantic structures through graphical

representation

Around 1995Around 1995IV for IR starts to get popular before of

some web applications– HotSause– SemioMap– WebCutter (Mapuciino)– AltaVista’s LiveTopic– Xerox PARC’s research prototypes

ExpectationsExpectations

Most of these systems did not live up to their expectations– Limited success– No clear advantages over other approaches– Many are “for demonstration only”

not practical No instant mapping and visualizing Not easy to be understood by the user

LessonsLessons

Applications that “Look great” do not guarantee to have users.

Visualization tools should reduce, rather than adding cognitive loads to the searcher.

No one feels that he/she has to use these visualization tools yet.

ProblemsProblems

Precision and Clarity– If all details are shown, the result is confusion – If only selected details are shown, it may be

lack of precision needed.

Graphics are often not conclusive– subject to interpretation– subject to the cognition of the viewer.

Problems Problems Structures

– Structures help people understand.

– Structures also disorient people easily. Usefulness

– For what purposes is an application created?– For what purposes do people use the application?– How usefulness can be demonstrated?

No theories No experimental results No practical applications

A Successful StoryA Successful StorySpotfire

– Completed in 5 years from research prototypes to commercial products.

– Focused on data presentation for data analysis Deterministic, rather than fuzziness Usefulness, not just pretty pictures.

– Utilized simple functionalities Not the most advanced features Practical

A Developing StoryA Developing Story

Kohonen Mapping for Data Analysis– A banking report example– A drug treatment/development example– Marie Synnestvedt’s data

Baking Cabling MessagesBaking Cabling Messages FINCEN (Financial Crimes Enforcement Network)

receives thousands and thousands of messages each day from banks all over the world, which one deserves more attention? – Solution:

cluster messages identify trends Interact with the data

– Samples: Kohonen net input: 243 dimensions, 130 input vectors Kohonen net output: 14 by 14 Index parameters: words appear in at least four messages and no

more than half of the total input.

Map of the Suspicious Activity Reporting (SARS)Map of the Suspicious Activity Reporting (SARS)

Drug Treatment DataDrug Treatment DataWAR (Wyeth Ayerst Research)

– Desired to have a visualization tool for data exploration on experimental dr

– Complained about the limited exploration power of Spotfire.

– Sent me a sample data for mapping– When the mapping was completed, the director

was gone.

Data: – 8624 cases (patients)– 120 independent variables (treatments)– Kohonen output: 20 by 20

Marie’s DataMarie’s Data488 cases 12 Variables used for mapping: SiteExtrem SiteHead SiteTrunk SiteSubVol

ThickGroup1

ThickGroup2

ThickGroup3

ThickGroup4

Level2s

Level3

Level4

Level5

Level 3

Level 4

Site Trunk

Thick Group1

SiteExtrem

Thick Group4

Thick G2

Thick G3

SiteHead

Level 5

SiteSubVol

Our Current Projects: Our Current Projects: AuthorLink/ConceptLink AuthorLink/ConceptLink

Make it practicalMake it simple Make it useful

The purpose of visualization is INSIGHT, not pretty pictures.

Design Objective 1Design Objective 1

Develop visualization tools that work on real world data. – Working with data that have meaningful

structures Thesaurus Citations Document collections with good semantic structures

– Real time mapping Large databases, small visualization areas

Design Objective 2Design Objective 2

Develop tools for associative mapping– Analyze co-occurrence data

Co-citation counts Co-occurrence of terms in documents

– View the invisible– Reveal "the meaning of

associations" Without visualization, “the meaning” could be

hidden in the data.

Design Objective 3Design Objective 3

Develop practical visualization interfaces for information access. – Simple and Practical

Everyone can use it without much learning

– Useful Connecting to good resources

– Focus on contents Not pretty graphics No additional cognitive interpretations

Design Objective 4Design Objective 4Develop real time interaction for

information visualization– “drag-and-drop” from visual mapping to search

engines– “real-time” feed-back from search engines.– Mixed-initiative interaction

The search engine responses to what the user asks for. The search engine may also conduct searches before

the user asks it to to do.

Design Objective 5Design Objective 5

Develop a flexible system architecture for system integration and future expansion. – Design in Java– Develop a middle solution that might be ported

to other databases/search engines.

System Architecture System Architecture

BRS Search EngineWeb Server

Java Servlets

Web-based Map Interface

Java Applet

MappingProcedures

Application Server

OracleDatabases

PUBMED Search Engine

Concept MappingConcept Mapping

Author MappingAuthor Mapping

Future ResearchFuture ResearchBeyond interaction

– moving from interaction to cooperation and to collaboration.

Creating a culture and the environment for information visualization– user education– hardware and software improvement– Encouragement of graphical thinking