CitiViz: A Visual User Interface to the CITIDEL System ECDL 2004, Bath, England, September 2004...

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CitiViz: A Visual User Interface to the CITIDEL System ECDL 2004, Bath, England, September 2004 Nithiwat Kampanya, Rao Shen, Seonho Kim, Chris North, and Edward A. Fox [email protected] http://fox.cs.vt.edu

Transcript of CitiViz: A Visual User Interface to the CITIDEL System ECDL 2004, Bath, England, September 2004...

Page 1: CitiViz: A Visual User Interface to the CITIDEL System ECDL 2004, Bath, England, September 2004 Nithiwat Kampanya, Rao Shen, Seonho Kim, Chris North, and.

CitiViz:A Visual User Interface to the

CITIDEL System

ECDL 2004, Bath, England, September 2004

Nithiwat Kampanya, Rao Shen, Seonho Kim, Chris North, and

Edward A. [email protected] http://fox.cs.vt.edu

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Acknowledgements (Selected) Sponsors: ACM, NLM, NSF (esp. grants CDA-9303152,

9312611; DUE-0121679, 0136690; IRI-9116991) Faculty/Staff: Lillian Cassel, Debra Dudley, C. Lee Giles,

Lenwood Heath, John Impagliazzo, Deborah Knox, JAN Lee, Manuel Perez, Naren Ramakrishnan, …

VT (Former) Students: Abhishek Agrawal, Supriya Angle, Guillermo Averboch, Anil Bazaz, Dennis Brueni, Robert France, Debby Hix, Marcos Goncalves, Aaron Krowne, Paul Mather, Kate McDevitt, Fernando Das Neves, Lucy Nowell, Durgesh Rao, Ryan Richardson, Hussein Suleman, Bill Wake, Jun Wang, Baoping Zhang, Jianxin Zhao

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Outline

Envision CITIDEL Other Related Works Research Questions CitiViz Homepage, Architecture Visualization Strategies, Examples Evaluation Conclusions, Future Work

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ENVISION

NSF “A User-Centered Database from the Computer Science Literature” (1991-93) With ongoing support from ACM

Collected bib/typesetter data, converted to SGML Scanned thousands of page images MARIAN search engine –

also applied to the Virginia Tech library catalog used as part of a prototype object-based DL with tailored visualization interface (L. Nowell dissertation)

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Envision Results Window

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Envision – Newer Version

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Envision – Newer Version – w. clusters

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Computing and Information Technology Interactive Digital Educational Library (CITIDEL)

Domain: computing / information technology

Genre: one-stop-shopping for teachers & learners: courseware (CSTC, JERIC), leading DLs (ACM, IEEE-CS, DB&LP, CiteSeer), PlanetMath.org, NCSTRL (technical reports), …

Submission & Collection: sub/partner collections www.citidel.org www.nsdl.org

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www.CITIDEL.org

Led by Virginia Tech, with co-PIs: Fox (director, DL systems) Lee (history) Perez (user interface, Spanish support)

Partners College of New Jersey (Knox) Hofstra (Impagliazzo) Villanova (Cassel) Penn State (Giles)

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CITIDEL Technology Features•Component architecture (Open Digital Library – Hussein Suleman)

•Re-use and compose re-deployable digital library components.

•Built Using Open Standards & Technologies

•OAI: Used to collect DL Resources and DL Interoperability

•XSL and XML: Interface rendering with multi-lingual community based translation of screens and content (Spanish, …)

•Perl: Component Integration

•ESSEX: Search Engine Functionality

•Fast, in-memory processing, snap-shots for persistence

•Multi-scheming

•Integrates multiple classifications / views through maps, closure

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Related Works

1st type of visualization techniques Predefined document attributes (e.g., author,

date, …) Envision

Semantic information (e.g., categories assigned to each document) Cougar, Cat-a Cone, Map.net ……

Document-query relevance TileBars, VIBE

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Related Works

2nd type of visualization techniques Automatically derive a collection overview via the

use of text mining Based on inter-document similarities

Scatter/Gather Grouper Galaxy of News Vivisimo Kartoo ……

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Research Questions

How to combine the two different types of visualization techniques for CITIDEL?

What text mining technology to use for post-retrieval analysis?

What are the key insights, how to support them?

What interaction and navigation strategies should be used to facilitate visual browsing and analysis?

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Addressing the Questions

1. Developed clustering components to discovery documents relationships and to identify subject categories for retrieved documents.

2. Developed a new visual interface:

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CitiViz HomePagehttp://feathers.dlib.vt.edu/CitiViz/index.html

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System Architecture

Component based design Communication between components is

XML based.

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System Architecture

Visualizing Components

Data Source Components Clustering Components

Java Servlets

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CitiViz Visualization Strategies

Overview strategy Aggregation by document clustering to show all the

retrieved documents Navigation strategies

Overview + detail Focus + context (Fish-eye view: hyperbolic tree) Combine tree graphs with scatter plot graphs. Integrate 2D scatter plot graph with a network of citations. Apply the aggregate towers technique to solve occlusion

problems of document visualized in the scatter plot graph.

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CitiViz Display of Detailed Information for a Selected Document: A Tower of Cylinders (to solve occlusion problem)

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CitiViz initial interface

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1. Show me retrieved results from ACM DL

Page 29: CitiViz: A Visual User Interface to the CITIDEL System ECDL 2004, Bath, England, September 2004 Nithiwat Kampanya, Rao Shen, Seonho Kim, Chris North, and.

1. Show me retrieved results from ACM DL

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2. “algorithm analysis”, by “Donald Knuth”

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2. “algorithm analysis”, by “Donald Knuth”

Clustering results

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2. “algorithm analysis”, by “Donald Knuth”

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3. “data compression”

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3. “data compression”

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Evaluation Tasks

1. Given an author and a topic, find a document published by that author and belonging to that topic.

2. Given an author and a publication year, find a document published by that author and in that year.

3. Given a title, find a document having that title.

4. Find the most recently published paper.

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Evaluation Results

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Discussion

Users performed chosen tasks faster with CitiViz than with the standard interface for CITIDEL.

No significant difference for tasks 1 and 3 between CitiViz when using clustering versus when using ACM classification

Possible explanation of differences observed with tasks 2 and 4: the clustering yields one level towers, and some users were confused about the multi-level

towers resulting from the ACM classification

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Conclusions

Text mining + information visualization Document clustering provides insights for users. Overview of document attributes in the 2D scatter

plot Overview of hierarchical concept map displayed as

a hyperbolic tree supports “focus+context” navigation.

Integrated the 2D scatter plot space with a network of citations.

Online tutorial and system – also animation.

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Future Work

Add more Data Source Components (DSC) Current DSC for CITIDEL = DSC for all its member DLs DSC: send query, parse HTML to XML, cluster result data Develop other DSCs for different DLs (e.g., NDLTD)

Improve clustering component (S. Kim) Extend CITIDEL content Test usability of CitiViz with broad base of users

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Summary

Envision CITIDEL Other Related Works Research Questions CitiViz Homepage, Architecture Visualization Strategies, Examples Evaluation Conclusions, Future Work