visualizating the non-visual: spatial analysis and interaction with information from text documents...

Visualizating the Non-Visual:Spatial Analysis and Interaction with

Information from Text Documents

J.A. Wise, J.J. Thomas, K. Pennock,

D. Lantrip, M. Pottier, A. Schur, and V. Crow

Bongshin LeeApril 4, 2001

April 4, 2001 CMSC838b Information Visualization 2

Paper Outline

• Introduction

• Visualizing text

• Visualization transformations: from text to pictures

• Examples from the MVAB Project

• Conclusions and directions for future research and development


Introduction

• Current Visualization approaches– For visualizing mostly structured and/or hierarchical

information

• Some research in information retrieval– Utilized graph theory or figural display– Information returned is documents in text form

• Users still have to read• Causes a severe upper limit

• Open Source digital information– Available text overwhelms the traditional reading

methods of inspection, sift and synthesis


Visualizing text

• True text visualizations– Must represent textual content and meaning without the

user having to read it

– Result from content abstraction and spatialization of the text document

• Use primarily preattentive, parallel processing powers of visual perception

• Goal is to spatially transform text information into a new visual representation


Visualization transformations: from text to pictures

• Four important technical considerations – Clear definition of text

• what comprises text

• how it can be distinguished from other symbolic representations

– Way to transform raw text into a different visual form

– Foundation for meaningful visualization• Suitable mathematical procedures and analytical measures

– A database management system


Processing Text

• Requirements of text processing engine– Identification and extraction of text features

• Frequency-based measures on words

• Higher order statistics taken on the words

• Semantic in nature

– Efficient and flexible representation of documents in terms of these text features

– Support for information retrieval and visualization• Pre-process, indexing


Visualizing output from text processing

• Representing the document– a vector in high dimensional feature space

• Comparisons, filters, and transformations can be applied

• Clustering using the normalized document vectors• Projection

– Principal Components Analysis– Multi-Dimensional Scaling– Exponential order of complexity

• Clustering in the high-dimensional feature space• Visualize the cluster centroids


Managing the representation

• Two basic classes of data– Raw text files

• Static in nature, Simple in structure• Easy to manage

– Visual forms of the text• Extensive and dynamic

• Object-Oriented Database– Flexibility of data representation– Power of inheritance– Ease of data access


Interface design for text visualization

• Backdrop– Central display resource

• Workshop– Grid having resizable windows to hold multiple

views

• Chronicle– Area where views are placed and linked to form

a visual story


Examplesfrom the MVAB Project

• MVAB– Multidimensional Visualization and Advanced

Browsing Project– Visualization and analysis of textual information– Showcased in SPIRE

• SPIRE– Spatial Paradigm for Information Retrieval and

Exploration

• Starfields and Topographical maps metaphors– Galaxies and Themescapes


Galaxies

• Displays cluster and document interrelatedness

• 2D scatterplot of ‘docupoints’

• Simple point and click exploration

• Sophisticated tools– Facilitate more in-depth analysis– Ex) temporal slicer


Galaxies Screen Shot


ThemeScapes

• Abstract, 3D landscapes of information

• Convey relevant information about topic or themes without the cognitive load

• Spatial relationships reveal the intricate interconnection of thems


ThemScapes - Advantages

• Displays much of the complex content of the document database

• Utilizes innate human abilities for pattern recognition and spatial reasoning

• Communicative invariance across levels of textual scale

• Promote analysis


ThemeScapes Screen Shot


Conclusions

• Text visualizations can overcome much of the user limitations– Enhanced insight and time savings (35 mins vs 2

weeks)– Creative with the tool

• Querying and analytical manipulation come together in a single visualization– Permits a different kinds of querying

• Text visualizations will have to access and utilize the cognitive and visual processes


Directions for Future R & D

• Visual Data Analysis

• Elaborate the visual metaphors

• Addition of sensory modalities– Virtual interaction


My Favorite Sentence

The bottleneck in the human processing and understanding of information in large amounts of text can be overcome if the text is specialized in a manner that takes advantage of common powers of perception.


Contributions

• Explorations of new visualizations

• Discussion of the process for mapping Raw Data Document collections into visualizations


Notes on the Reference

• Designing Interaction: Psychology at the Human Computer Interaction

• Interfaces Issues and Interaction Strategies for Information Retrieval Systems

• Clustering and Dimensionality Reduction in SPIRE


Critique – Strengths and Weaknesses

• Strengths– Provide natural visual metaphors– Enable the users to see the relationships

between documents with minimal required reading

• Weaknesses– No validation of some conclusions


What has happened to this topic?

• 1996 R&D 100 Award• OCSB

– On-line Citation Searching and Browsing in UMD

• "ThemeScape" is now a trademarked term of Cartia, Inc.

• WebThemeTM – an interactive tool that provides a visual display of the

common themes in collections of web-based documents


WebTheme Screen Shot

visualizating the non-visual: spatial analysis and interaction with information from text documents...

Documents

text information

text documents

text extensive

information retrieval

text features support

text form users

visualization preprocess

figural display information