Document (Text) Visualization

Mao Lin Huang

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

Document Lens

Why: -Text too small to read but yet needed to perceive patterns. - Perspective wall wastes corner areas of screen

What: General visualization technique based on a common strategy for understanding paper documents when their structure is not known.

How: 3D Visualization Tool For Large Rectangular Presentations

Document Lens Features

Lens – rectangular – interested in text that is mostly rectangular

Sides are elastic and pull the surrounding parts towards the lens creating a pyramid

Document Lens

Document Lens