Transcript chap14

CS3041 – Final week
• Today: Searching and Visualization
• Friday: Software tools
– Study guide distributed (in class only)
• Monday: Social Imps
– Study guide review
• Tuesday: Final Exam
• Thursday: UI in Games (optional)
– Final project due
Chapter 14
Information Searching and
Visualization
Searching
• Many Forms of Information Search
– Searching text and database
– Multimedia documents
– Data Visualization
• Different levels of searching
– Specific fact finding
– Extended fact finding
– Information availability
– Open-ended browsing
Searching Text and Databases
• Simple case, general keyword search
– Google, Yahoo, Lycos
– Users often have problems with high volumes
of returned data
• SQL
– Powerful tool for data mining 'experts‘
• Natural language queries
– Ask Jeeves
• Form-fillin queries
Five Phase Fact Finding Framework
• Formulation
– Identify data source, search criteria
• Initiation of action
– Explicit (button) or implicit (immediate)
• Review of results
– Typically a results overview
• Refinement
– Adjust keywords / criteria, drill down
• Usage
– Export results for later use / sharing
Multimedia Documents
• Much harder problem than text
– Often relies on metadata
– Automatic recognition requires many auxiliary
technologies (image processing, speech to text)
• Some common search types
–
–
–
–
–
–
Images (KimDaBa)
Maps (Mapquest)
Design / diagram (AutoCAD)
Sound
Video
Animations (Disney internal animation tools)
Example: KimDaBa
• "KimDaBa or KDE Image Database is a
tool which you can use to easily sort your
images.“
– Keyword / metadata browser
Example: KimDaBa
• Search criteria
Visual browsing
Filtering and Search Interfaces
• Filtering with complex Boolean queries
– Users often trip here because of the
difference between natural language vs
boolean algebra
• "List all employees who live in Boston and New
York“
– In language, AND = inclusion
– In boolean logic, AND = refinement
• "I'll eat pepperoni or sausage pizza“
– In language, OR = exclusion
– Boolean, OR = inclusion
Filtering and Search Interfaces
• Automatic filtering
– Applying user-constructed criteria to dynamic
information
• Spam filters
Filtering and Search Interfaces
• Dynamic queries
– Adjusting interface controls via direct manipulation
and displaying the results immediately ( < 100 ms)
– Facilitates data exploration
• Collaborative filtering
– Users rate results
– Tivo uses this ("Thumbs up" vs "Thumbs down")
• Multilingual searches
• Visual searches
Filtering and Search Interfaces
• Dynamic searching
– Spotfire visualization tool
Filtering and Search Interfaces
• Visual searches
– Airplane seat selection
Information and Data Visualization
• Visualization is an area of research that aims to
let users visually explore large data sets, looking
for patterns and relationships
– A picture is worth 1K words
– An interface is worth 1K pictures
• Visual data mining
– People are good at visual pattern matching
• Visual information seeking mantra:
– Overview first, zoom and filter, then details on
demand (times7)
Information and Data Visualization
• Data types by task taxonomy
– 1D Linear
• text, sequences
– 2D Map
• geographic, blueprints
– 3D World
• Medical, CAD/CAM
–
–
–
–
Multidimensional
Temporal
Tree
Network
Information and Data Visualization
• Multidimensional Data
– Any data set with n attributes, where n > 3
– N-d tools need to support a wide variety of tasks
• Finding patterns
• Identifying correlations, clusters, gaps, outliers
– Lots of different techniques
• Scatterplots
• Glyphs
• Dimensional stacking (  Jeff’s thesis  )
– (1pt extra credit on the final if you find the title)
• Parallel coordinates
Information and Data Visualization
• Parallel coordinates example
– XmdvTool from WPI
Information and Data Visualization
• Data visualization tasks
–
–
–
–
Overview: Gain an overview of the entire collection
Zoom: Zoom in on items of interest
Filter: Filter out uninteresting items
Details on demand: Select an item or group and get
details when needed
– Relate: View relationships among items
– History: Keep a history of actions
– Extract: Allow extraction of subcollections and of the
query parameters
Information and Data Visualization
• Challenges for information visualization
tools:
– Standardized data import
– Combining visual representations with text
– Viewing related information
– Viewing large volumes of data
– Support data mining
– Collaboration
– Universal usability