cs3041 – final week today: searching and visualization friday: software tools –study guide...

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


• Automatic filtering– Applying user-constructed criteria to dynamic

information• Spam filters


• 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


• Dynamic searching– Spotfire visualization tool


• 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)


• Data types by task taxonomy– 1D Linear

• text, sequences

– 2D Map • geographic, blueprints

– 3D World• Medical, CAD/CAM

– Multidimensional– Temporal– Tree– Network


• 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


• Parallel coordinates example– XmdvTool from WPI


• 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


• 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