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