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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