Information Search and Visualization

1. Information Search and Visualization Human Computer Interaction CIS 6930/4930 Section 4188/4186

2. Intro • How can we design interfaces to search through large amounts of data? • We’ll look at different approaches to sift through information • Old approach: Information Retrieval • New approaches: • Information Gathering • Seeking • Filtering • Visualization • Data mining and warehousing • Difficulty increases with data volume and diversity • Ex. Find a news story, find a picture • How can we design an interface for • New users (how do I express what I want? boolean operators are not that easy to use) • Experienced users (powerful search methods) • Use research from: • Perceptual psychologists, statisticians, graphics designers

3. Searching Textual Documents and Databases • Most widely used (and understood) • Web searches relevance still needs work • To satisfy both users • create two interfaces (advanced and basic) • Multilayer interface • User satisfaction increases with more search control (Koenemann ’96) • Clustering into meaningful hierarchies might be effective (Dumais ’01)

4. Multimedia Searches • Currently: Requires metadata (captions, keywords, properties) • Query by Image Content (QBIC) – Find pictures of the Florida Gators Football team (w/o using descriptors, webpage info, etc.) • Approaches: • Search for distinctive features • Give example images • Image spaces • Major research area • Best: restrict database if possible (like medical, etc.) • Map Search • Easy: search by lat and long • Harder: search by features (find all cities near a seaport and a moutain > 10000 ft) • App: Find businesses for mobile GPS systems • Design or diagram search • CAD models, engineering apps • Ex: Find 6 cylinder engine designs with pistons > 6 cm • Some basic structured document searching for things like newspapers and magazine layouts

5. Multimedia Searchse • Sound Search • Music-information retrieval (MIR) • New approaches: • Query with musical content (Hu ’02) • Query by recognized patterns like singers • Using speech recognition and TTS as inputs to audio databases • Video Search • Only preliminary research on this topic • Infomedia (screenshot) uses visual features + text (TTS) for esarching • Currently: show timeline to allow quick browsing of contents • Animation Search • Untapped, but growing need • Might be easier with standard definitions like Flash

6. Advanced Filtering and Searching Interfaces • Alternatives to form-fillin • Filter with complex Boolean queries • Research into how we can make them easier to specify • Difficulty is in the colloquial use of English (all classes in weil and NEB, or I’ll take ketchup or mustard) • Novel metaphor approaches (doesn’t scale well) • Venn diagrams • Decision Tables • Water through filters • Aesthetic Computing (screenshot) • Automatic Filtering • Users create rules for data • Ex. Email filters, news stories filters • Similar to : Selective Dissemination of information (SDI) • Dynamic Queries • Adjusting numerical sliders (www.bluenile.com) • Appealing and easy to understand • a.k.a. Direct-manipulation queries (objects, [rapid, reversable, immediate] actions, feedback) • Reduces errors and encourages exploration • Large databases can give previews given the user defined ranges (Fig. 14.8) • Although having such large ‘hits’ might seem poor, (Tanin ’00) showed 1.6 to 2.1 performance and satisfaction increase • Faceted metadata search • Combine category browsing with dynamic previews (Yee ’03) • Search on a topic (car price), then restrict on feature (car year), then on # of doors, then widen on all years • Collaborative Filtering • Groups of users to combine evaluations to find interesting results • Amazon.com’s lists or “other people who bought this item also bought…” • Good for organizational databases, news files, music, shopping • Multilingual Searches • Research areas to use perhaps restricted domain-specific translation dictionaries (like medical ones) • Visual Searches • Use visual representations like maps instead of text lists to select and refine searches • Trees to represent product catalogs • Very powerful. (Also the calendar and plane layout methods) • User error reduced • User satisfaction (thoroughness)

7. Information Visualization • Visualize information data in novel methods to amplify cognition (Card ’99, others) • Different the scientific viz because of the abstract nature • Goal: Present compact graphics representations and user interface for manipulating large # (or subset) of items • Visual data mining • Apply visual bandwidth (which is very great) and human perception • Make discoveries, decisions, hypothesis • Underutilized in most interfaces • Humans are good at: • Detecting patterns • Recall images • Detect subtle changes in size, color, shape, texture • Research: new dynamic info viz. Go beyond icons and illustrations

8. Info Viz • Provide useful tools to allow users to find trends in data • Go beyond novelty and address true business concerns • Share insights easily with others • Some user resistance (esp. if text really is better!) • Solution: Measure benefits • Visual information mantra: • Overview first, zoom and filter, then details on demand • Figure out data type and task, then look at different current methods to visual them. Box 14.2 (pg 583)

9. Data Types • 1D Linear – • Ex: source code, text, audio • Info Viz approaches: zoom, color coding • Consider: Layout, color, size, overview approach • Tasks: Find # of items, changes • Apps: Document Lens, SeeSoft, Info Mural • 2D Map - Planar data • Ex: GIS, floorplans, newspaper layouts • Approaches: Multilayer (each layer is 2D), spatial displays • Consider: Data (name, owner, value) and interface (size, color, opacity) attributes • Tasks: Find adjacent items, regions, paths • Apps: GIS, ArcInfo, ThemeView • screen shots

10. Data Types • 3D World – more than just geometry • Ex: 3d molecules, body, buildings • Approaches: landmarks, overviews, multiple views, tangible UI • Consider: both geometry and relationships, navigation can be difficult for many • Tasks: focus on meta-relationship patterns • Apps: Medical imaging, CAM, chem structure, scientific sims, flythroughs • Making things 3d that aren’t or don’t fit well, doesn’t make the results better, could hamper performance • Screen shots

11. Data Types • Multidimensional data