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) 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