14. Information Search and Visualization

Introduction information retrieval, database management  information gathering, seeking, filtering, or visualization data mining from data warehouses and data marts  knowledge networks or semantic webs information search using traditional UI – hurdle for novice users and an inadequate for experts Improvements on traditional text and multimedia searching seem possible as a new generation of visualization strategies for query formulation and information presentation emerges task actions (browsing or searching) represented by interface actions (scrolling, zooming, joining, or linking) Tasks – specific/extended fact finding, exploration of availability, open-ended browsing and problem analysis

Searching in Textual Documents and Database Querying search engine SQL – requires training, and even then users make frequent errors natural-language queries – appealing but limited computer processing capacity form-fillin queries and query-by-example simple and advanced search interfaces (fig. 14.1) five-phase framework Formulation: expressing the search  source, fields, phrases, variants Initiation of action: launching the search  explicit, implicit initiation, dynamic query Review of results: reading messages and outcomes  sequence and cluster Refinement: formulating the next step  history buffer Use: compiling or disseminating insight

Multimedia Document Searches Image search -- query by image content (QBIC)  search for distinctive features or search for distinctive colors Map search – search by features Design or diagram search – finding engine designs with pistons smaller than 6 cm Sound search – Music-information retrieval system Video search Animation search

Advanced Filtering and Search Interfaces filtering with complex Boolean queries - difficulty of use automatic filtering - user constructed set of keywords to dynamically generated information dynamic queries - direct manipulation queries faceted metadata search - integrating category browsing with keyword searching collaborative filtering - each user rates items, and then system suggest unread items multilingual searches visual searches -

Information Visualization How to present and manipulate large amounts of information in compact and user-controlled ways Information visualization - the use of interactive visual representations of abstract data to amplify cognition Resistance to visual approach - textual tools use compact presentations that are rich with meaningful information and comfortingly familiar visual-information-seeking mantra – overview first, zoom and filter, then details on demand Data type by task taxonomy (TTT) and seven tasks (Box 14.2)

Information Visualization 1-D 1inear data in a sequential manner – textual documents, dictionaries, alphabetical list of names interface-design issues include what fonts, color, size to use, and what overview, scrolling, or selection methods to provide for users 2-D map data maps, floor plans, newspaper layouts interface-domain features (size, color, opacity) user tasks – to find adjacent items, regions containing items, paths between items and to perform the seven basic tasks

Information Visualization 3-D world data Computer-assisted medical imaging, architectural drawing, mechanical design, chemical structure modeling, and scientific simulations users’ tasks typically deal with continuous variables such as temperature or density cope with the position and orientation when viewing the objects  potential problems of occlusion and navigation  overviews, landmarks, teleoperation, multiple views and TUI Multidimensional data n attributes in a n-dimensional space tasks include finding patterns such as, clusters, correlations, gaps and outliers three-dimensional scattergram (disorientation and occlusion)

Information Visualization Temporal data items have a start and finish time, and that items may overlap finding all events before, after, or during time period and the seven basic tasks Tree data Treemap Network data shortest or least costly paths connecting two items or traversing the entire network

Information Visualization Overview task zoom-out views of each data type to see the entire collection plus detail view movable field-of-view box (zoom factors of 3 to 30), fisheye strategy Zoom task to control zoom focus and zoom factor Filter task sliders, buttons, or other control widgets coupled with rapid display update

Information Visualization Details-on-demand task simply click on an item to get a pop-up window with values of each of the attributes Relate task proximity, containment, connection, color coding; highlighting History task history of actions to support undo, replay, and progressive refinement Extract task extract , save, send by electronic mail, insert, publish

Information Visualization Challenges for information visualization import data combine visual representations with textual labels see related information view large volumes of data integrate data mining collaborate with others achieve universal usability