Transcript 슬라이드 제목 없음 - Korea University

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, openended 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
1. Formulation: expressing the search  source, fields, phrases,
variants
2. Initiation of action: launching the search  explicit, implicit
initiation, dynamic query
3. Review of results: reading messages and outcomes  sequence
and cluster
4. Refinement: formulating the next step  history buffer
5. 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. 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. 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. 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
4. 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
5. 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
6. Tree data
 Treemap
7. Network data
 shortest or least costly paths connecting two items or traversing the entire
network
Information Visualization
8. 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
9. Zoom task
 to control zoom focus and zoom factor
10.Filter task
 sliders, buttons, or other control widgets coupled with rapid display update
Information Visualization
11.Details-on-demand task
 simply click on an item to get a pop-up window with values of each of the
attributes
12.Relate task
 proximity, containment, connection, color coding; highlighting
13.History task
 history of actions to support undo, replay, and progressive refinement
14.Extract task
 extract , save, send by electronic mail, insert, publish
Information Visualization
14.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