14. Information Search and Visualization. Introduction information retrieval, database management information gathering, seeking, filtering, or visualization

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

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 buffer5. Use: compiling or disseminating insight

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

Multimedia Document Searches

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)

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


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)


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


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


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


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
