Transcript Soarian™ User Interface

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Information Search and Visualization
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Information Terminology
 Information Retrieval
 Information gathering, seeking, filtering, and visualization
 Task objects: e.g., video clips, documents
 Task actions: browsing and searching
 Interface actions: Scrolling, joining, zooming, linking
 Database Management – refers to structured relational database systems,
well defined attributes and sort-keys
 Data mining, data warehouses, data marts
 Knowledge networks, semantic webs
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Information Search and Visualization
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Information Terminology
 Specific fact finding: known-item search
• Example: find the email address of Keith Jackson
 Extended fact finding
• Example: What are the sonnets by Shakespeare
 Exploration of availability
• Example: Is there new work in process control published by IEEE
 Open ended browsing and problem analysis
• Is there new research on the use of cell phones in China
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Information Search and Visualization
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Searching in Text Documents and Database Querying
 Google’s Link Based Ranking Measure – PageRank (Brin & Page, 1998)
• Computes a query independent score for each document
• Takes into consideration the importance of the pages that point to a given page
• The big dogs know where to hunt
 SQL (database query language)
• Example:
SELECT DOCUMENT#
FROM JOURNAL = MY_FAVORITE_JOURNAL
WHERE (DATE > 2001 AND DATE <= 2003)
AND (LANGUAGE = ENGLISH)
AND (PUBLISHER = HFES OR ACM)
 Natural Language Queries
• Mainly just eliminates frequent terms
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Information Search and Visualization
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Searching in Text Documents and Database Querying
 Form-Fillin Queries (http://thomas.loc.gov/)
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Information Search and Visualization
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Searching in Text Documents and Database Querying
 Phases of search
• Formulation: expressing the search
• Initiation of action: launching the search
• Review of results: reading messages and outcomes
• Refinement: formulating the next step
• Use: compiling or disseminating information
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Information Search and Visualization
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Searching in Text Documents and Database Querying
 Formulation
• Identify the source of the information (e.g., within a specific library)
• Use fields to limit the search (e.g., year or language)
• Recognize phrases to allow entry of names (e.g., Abraham Lincoln)
– Allow for search my phrase or individual items in the phrase
• Apply variants to relax the search constraints
– Case sensitivity (JEFFERSON, Jefferson)
– Stemming (sing, singing)
– Partial matches (biology, psychobiology, sociobiology)
– Phonetic variations (Smith, Smyth, Smythe)
– Abbreviations (ATT, NCR)
– Synonyms (West Coast retrieves Washington, Oregon and California)
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Information Search and Visualization
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Searching in Text Documents and Database Querying
 Formulation
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Information Search and Visualization
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Searching in Text Documents and Database Querying
 Initiation of Action
• Explicit initiation (e.g., search button)
• Implicit initiation: each change to a component of the formulation phase
immediately produces a new set of search results (e.g., Google)
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Information Search and Visualization
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Searching in Text Documents and Database Querying
 Review of Results
• Users can read messages and view textual lists
• Allow the user to control
– The number of results
– Which fields are displayed
– The sequence of the results
– How results are clustered
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Information Search and Visualization
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Searching in Text Documents and Database Querying
 Review of Results
• Clustering
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Information Search and Visualization
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Searching in Text Documents and Database Querying
 Review of Results
• User control
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Information Search and Visualization
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Searching in Text Documents and Database Querying
 Refinement
• In the event of few results, indicate that using fewer search criteria, or
partial matches may increase the number of hits
• Suggested spellings
• If no results are found, always provide users with that information
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Information Search and Visualization
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Searching in Text Documents and Database Querying
 Use Results
• Merge, save, distributed via email, output to visualization programs, or
statistical tools
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Information Search and Visualization
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Multimedia Document Searches
 Most systems used to locate images, video, sound and animation
depend on metadata
 Example: search of a photo library by date, photographer or text
captions
• Requires significant human effort to add captions and annotate
 Image search: query by image content
 Map search
• Search by latitude and longitude
• Search by features (e.g., search for all cities in northwest United States
with airports)
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Information Search and Visualization
 Picasa
 Supports browse and search of photos in public albums
 Automatically organizes the user’s online photo collection based to who's in
each picture
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Information Search and Visualization
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Other Searching Mechanisms
 Sound Search – Music-information retrieval (MIR)
• Users can play or sing as input, and matching songs will be returned
 Video Search
• Segment into scenes
• Allow scene skipping
 Animation Search
• Examples: search for morphing faces
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Information Search and Visualization
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Video Search
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Informedia
 Designed at CMU to solve the problem of searching huge collections of video
and audio recordings
 Developed new approaches for automated video and audio indexing,
navigation, visualization, search
 Provides full-content search and retrieval of current and past TV and radio
news and documentary broadcasts.
 Generates various summaries for each story segment: headlines, filmstrip
story-boards and video-skims
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Information Search and Visualization
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Video Search - Informedia
 Example: 12 documents returned for "El Niño" query along with different
multimedia abstractions from certain documents
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Information Search and Visualization
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Advanced Filtering and Search Interfaces
 Filtering with complex Boolean queries
• Example: List all employees who live in Denver and Detroit
• Would most likely result in a null result since “and” implies intersection
• Most employees do not live in both locations
• Other approaches
– Venn Diagrams
– Decision Tables
– Metaphors of water flowing through a series of filters
 Automatic Filtering
• Selective dissemination of information
• Filtering email before it is placed in the Inbox
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Information Search and Visualization
Decision Table
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Information Search and Visualization
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Advanced Filtering and Search Interfaces
 Dynamic queries
• Uses direct manipulation objects
http://www.bluenile.com/build-yourown-diamondring?first_step=diamond&forceStep=
DIAMONDS_STEP
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Information Search and Visualization
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Advanced Filtering and Search Interfaces
 Metadata search (e.g., Flamenco)
• Attribute values are selected by the user
• http://flamenco.berkeley.edu/demos.html
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Information Search and Visualization
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Advanced Filtering and Search Interfaces
 Collaborative Filtering
• Users work together to define filtering criteria in large information spaces
• Example: If you ranked five movies highly, the algorithm provides you with a list
of other movies that were rated highly by people who liked your five movies
 Visual Searches
• Examples: Selecting dates on calendars or seats from a plane image
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Information Search and Visualization
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Advanced Filtering and Search Interfaces
http://www.mediabistro.com/10000words/what-is-a-treemap-5examples-and-how-you-can-create-one_b736
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Information Search and Visualization
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Information Visualization
 The use of interactive visual representations of abstract data to amplify cognition
 Scientific Visualization: requires two dimensions because typical questions
involve
• Continuous variables
• Volumes
 Information Visualization involve
• Categorical variables
• Discovery of patterns
• Trends
• Clusters
• Outliers
• Gaps in data
http://www.youtube.com/watch?v=xekEXM0Vonc
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Information Search and Visualization
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Information Visualization
 Uses human perceptual abilities to make discoveries, decisions and propose
explanations
 Users can scan, recognize and recall images quickly
 Users can detect changes in size, color, shape, movement and texture
 IV Rule
• Overview first
• Zoom and filter
• Details on demand
http://www.google.com/publicdata/
directory
http://www.youtube.com/watch?feature=fvwrel&v=RgA4aaEfgPQ
&NR=1
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Information Search and Visualization
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Information Visualization
 1D Linear Data
• Text documents, dictionaries
• Organized sequentially
• Example: view 4000 lines of
code
• Newest lines are in red,
oldest lines in blue
• Browser window shows code
overview and detail window
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Information Search and Visualization
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Information Visualization
 1D Linear Data
• All the words in Alice in
Wonderland, arranged in an
arc, starting at 12:00
• Lines are drawn around the
outside, words around the
inside
• Words that appear more
often are brighter
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Information Search and Visualization
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Information Visualization
 2D Map Data
• Planar data include geographic
maps
• Each item has task domain
attributes, (e.g., name)
• Each item has interface
features (e.g., size or color)
• User tasks (find adjacent
items, regions containing
items, paths between items
•Proximity indicates similarity of
topics
•Height reflects the number of
documents
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Information Search and Visualization
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Information Visualization
 3D World Data
• Real world objects –
molecules, human body,
buildings and the relationships
between the objects
• Users work with continuous
variables (e.g., temperature)
http://www.youtube.com/watch?v=rcuq2
eyuqHQ&feature=autoplay&list=ULOnY
SHQumfro&playnext=1
http://www.youtube.com/watch?v=jbkSRLYSojo
http://www.youtube.com/watch?v=8Ez6UQ69iQ0
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Information Search and Visualization
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Information Visualization
 Multidimensional data
 Extracted data from statistical
databases
 Tasks include finding patterns,
correlations between pairs of
variables, clusters, gaps and
outliers
•www.inxight.com
•Example of listing of houses for sale
•Spreadsheet metaphor
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Information Search and Visualization
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Information Visualization
 Multidimensional data
• Hierarchical or k-means
clustering to identify similar
items
• Hierarchical: identifies close
pairs of items and forms everlarger clusters until every point
is included in the cluster
• K-means: starts when users
specify how many clusters to
create, then the algorithm
places every item into the most
appropriate cluster
•http://www.cs.umd.edu/hcil/bioinfovis/hce.
shtml
•Example: hierarchical clustering of gene
expression data
• Identifying clusters of genes that are
activated with malignant as opposed to
benign melanoma (skin cancer)
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Information Search and Visualization
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Information Visualization
 Temporal Data
 Illnesses, Vaccinations,
Surgeries, Lab Results
 Events have a start/end time,
and items may overlap
 Tasks: finding all events before,
after or during some time period
or moment
•www.cs.umd.edu/hcil/lifelines
•Example: Patient Medical Record
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Information Search and Visualization
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Information Visualization
 Tree Data
• Collection of items where each
item has a link to one parent
item
Example: Organization Chart
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Information Search and Visualization
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Information Visualization
 Tree Data
• Hyperbolic Tree Structure
• Limit the number of nodes in the center of the UI
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Information Search and Visualization
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Information Visualization
 TreeMap
• Each rectangle represents a
stock and are organized by
industry groups
• The rectangle is proportional
to the market capitalization
• The color indicates gain/loss
• “N” indicates a link to a news
story
Map of the Market
http://www.marketwatch.com/tools/stockresear
ch/marketmap
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Information Search and Visualization
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Information Visualization
 Social Network Data
• When items are linked to an
arbitrary number of other items
• Users often want to know the
shortest or least costly path
connecting two items
Facebook Data Visualization tools
http://www.toprankblog.com/2010/08/6facebook-search-engine-data-visualizationtools/
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Information Search and Visualization
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Information Visualization
 Facebook: Social Graph
 Facebook: Friend Wheel
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Information Search and Visualization
 Information Visualization
 Parallel Coordinates
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Information Search and Visualization
 Star Plots
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Information Search and Visualization
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Information Visualization
 Overview Task
• Users can get a overview of the entire collection
• Zoom
• Detail View
 Filter Task
• Users can filter-out items that are not of interest
 Details-on-demand Task
• Users can select an item or group to set details
 Relate Task
• Users can relate items or groups within a collection
• Show relationships by proximity, containment, connection or color coding
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Information Search and Visualization
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Information Visualization
 History Task
• Supports undo, replay and progressive refinement
 Extract Task
• Allows extraction of sub-collections
• Send items are obtained
– Save
– Email
– Insert to a statistical package
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Information Search and Visualization
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Periodic table of data visualization methods
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Web Site