1 information search and visualization information terminology information retrieval information...
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Information Search and Visualization
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
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
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
Searching in Text Documents and Database Querying
Form-Fillin Queries (http://thomas.loc.gov/)
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Information Search and Visualization
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
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
Searching in Text Documents and Database Querying
Formulation
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Information Search and Visualization
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
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
Searching in Text Documents and Database Querying
Review of Results
• Clustering
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Information Search and Visualization
Searching in Text Documents and Database Querying
Review of Results
• User control
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Information Search and Visualization
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
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
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
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
Video Search
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
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
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
Advanced Filtering and Search Interfaces
Dynamic queries
• Uses direct manipulation objects
http://www.bluenile.com/build-your-own-diamond-ring?first_step=diamond&forceStep=DIAMONDS_STEP
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Information Search and Visualization
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
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
Advanced Filtering and Search Interfaceshttp://www.mediabistro.com/10000words/what-is-a-treemap-5-examples-and-how-you-can-create-one_b736
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Information Search and Visualization
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
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.youtube.com/watch?feature=fvwrel&v=RgA4aaEfgPQ&NR=1http://www.google.com/
publicdata/directory
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Information Search and Visualization
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
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
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
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=8Ez6UQ69iQ0
http://www.youtube.com/watch?v=rcuq2eyuqHQ&feature=autoplay&list=ULOnYSHQumfro&playnext=1
http://www.youtube.com/watch?v=jbkSRLYSojo
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Information Search and Visualization
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
Information Visualization
Multidimensional data
• Hierarchical or k-means
clustering to identify similar
items
• Hierarchical: identifies close
pairs of items and forms ever-
larger 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
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
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
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
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/stockresearch/marketmap
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Information Search and Visualization
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 toolshttp://www.toprankblog.com/2010/08/6-facebook-search-engine-data-visualization-tools/
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Information Search and Visualization
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
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
Information Visualization
History Task
• Supports undo, replay and progressive refinement Extract Task
• Allows extraction of sub-collections
• Send items are obtained
– Save
– Insert to a statistical package
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Information Search and Visualization
Periodic table of data visualization methods
Web Site