Information Retrevial Session on Monday
Visualization of search results
3d Information Visualization: an introduction and practical applications
Brad Eden – eden@library.ucsb.edu
Doctorate in music, edits a bunch of stuff
What is information visualization
The use of computer supported interactive visual representations of abstract data to amplify cognition
Readings in information visualization: using vision to Think
Tell me and I’ll forget
Show me and I may remember
Involve me and I will remember
David Rumsey Map Collection at http://www.davidrumsey.com/gis/3d.htm
www.virtualworld3d.com/p_travel.htm
Olive
www.otal.umd.edu/olive
Look to the atlas of cyberspaces for 3d sites
Lexington public library – aqua browser, topic map.
Using the structured metadata to present the connections between content.
Standard topic map
www.grokker.net
live plasma – displays music in a universe model
cubic eye – empty box you can play with
5 screens you can manipulate
Slide deck is available from his web site.
Information Visualization and Large Scale Repositories
Linn Marks Collins
NSF cyberinfrastructure vision ofr 21st century discovery
Data set – 60 million documents
Problem solvers as users
Higher order thinkers
Understand abstract representations of information
Tend to think in multiple ways
Would rather do science than learn a new interface
Two search result visualization products
Active graph
Interactive scatter plot of search results
By knowing her users she was able to develop a interface that is intuitive to a scientist, but not intuitive to anyone else
The interface shows the number of citations of articles, category, the journal, and the metadata. Incorporates filters. Very convoluted interface, with no clear functions, but – it works for the audience.
Increases information density
Quantitative and qualitiative data can be displayed
Feed back has been positive
Paper published last year on this tool
Aggregation of bibliographical data
LANL
More information in repositories = more time and effort to learn what is in the repository
Know what people are looking for, know the sources they go to, and what they find interesting – so why should they have to find it? Can’t we supply their needs directly?
This is interesting – could we do something similar? We know area they work in, we know their engagements, can we supply an information feed to supply most of their needs?
ScienceSifter
Uses information feed (RSS)
Created from local data
From external sources
Combine and filiter multiple feeds
Seeral viewing options
List
List with descriptions
Visualization with hyperbolic tree
Scientists work together with the channel editors to identify their reading needs
Creates the feed, then lets them read it and save for later
This is TPC’s!?!
Hyperbolic tree
Center group
Next is the key words
Each line from the keywords are the journals
Each line out of there is an article
Interactive, you can select the keyword, then the journals, then the articles
Features asked for is the ability highlight specific items, and save items for later.
Paper presented and available on this
Oncosifter another paper on hyperbolic tree displays
As more data is available, more work is needed to retrieve previous research, where analysis after the fact is more important than the classic experimentation.
IR and data visualization become more important.
Check for interesting papers by these people.
Mark Marinez
Tamara McMahon
Ketan Mane
Rick Luce
Miriam Blake
Jeremy Hussell
Collective Intelligence & Holistic Sense Making
Chaomei Chen, Drexel University
Sum of the parts > whole
What are the hot topics in subject?
How are the hot topics related?
How do they evolve over time?
What are and how do we assess the emerging insights?
Demonstrated a cluster representation of search results demonstrating the connections between papers, clusters etc.
Demonstrated social network analysis
Showed a interesting mash up between google maps and citation index on terriorism. Very powerful impact to see where and when things are displayed.
Retrieval vs visual Analytics
Retrieval
Recall
Discret search
Part formal
Visual
Recognition
Continuous foraging
Whole
Intuitive
Chaomei.chen@cis.drexel.edu
www.pages.drexel.edu/~cc345
3d Information Visualization: an introduction and practical applications
Brad Eden – eden@library.ucsb.edu
Doctorate in music, edits a bunch of stuff
What is information visualization
The use of computer supported interactive visual representations of abstract data to amplify cognition
Readings in information visualization: using vision to Think
Tell me and I’ll forget
Show me and I may remember
Involve me and I will remember
David Rumsey Map Collection at http://www.davidrumsey.com/gis/3d.htm
www.virtualworld3d.com/p_travel.htm
Olive
www.otal.umd.edu/olive
Look to the atlas of cyberspaces for 3d sites
Lexington public library – aqua browser, topic map.
Using the structured metadata to present the connections between content.
Standard topic map
www.grokker.net
live plasma – displays music in a universe model
cubic eye – empty box you can play with
5 screens you can manipulate
Slide deck is available from his web site.
Information Visualization and Large Scale Repositories
Linn Marks Collins
NSF cyberinfrastructure vision ofr 21st century discovery
Data set – 60 million documents
Problem solvers as users
Higher order thinkers
Understand abstract representations of information
Tend to think in multiple ways
Would rather do science than learn a new interface
Two search result visualization products
Active graph
Interactive scatter plot of search results
By knowing her users she was able to develop a interface that is intuitive to a scientist, but not intuitive to anyone else
The interface shows the number of citations of articles, category, the journal, and the metadata. Incorporates filters. Very convoluted interface, with no clear functions, but – it works for the audience.
Increases information density
Quantitative and qualitiative data can be displayed
Feed back has been positive
Paper published last year on this tool
Aggregation of bibliographical data
LANL
More information in repositories = more time and effort to learn what is in the repository
Know what people are looking for, know the sources they go to, and what they find interesting – so why should they have to find it? Can’t we supply their needs directly?
This is interesting – could we do something similar? We know area they work in, we know their engagements, can we supply an information feed to supply most of their needs?
ScienceSifter
Uses information feed (RSS)
Created from local data
From external sources
Combine and filiter multiple feeds
Seeral viewing options
List
List with descriptions
Visualization with hyperbolic tree
Scientists work together with the channel editors to identify their reading needs
Creates the feed, then lets them read it and save for later
This is TPC’s!?!
Hyperbolic tree
Center group
Next is the key words
Each line from the keywords are the journals
Each line out of there is an article
Interactive, you can select the keyword, then the journals, then the articles
Features asked for is the ability highlight specific items, and save items for later.
Paper presented and available on this
Oncosifter another paper on hyperbolic tree displays
As more data is available, more work is needed to retrieve previous research, where analysis after the fact is more important than the classic experimentation.
IR and data visualization become more important.
Check for interesting papers by these people.
Mark Marinez
Tamara McMahon
Ketan Mane
Rick Luce
Miriam Blake
Jeremy Hussell
Collective Intelligence & Holistic Sense Making
Chaomei Chen, Drexel University
Sum of the parts > whole
What are the hot topics in subject?
How are the hot topics related?
How do they evolve over time?
What are and how do we assess the emerging insights?
Demonstrated a cluster representation of search results demonstrating the connections between papers, clusters etc.
Demonstrated social network analysis
Showed a interesting mash up between google maps and citation index on terriorism. Very powerful impact to see where and when things are displayed.
Retrieval vs visual Analytics
Retrieval
Recall
Discret search
Part formal
Visual
Recognition
Continuous foraging
Whole
Intuitive
Chaomei.chen@cis.drexel.edu
www.pages.drexel.edu/~cc345
posted by Ed Dale at 8:11 AM
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