1. Visualization of Web Search Results in 3D
Seminar Presentation
Visualization of Web Search Results in 3D
Kotaiah Choudary R
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2. Overview Challenges in present web search scenario
What is information visualization
Why we need visualization
How visualization tools help in web search
Different visualization tools
Case study :NIRVE,a 3D visualization tool
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3. Internet Evolution Users Growth Past users : only experts
Current users : all people
Growth
Before 1993 : Increasing linearly
After 1993 : Doubled per every months
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4. How typical search engine works?
When user enters a query ,search engine will search the database for documents matching with the keywords.
Search engine ranks the documents based upon some ranking algorithm .
Eg:no of occurrences of words, links to other pages.
Search engine shows all the documents using these ranks.
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5. Do web search results are page relevant?
billions
GG=Google, ATW=AllTheWeb, INK=Inktomi , TMA=Teoma, AV=AltaVista
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6. What is Information Visualization?
The use of computer-supported, interactive, visual representations of abstract data to amplify cognition.
It joins the human’s capacity of visual thinking and the computer’s capacity of analytical computing, thereby building a bi-directional visual and interactive interface between human user and the information resources
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7. Why We Need to Visualize Text ?
Language only hints at meaning. Most meaning of text lies within our minds and common understanding.
Text consists of abstract concepts which are difficult to visualize.
Eg:graph
Text represents similar concepts in many different ways Eg:space ship, flying saucer
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8. Why Visualization in Web Search?
Exploring information collections becomes increasingly difficult as the volume grows high.
With minimal effort, the human visual system can process a large amount of information in a parallel manner.
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9. Why Visualization in Web Search? Cont..
The occurrence of advanced graphical software and hardware enables the large-scale visualization and the direct manipulation of interfaces
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10. Goals of Information Visualization
Relieve the cognitive overload
Size & Scalability
User interaction
Speed
from slow reading to faster visual perception
Insight not pictures
understanding and creating knowledge
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11. Where We Locate Visualization Tools
At the Web Search engine
A web search engine will give the results in visualization mode.
Eg: TEOMA, Vivisimo,etc…
At the user system
Use a special Visualization Tool to browse the search engine and give the results back in the visual mode.
Eg: NIRVE, Periscope etc…
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12. How Typical Visualization Tool Works?
Visualization tool takes set of key words from user and gives to search engine
Search engine gives results to visualization tool as one ENTRY per document
In each ENTRY Document title,Relevance score, Document rank ,Document length , URL is there
Creates concepts by taking some combinations of frequent words
Do Text clustering by using concepts
Displays all Clusters & Documents by using some visualization technique
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13. Visualization Techniques for Web Search
Textual
Represent clusters linearly
No graphical interfaces required
Eg:TEOMA,Vivisimo,etc..
2D approach
Represents all clusters with relationships between them by 2D graphical interfaces
Structures used in 2D approach : Hierarchical trees,histograms,etc..
3D approach
Represents all clusters with relationships between them by 3D graphical interfaces
Structures used in 3D approach : Cone trees,etc
Eg:NIRVE,PeriScope,AltaVista
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14. Textual Visualization Technique Eg:TEOMA
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15. 3D Visualization Technique
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16. 2D Visualization Technique
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17. What is NIRVE (NIST Information Retrieval Visualization Engine )
It is a post-processor for NIST's PRISE [ZPRISE] search engine
It is a 3-dimensional interface designed to enhance the information retrieval process by providing an overview of a set of text documents as well as access to the details of the individual documents through seamless
user navigation and manipulation
NIRVE has interactive 3D graphics techniques , used to help the user comprehend and filter result sets
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18. Features of NIRVE
Graphical summary of result set of search, with details on demand
Map keywords into user-specified concepts
Concept can be computed as union (OR) or intersection (AND) of its constituent keywords
Automatic clustering of documents, based on their concept profile
Cluster icons arranged on sphere or 2D surface; better clusters near the "North Pole“
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19. Features of NIRVE cont…
Relationship among clusters illustrated by color-coded arcs representing set-difference
User can mark documents and clusters as good / bad / unsure and apply display filter to result set
User can open a cluster to view constituent document titles, arranged according to title similarity and score assigned by search engine
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20. Working With NIRVE
Users can use various features of NIRVE by using 3 windows
NIRVE Control
It is a "menu" window used for overall control and mode-setting.
Document Space
It contains the main display of icons representing the selected documents, and clusters thereof
Concept Control
It allow the user to consolidate the keywords used in the query into a smaller set of concepts.
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21. NIRVE Control Document Space
Recycle,tire,dump,landfill,environmental, protection,agency,pollution,contamination
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22. Concept Control
It allow the user to consolidate the keywords used in the query into a smaller set of concepts
Creation or change or deletion of concepts
(at the NIRVE control menu)
Use of UNUSED
(at document space menu)
Change of keyword from one concept to another.
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23. Implementation of NIRVE
Control menu managed by Tcl/Tk software .
Tcl/Tk is a Tool Command Language/Tool Kit. Combination of a scripting language (TCL) and a widget tool kit (TK) that allows the rapid creation of portable GUI programs.
Graphical windows managed by OpenGL.
OpenGL is a widely used 3-D graphics library.
Graphical windows &control menu connected by Xlib . Xlib is a library of functions for displaying text and graphics and receiving input through (possibly remote) servers attached to displays and input devices.
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24. Conclusion
The utility of visualization techniques derives in large part from their ability to reduce mental workload.
Refinement of search is possible
The user can manipulate the retrieval set interactively while maintaining context.
User experience also influence performance.
Assessing relations are based on statistical properties of term and/or document occurrence data
Visualization techniques are also not satisfactory,For very large amounts of documents(several thousands).
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