Mirage Demo

Slide1: 

Tin Kam Ho Department of Scientific Computing Research Computing Sciences Research Center Bell Labs, Lucent Technologies In collaborations with David Wittman, J. Anthony Tyson of UC Davis Samuel Carliles, William O’Mullane, Alex Szalay of JHU Mirage -- Interactive Pattern Discovery with Large Imaging Databases http://www.cs.bell-labs.com/who/tkh/mirage

Mining Large Imaging Databases: 

Mining Large Imaging Databases Basic needs: Hierarchical data structures andamp; indexing Sophisticated navigation tools Also, Joint usage of data, meta-data, extracted features andamp; catalogs Automatic pattern discovery algorithms to compute layers of abstraction from observation, concepts, to theory Mirage uses visualization to help Track horizontal correlations across different types of attributes for the same objects Track vertical correlations across layers of abstraction from signal to the result of analysis Integrate human and machine pattern recognition capabilities

Horizontal Correlations: Similarity of Objects from Different Perspectives: 

Horizontal Correlations: Similarity of Objects from Different Perspectives Objects can be described by many types of attributes: position, morphology, color, spectra, temporal variability, motion … Meaningful similarity metric exists only for attributes of the same type Similar groups found from one perspective need to be correlated to those from others e.g. Are the objects similar in color also similar in shape? Shape groups Color groups

Slide4: 

Raw Images Vertical Correlations Across Layers of Analysis Processed Images Numerical Features Classes and Groups Relationship between Groups Interpretation in Context Validation in Input Domain

Human / Machine Interaction in Pattern Discovery: 

Human / Machine Interaction in Pattern Discovery Domain expertise Hypotheses Decisions in algorithmic choices Interpretation in context Visualized data geometry Systematic exploration control Computed features andamp; data structures Tentative classifications

Mirage in action …: 

Mirage in action … A simple way to start : java –jar Mirage0.3.jar

Loading in a Data Matrix: 

Loading in a Data Matrix

Making a histogram plot on any attribute: 

Making a histogram plot on any attribute

Selecting some interesting bars: 

Selecting some interesting bars

Selected bars highlighted: 

Selected bars highlighted

Changing the plot to a different attribute: 

Changing the plot to a different attribute

Making a scatter plot with two attributes: 

Making a scatter plot with two attributes

Highlighting an interesting trend: 

Highlighting an interesting trend

Following it to a different pair of attributes: 

Following it to a different pair of attributes

Making a plot in parallel coordinates: 

Making a plot in parallel coordinates

Selecting objects of interest: 

Selecting objects of interest

Highlighting the selection: 

Highlighting the selection

Bringing up a table view to read the details: 

Bringing up a table view to read the details

When the plots are combined …: 

When the plots are combined …

Selection from one plot … : 

Selection from one plot …

Can be broadcast to all others : 

Can be broadcast to all others

Opening a new page of plots: 

Opening a new page of plots

Configuring it as you wish: 

Configuring it as you wish

Relating computed spectral classes to other views: 

Relating computed spectral classes to other views

Selecting one computed class: 

Selecting one computed class

Broadcasting it to see member spectra: 

Broadcasting it to see member spectra

Many cool features waiting for you to explore …: 

Many cool features waiting for you to explore …

Slide28: 

Challenges for the Analysis Tool A good tool should support separate treatment of non-comparable groups of variables versatile visualization utilities allowing many perspectives exploration across data types andamp; levels of abstraction feedback between manual andamp; automatic pattern recognition methods A good tool should also leverage existing visualization, analysis methods enable continuous growth: new visualization, analysis tools support seamless interface with data archives be scalable in data volume and processing speed

Mirage Core: 

Mirage Core Data Access Clients Data Analysis Methods Custom Data Views Data Exchange Pipes VO Data Archives External Rendering Code Web Services Other Analysis Platforms Cone Search, CAS Extinction Calculator Message Based Updates FITS Viewer, … Towards Extensibility

Slide30: 

Data Access, Custom Views: VO Enabled Mirage (with Samuel Carliles, William O’Mullane, and Alex Szalay) http://skyservice.pha.jhu.edu/develop/vo/mirage/

Data Analysis Functions: Extinction Web Service(with Chris Miller, Simon Krughoff)Using DIRBE/IRAS Dust Maps by Schlegel et al.: 

Data Analysis Functions: Extinction Web Service (with Chris Miller, Simon Krughoff) Using DIRBE/IRAS Dust Maps by Schlegel et al.

Continuous Data Updates: SEQUIN experiment(With Marina Thottan, Ken Swanson): 

Network Poller Obtains statistics from each node Health Checker Computes health indicators Record Keeper Stores statistics and indicators in relational database Messenger Broadcasts messages about database updates Mirage Monitor Retrieves data, updates displays when message arrives Monitored Network Continuous Data Updates: SEQUIN experiment (With Marina Thottan, Ken Swanson)

Open Questions: 

Open Questions What questions do scientists want to ask about their data? How can they be translated into graphical operations and answers? Which automatic algorithms are reliable for the tasks? Which visualization techniques can help where it matters? How can we handle large data volume, variable demands on speed, disperse archives, and bandwidth constraints? What are the best ways to support continuous and collaborative explorations? …

Mirage can be downloaded at: 

Mirage can be downloaded at Publicly released on the web since late 2002 Development ongoing … Open source soon to be available http://www.cs.bell-labs.com/who/tkh/mirage