1. Tin Kam Ho Computing Sciences Research Center Bell Labs, Lucent Technologies In collaboration with David Wittman, J. Anthony Tyson of UC Davis Samuel Carliles, William O’Mullane, Alex Szalay of JHU Interactive Pattern Discovery with Large Imaging Databases

2. What Is the Story in this Image?

3. 1.Describe each symbol shape with a numerical vector [23 12 17 28 11 …] 2.Find clusters of symbol shapes 3.Interpret each cluster using context Solving the Puzzle with a 3-step Approach

4. 10.10.10 51.37.50.54.41.35.37 39.47.33.44 13.13 33.52.6.52 83.65.73.68 73.84 72.65.83 83.69.84 65 71.79.65.76 79.70 82.69.83.84.79.82.73.78.71 83.69.82.86.73.67.69 79.78 73.84.83 76.79.78.71.13.68.73.83.84.65.78.67.69 78.69.84.87.79.82.75 70.65.83.84.69.82 84.72.65.78 84.72.69 83.89.83.84.69.77 73.84.83.69.76.70 68.73.83.67.79.78.78.69.67.84.83 67.65.76.76.83 65.70.84.69.82 65 67.65.66.76.69 66.82.69.65.75.14 *** SERVICE GOAL -- AT&T said it has set a goal of restoring service on its long-distance network faster than the system itself disconnects calls after a cable break.

5. Tracking Intensive Rain Cells in Radar Images

6. http://dls.physics.ucdavis.edu/ BVRz to 26 mag over 28 sq. degree The Deep Lens Survey (Tyson, Wittman, … )

7. Weak Gravitational Lensing Uses distortion of background galaxies to map foreground mass concentrations J.A. Tyson, DLS 2002

8. Catalog of Extracted Objects

9. Stars or Galaxies? J.A. Tyson, DLS 2002

10. Discrimination task depends on tiny differences in color and shape Survey is to an unpreceded depth: most objects have never been observed before and nobody knows their true classification How does one build confidence on the results of the classifier? Need to correlate several perspectives: object characteristics in the color space, shape parameters, the brightness statistics Visualization can help verify correctness of preprocessing steps, clean up undesirable artifacts, choose relevant samples, spot explicit patterns, select useful features, and suggest algorithms and models

11. The Virtual Observatory http://www.us-vo.org/ http://www.ivoa.net/

12. Essential Steps in Automatic Pattern Recognition Feature Extraction Classifier Training Classification Clustering Cluster Validation Cluster Interpretation Samples features classifier features class membership Supervised learning Unsupervised learning feature 1 feature 2

13. Feature Set A Set B Unknown Relationship Clustering Data Mining ParametersResponses Feature Computation Filtering, Clustering Simulation Analysis Data Relationships Across Multiple Feature Sets

14. Key Algorithms Clustering: find natural groups in data, construct index structures to facilitate proximity queries Dimensionality reduction: embed high-dimensional data in 2D displays Navigation: traverse index structures in systematic ways

15. Clustering Methods Model based Clustering identification of finite mixtures Partitional Clustering divides data set into N mutually exclusive subsets Hierarchical Clustering top-down procedures: tree splitting bottom-up, agglomerative procedures: merge similar clusters successively

16. Similarity / Clustering of Objects from Different Perspectives Objects can be described by many types of attributes: position, weight, shape, spectrum, time variability, … Meaningful similarity metric exists only for the same type of attributes Clusters 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 clusters Color clusters

17. Exploratory Tools Needed To bring in domain expertise, interpretation context To visualize data or classifier geometry To track point/class correlations To test tentative classifications To compare groupings from different perspectives To relate numerical data to other data types To facilitate systematic, repeatable explorations

18. Mirage for Interactive Pattern Recognition Data Display in Linked Views Show patterns in histograms, scatter plots, parallel coordinates, tables, and images Selection and Tracking Select points in any view, broadcast to all others Traversal of Data Structures Walk in histograms, cluster graphs or trees, echoed in all other views Graphical Utilities Open multiple-page plots with arbitrary configuration Command Scripts Run prepared groups of operations as an animation Intuitive Graphical Tool for  Exploratory Data Analysis  Visualization of Clusters and Classes  Correlation of Proximity Structures  Manual or Automatic Classification http://www.cs.bell-labs.com/who/tkh/mirage

19. Software Features Based on Java Swing library Intuitive, easy-to-use graphical operations Mutiple-page, arbitrary plot configurations Online or offline cluster analysis GUI or Script driven command execution Database interface via JDBC Ready to be adapted for on-line monitoring Ready to be integrated with database access and decision support systems

20. Design Motivated by the Needs Interactive plays, intuitive operations to bring domain experts into the loop Multiple types of plots, extensible for more to visualize data or classifier geometry Linked views, traversal actions to track point/class correlations Highlights, colors to test tentative classifications Projection to arbitrary subspaces to compare groupings in different perspectives Linking data with images to relate numerical data to other types Command scripting to facilitate systematic, repeatable explorations

21. Challenges for the Analysis Tool Separate treatment of non-comparable groups of variables Versatile visualization utilities allowing many perspectives Support for exploratory discovery across diverse data types Integrate manual & automatic pattern recognition methods Also, a good tool should -- leverage existing visualization and analysis methods -- enable continued growth: new visualization, analysis tools -- support interface with existing databases -- be scalable in data volume and processing speed

22. 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 Python? Matlab? FITS viewer, … Towards Extensibility

23. VO Enabled Mirage (with Samuel Carliles, William O’Mullane, and Alex Szalay)

24. VO Enabled Mirage http://skyservice.pha.jhu.edu/develop/vo/mirage/ Load VOTable data and perform VO Cone/SIAP and SDSS CAS searches using IVOA Client Package Astronomical imaging module loads FITS images using JSky classes, supporting image operations: Select data points and broadcast selection to other views. Cut levels. Colormap. SAO DS9-style brightness/contrast enhance. Zoom.

25. Extinction Web Service (with Chris Miller, Simon Krughoff) Using DIRBE/IRAS Dust Maps by Schlegel et al.DIRBE/IRAS Dust Maps Mirage Core Object selection Extracts RA,DEC,[mag] from Mirage data set SOAP client calls Extinction server Merges results with Mirage data set Extinction Service Positions, mags Positions, mags, filterIDs E(b-v), dered_mags Enhanced data set Result stream

26. 205th Meeting of the American Astronomical Society 9-13 January 2005 San Diego, CA Wednesday, 12 January Astronomical Research with the Virtual Observatory More at NVO Public Release 1.0

27. Analysis of Simulations of Control Dynamics in Optical Transport Systems (with the FROG collaboration) Head End Terminal Repeater Fiber link Repeater Gain Equalizer Tail End Terminal Signal Spectrum with noise floor

28. Monitoring Network Traffic Software tool for online monitoring and analysis of QoS in IP networks continuously monitors traffic statistics at edge and core devices synthesizes statistics in real time to obtain network-wide QoS status and general network element health indicators Mirage refreshes displays on alerts of database updates via Java Messaging Service SEQUIN SNMP polling SLA verification Billing Provisioning MPLS IP Core (QoS-guaranteed paths) DiffServ Edge (aggregation and classification) (With Marina Thottan, Ken Swanson)