1. Dynamic Visualization of Transient Data Streams P. Wong, et al The Pacific Northwest National Laboratory Presented by John Sharko Visualization of Massive Datasets

2. Characteristics of Data Streams Arrives continuously Arrives unpredictably Arrives unboundedly Arrives without persistent patterns

3. Examples of Data Streams Newswires Internet click streams Network resource management Phone call records Remote sensing imagery

4. Visualization Problem Fusing a large amount of previously analyzed information with a small amount of new information Reprocess the whole dataset in full detail

5. First Objective Achieve the best understanding of transient data when influx rate exceed processing rate Approach: Data stratification to reduce data size

6. Second Objective Incremental visualization technique Approach: Project new information incrementally onto previous data

7. Primary Visualization Output Multidimensional Scaling OJ Simpson trial French elections Oklahoma bombing

8. Adaptive Visualization Using Stratification

9. Methods for Adaptive Visualization Vector dimension reduction Vector sampling

10. Vector Dimension Reduction Approach: dyadic wavelets (Haar) 200 terms 100 terms 50 terms

11. Results of Vector Dimension Reduction 200100 50 Dimensions

12. Results of Vector Sampling 3298 1649 824 Number of Documents

13. Scatterplot Similarity Matching

14. Procrustes Analysis Results 20010050 All0.0 (self)0.0220.084 1/20.0160.0510.111 1/40.0330.0620.141

15. Incremental Visualization Using Fusion Reprocessing by projecting new items onto existing visualization Feature: reprocessing the entire dataset is often not required

16. Hyperspectral Image Processing Apply MDS to scale pixel vectors K-mean process to assign unique colors Stratify the vectors progressively

17. Robust Eigenvectors Generate three MDS scatter plots for each third of the image

18. Robust Eigenvectors (cont’d) Generate MDS scatterplot for entire dataset

19. Robust Eigenvectors (cont’d) Extract points from cropped areas

20. Using Multiple Sliding Windows Eigenvectors determined by the long window New vectors are projected using the Eigenvectors of the long window Data Stream Long WindowShort Window Sliding Direction

21. Dynamic Visualization Steps 1. When influx rate < processing rate, use MDS 2. When influx rate > processing rate, halt MDS 3. Use multiple sliding windows for pre-defined number of steps 4. Use stratification approach for fast overview 5. Check for accumulated error using Procrustes analysis 6. If error threshold not reached, go to step 3 If error threshold reached, go to step 1

22. Conclusions The data stratification approach can substantially accelerate visualization process The data fusion approach can provide instant updates