Sections
1. Introduction
2. Related work
3. Demonstration dataset and preprocessing
4. Multidimensional scaling
5. Adaptive visualization using
stratification
6. Data stratification options and results
7. Scatterplot similarity matching
8. Incremental visualization using fusion
9. Combined visualization technique
10. Discussion and future work
11. Conclusions
12. Acknowledgments
13. Exercises and problems
14. References
Abstract
We introduce two dynamic visualization techniques using multi-dimensional scaling to analyze transient data streams such as newswires and remote sensing imagery. While the time-sensitive nature of these data streams requires immediate attention in many applications, the unpredictable and unbounded characteristics of this information can potentially overwhelm many scaling algorithms that require a full recomputation for every update. We present an adaptive visualization technique based on data stratification to ingest stream information adaptively when influx rate exceeds processing rate. We also describe an incremental visualization technique based on data fusion to project new information directly onto a visualization subspace spanned by the singular vectors of the previously processed neighboring data. The ultimate goal is to leverage the value of legacy and new information and minimize reprocessing of the entire dataset in full resolution. We demonstrate these dynamic visualization results using a newswire corpus, a remote sensing imagery sequence, and a hydroclimate dataset.