Abstract
We introduce two dynamic visualization techniques using multi-dimensional scaling to analyze transient data streams such as newswires and remote sens- ing 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 al- gorithms that require a full re-computation 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 de- scribe 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 re-processing of the entire dataset in full resolution. We demonstrate these dynamic visuali- zation results using a newswire corpus, a remote sensing imagery sequence, and a hydroclimate dataset.
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