Creating and exploiting network models from wide area motion imagery (WAMI) is an important task for intelligence
analysis. Tracks of entities observed moving in the WAMI sensor data are extracted, then large numbers of tracks are
studied over long time intervals to determine specific locations that are visited (e.g., buildings in an urban environment),
what locations are related to other locations, and the function of each location. This paper describes several parts of the
network detection/exploitation problem, and summarizes a solution technique for each: (a) Detecting nodes; (b)
Detecting links between known nodes; (c) Node attributes to characterize a node; (d) Link attributes to characterize each
link; (e) Link structure inferred from node attributes and vice versa; and (f) Decomposing a detected network into
smaller networks. Experimental results are presented for each solution technique, and those are used to discuss issues for
each problem part and its solution technique.
Our goal is to enable an individual analyst to utilize and benefit from millions of visualization instances created by a community of analysts. A visualization instance is the combination of a specific set of data and a specific configuration of a visualization providing a visual depiction of that data. As the variety and number of visualization techniques and tools continues to increase, and as users increasingly adopt these tools, more visualization instances will be created (today, perhaps only viewed for a moment and thrown away) during the solution of analysis tasks. This paper discusses what fraction of these visualization instances are worth keeping and why, and argues that keeping more (or all) visualization instances has high value and very low cost. Even if a small fraction is retained the result over time is still a large number of visualization instances and the issue remains, how can users utilize them? This paper describes what new functionality users need to utilize all those visualization instances, illustrated by examples using an information workspace tool based on zoomable user interface principles. The paper concludes with a concise set of principles for future analysis tools that utilize spatial organization of large numbers of visualization instances.