The goal of our project is to create a set of next-generation cyber situational-awareness capabilities with applications to
other domains in the long term. The situational-awareness capabilities being developed focus on novel visualization
techniques as well as data analysis techniques designed to improve the comprehensibility of the visualizations. The
objective is to improve the decision-making process to enable decision makers to choose better actions. To this end, we
put extensive effort into ensuring we had feedback from network analysts and managers and understanding what their
needs truly are. This paper discusses the cognitive task analysis methodology we followed to acquire feedback from the
analysts. This paper also provides the details we acquired from the analysts on their processes, goals, concerns, etc. A
final result we describe is the generation of a task-flow diagram.
We present a visualization technique that allows a user to identify and detect patterns and structures within a multivariate data set. Our research builds on previous efforts to represent multivariate data in a 2D information display through the use of icon plots. Although the icon plot work done by Pickett and Brinstein is similar to our approach, we improve on their efforts in several ways. Our technique allows analysis of a time series without using animation; promotes visual differentiation of information clusters based on measures of variance; and facilitates exploration through direct manipulation of geometry based on scales of variance. Our goal is to provide a visualization that implicitly conveys the degree to which an elements ordered collection of attributes varies from the prevailing pattern of attributes for other elements in the collection. We apply this technique to multivariate abstract data nd use it to locate exceptional elements in a data set and divisions among clusters.
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