Jordan Riley Benson

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We present a visualization infrastructure that maps data elements to agents, which have behaviors parameterized by those elements. Dynamic visualizations emerge as the agents change position, alter appearance and respond to one other. Agents move to minimize the difference between displayed agent-to-agent distances, and an input matrix of ideal distances.(More)
Creating displays that can convey the patterns and insights within a tremendously large set of input data that is continuously updated in real time is a difficult undertaking. The need for these displays typically arises from a domain specific problem space but many of the techniques can be generalized. In this paper we outline the methodology used to(More)
We present our solution to the VAST 2015 Mini-challenge 1. In our solution we utilized existing visual analytics software and custom tools to analyze the movement and communication data provided for the fictitious Dino Fun World amusement park. Our process focused on collaborative data discovery and the application of analytics procedures. In this paper we(More)
We report on the sensemaking breakout group at the Human Centered Big Data Research (HCBDR-2014) workshop. The authors are a multi-disciplinary team of invited researchers and stakeholders who participated in this breakout session. This report includes an overview of our discussions on the many research challenges associated with sensemaking within a big(More)
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