Jordan Riley Benson

Sensemaking in Big Data Environments

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 data environment. Specifically, we focused on key topics that fit squarely in the intersection of the sensemaking and big data research, as other communities already exist for decision making and big data technologies independently. As part of this effort, our group developed and proposed a framework around which this community can target and structure future research. This framework is intended to allow the community to systematically identify areas where innovative research might make large contributions to sensemaking in a big data environment.

Case study of dino fun world movement and communication data

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 outline the techniques we used and highlight both the successes and shortcomings we found in our toolset when applying it to this challenge.

Scalability of the NOCturne Display

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 address the cyber security domain space and discuss the methodologies we used and how they could be applied to big data visualization challenges in other domains.