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Benchmark Details

Provenance: VAST Challenge 2020
Title: Mini-Challenge 3


This global outage event has raised awareness for the need to be able to assemble a rapid response team in the case of a future outage event. The “white hat” community recognizes that serious delays result when CGCS has to go through a complex process to respond to a future event. The white hat community decides to provide CGCS with much more information about their individual expertise, contact information, available tools, and any other information they think could be useful. They want CGCS to be able to search this network and quickly assemble a set of superstars with complementary skills who can be part of an emergency response team. CGCS’s current visual analytics environment supports basic situation awareness and provides a set of simple tools to help them see patterns in individual data sets. However, the existing system will not support the new data that the white hat community is providing. CGCS recognizes that they need a new visual analytics environment, and they would like you to design it. CGCS wants this environment to support two tasks: * High level situation awareness of issues with the global internet, including reports of the emergence of new network attacks, reports of significant network failures, and activities of the white hat community. * Ability to assemble a team from across the white hat community to respond to new events. The CGCS wants to be able to identify one or more experts experienced in a particular issue using all of the data available to them, including resumes, news article mentions, publications, and self-reported expertise. If no experts are found, CGCS may need to infer which of their experts they should ask for ideas about who to contact. In some cases, a full team may have to be assembled, bringing together multiple people with complementary skills who will be able to work well together. CGCS wants to incorporate as much machine learning into the system as possible, but they want to have insight into not only what the machine learning is doing, but what uncertainties exist in both the data and the algorithms. However, CGCS personnel are not experts in machine learning, so they want to have explanations that make sense to them. Your objective is to design for the new CGCS situation awareness and team development environment, illustrating how visual analytics can support CGCS tasks.

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Kris Cook, Pacific Northwest National Laboratory
Jordan Crouser, Smith College
Jereme Haack, Pacific Northwest National Laboratory
John Fallon, University of Massachusetts Amherst
Curtis Larimer, Pacific Northwest National Laboratory
Diane Staheli, MIT Lincoln Laboratory
Kristen Liggett, Air Force Research Laboratory
Steve Gomez, MIT Lincoln Laboratory

Total uses: 1
Used by:
Purdue University
Award: Effective Transformation of Task Decomposition into Conceptual Design

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