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Benchmark Details


Provenance: VAST Challenge 2013
Title: MC1 - Box Office VAST - Predictive Visual Analytics


Description:

VAST Challenge 2013 Mini-Challenge 1 concerns predictive visual analytics. It is decidedly different from previous VAST challenges - it will be continuous and iterative, it will have reviewing and recognition presented from now until the final judging session, contestants will be required to register to participate and login to this site to get data and make submissions. Results will be posted on this website as we go. Contestants can join at any time - there will be awards for long term successes at predictions, but there will also be plenty of interim recognition.

The theme is movie success at the box office and in viewer ratings. Participants will be asked to predict how well a set of movies will do at the box office in terms of box office "take" (ticket sales) and how well they will do in the eyes of the viewers (the movies' viewer ratings) for their opening weekend in the U.S. A key feature of the challenge, though, is that contestants will use visual analytics to support their movie analysis and show us how it was used in their analytic processes. So, while the two numeric predictions would be possible to provide by plugging lots of data into a model, we will ask some additional questions to go along with the predictions that will require a human-in-the-loop and hopefully some outstanding visualizations.

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Dataset available at:
No Dataset Available
Solution:
No Solution Available
Contacts:
Kris Cook, Pacific Northwest National Laboratory
Georges Grinstein, University of Massachusetts-Lowell
Mark Whiting, Pacific Northwest National Laboratory

Total uses: 10
Used by:
Arizona State University
Award: Excellent Visual Analysis of Structured and Unstructured Data
Drexel University
IIIT Hyderabad
INRIA
University of Konstanz - Jentner
Award: Excellent Interactive Analysis
University of Konstanz - Seebacher
Award: Effective Visual Design
University of Minas Gerais
University of Stuttgart
Award: In Depth Visual Exploration of Features
University of the Andes
Vienna University of Technology

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