@conference {16058,
	title = {VAST 2009 challenge: An insider threat},
	booktitle = {Visual Analytics Science and Technology, 2009. VAST 2009. IEEE Symposium on},
	year = {2009},
	month = {2009/10//},
	pages = {243 - 244},
	abstract = {The 4th VAST Challenge centered on a cyber analytics scenario and offered three mini-challenges with datasets of badge and network traffic data, a social network including geospatial information, and security video. Teams could also enter the Grand challenge which combined all three datasets. In this paper, we summarize the dataset, the overall scenario and the questions asked in the challenges. We describe the judging process and new infrastructure developed to manage the submissions and compute accuracy measures in the social network mini challenge. We received 49 entries from 30 teams, and gave 23 different awards to a total of 16 teams.},
	keywords = {2009, analytics;data, analytics;geospatial, challenge;cyber, data;security, information, information;human, interaction;network, interfaces;, network;visual, traffic, user, VAST, video;social, visualisation;graphical},
	doi = {10.1109/VAST.2009.5334454},
	author = {Grinstein,G. and Scholtz,J. and Whiting,M. and Plaisant, Catherine}
}
@article {16066,
	title = {Evaluating Visual Analytics at the 2007 VAST Symposium Contest},
	journal = {Computer Graphics and Applications, IEEE},
	volume = {28},
	year = {2008},
	month = {2008/04//march},
	pages = {12 - 21},
	abstract = {In this article, we report on the contest{\textquoteright}s data set and tasks, the judging criteria, the winning tools, and the overall lessons learned in the competition. We believe that by organizing these contests, we{\textquoteright}re creating useful resources for researchers and are beginning to understand how to better evaluate VA tools. Competitions encourage the community to work on difficult problems, improve their tools, and develop baselines for others to build or improve upon. We continue to evolve a collection of data sets, scenarios, and evaluation methodologies that reflect the richness of the many VA tasks and applications.},
	keywords = {2007, analytics, analytics;data, and, Contest;Visual, Science, Symposium, Technology;data, VAST, visualisation;, visualization;visual},
	isbn = {0272-1716},
	doi = {10.1109/MCG.2008.27},
	author = {Plaisant, Catherine and Grinstein,G. and Scholtz,J. and Whiting,M. and O{\textquoteright}Connell,T. and Laskowski,S. and Chien,L. and Tat,A. and Wright,W. and Gorg,C. and Zhicheng Liu and Parekh,N. and Singhal,K. and Stasko,J.}
}
@conference {16064,
	title = {VAST 2008 Challenge: Introducing mini-challenges},
	booktitle = {Visual Analytics Science and Technology, 2008. VAST {\textquoteright}08. IEEE Symposium on},
	year = {2008},
	month = {2008/10//},
	pages = {195 - 196},
	abstract = {Visual analytics experts realize that one effective way to push the field forward and to develop metrics for measuring the performance of various visual analytics components is to hold an annual competition. The VAST 2008 Challenge is the third year that such a competition was held in conjunction with the IEEE Visual Analytics Science and Technology (VAST) symposium. The authors restructured the contest format used in 2006 and 2007 to reduce the barriers to participation and offered four mini-challenges and a Grand Challenge. Mini Challenge participants were to use visual analytic tools to explore one of four heterogeneous data collections to analyze specific activities of a fictitious, controversial movement. Questions asked in the Grand Challenge required the participants to synthesize data from all four data sets. In this paper we give a brief overview of the data sets, the tasks, the participation, the judging, and the results.},
	keywords = {2008, analytics;data, Challenge;heterogeneous, collections;visual, data, VAST, visualisation;},
	doi = {10.1109/VAST.2008.4677383},
	author = {Grinstein,G. and Plaisant, Catherine and Laskowski,S. and O{\textquoteright}Connell,T. and Scholtz,J. and Whiting,M.}
}
