Aptima and VAST Challenge 2011: Difference between revisions
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Oculus – http://www.oculusinfo.com/ (http://www.oculusinfo.com/nspace2-and-the-ieee-vast-challenge/) | Oculus – http://www.oculusinfo.com/ (http://www.oculusinfo.com/nspace2-and-the-ieee-vast-challenge/) | ||
== Solutions == | |||
=== 125-Bertini === | |||
http://129.63.17.205/vast/challengesubmissions/125-Bertini-GC/index_grand.htm | |||
==== MC1: ==== | |||
Lots of data wrangling/pre-processing at initial stage of analysis. | |||
Built in-house analysis tool to examine tweets. Application features frequency chart, map, and filtering frames. | |||
* Started by looking at map overview for tweet patterns | |||
* Refinement patterns focus on identifying "interesting" tweet clusters in overview, using word cloud for detail views and user IDs, and then tracking user IDs for finding more pivotal events. Also looks at full tweets on occasion. | |||
==== MC2: ==== | |||
==== MC3: ==== | |||
Revision as of 16:24, 6 March 2013
Slides from Aptima about this project and Brown collaboration: Media:AARDVARK_use_case.pdf
VAST Challenge 2011 Info
The task descriptions are available here: http://hcil.cs.umd.edu/localphp/hcil/vast11/index.php/taskdesc/index
The dataset for the challenge is available here: http://hcil.cs.umd.edu/localphp/hcil/vast11/index.php/dataset/register
Research Questions
From David's initial email:
- Is provenance better displayed in the primary/overview case or available as drill-down data?
- What data works better in layers and what data works better in separate windows and why?
More questions:
Related Work
Related Products
Palantir – http://www.palantir.com/ (http://www.cs.umd.edu/hcil/VASTchallenge2010/Entries/196_Palantir_GC/index_grand.htm) (http://vac.nist.gov/2008/entries/Palantir-Palantir-Grand-1/index.html)
Oculus – http://www.oculusinfo.com/ (http://www.oculusinfo.com/nspace2-and-the-ieee-vast-challenge/)
Solutions
125-Bertini
http://129.63.17.205/vast/challengesubmissions/125-Bertini-GC/index_grand.htm
MC1:
Lots of data wrangling/pre-processing at initial stage of analysis. Built in-house analysis tool to examine tweets. Application features frequency chart, map, and filtering frames.
- Started by looking at map overview for tweet patterns
- Refinement patterns focus on identifying "interesting" tweet clusters in overview, using word cloud for detail views and user IDs, and then tracking user IDs for finding more pivotal events. Also looks at full tweets on occasion.