2 research outputs found
VAST Challenge 2015: Mayhem at Dinofun World
A fictitious amusement park and a larger-than-life hometown football hero provided participants in the VAST Challenge 2015 with an engaging yet complex storyline and setting in which to analyze movement and communication patterns. The datasets for the 2015 challenge were large—averaging nearly 10 million records per day over a three day period—with a simple straightforward structured format. The simplicity of the format belied a complex wealth of features contained in the data that needed to be discovered and understood to solve the tasks and questions that were posed. Two Mini-Challenges and a Grand Challenge compose the 2015 competition. Mini-Challenge 1 contained structured location and date-time data for park visitors, against which participants were to discern groups and their activities. MiniChallenge 2 contained structured communication data consisting of metadata about time-stamped text messages sent between park visitors. The Grand Challenge required participants to use both movement and communication data to hypothesize when a crime was committed and identify the most likely suspects from all the park visitors. The VAST Challenge 2015 received 74 submissions, and the datasets were downloaded, at least partially, from 26 countries
Recommended from our members
LDA Ensembles for Interactive Exploration and Categorization of Behaviors
We define behavior as a set of actions performed by some agent during a period of time. We consider the problem of analyzing a large collection of behaviors by multiple agents, more specifically, identifying typical behaviors as well as spotting behavior anomalies. We propose an approach leveraging topic modeling techniques -- LDA (Latent Dirichlet Allocation) Ensembles -- for representing categories of typical behaviors by topics obtained through applying topic modeling to a behavior collection. When such methods are applied to text documents, the goodness of the extracted topics is usually judged based on the semantic relatedness of the terms pertinent to the topics. This criterion, however, may not be applicable to topics extracted from non-textual data, such as action sets, since relationships between actions may not be obvious. We have developed a suite of visual and interactive techniques supporting the construction of an appropriate combination of topics based on other criteria, such as distinctiveness and coverage of the behavior set. Our case studies in the operation behaviors in the security management system and visiting behaviors in an amusement park and the expert evaluation of the first case study demonstrate the effectiveness of our approach