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 par...
Given vehicle tracking data, loyalty and credit card logs of employees from a fictitious company, GA...
This summary describes a tool we created for the Grand Challenge of the VAST Challenge 2015. We pres...
Crimes, forest fires, accidents, infectious diseases, human interactions with mobile devices (e.g., ...
The 2016 VAST Challenge returns to the (fictional) island of Kronos to pose three Mini-Challenges. I...
This report describes the joint entry from Middlesex University and the University of Leeds for Mini...
The 2014 VAST Mini-challenge 1 asked participants to summarise the structure of two organisations wi...
Social Media is a very good example of a large communication network. Typically, most data generated...
The VAST 2008 Challenge consisted of four heterogeneous synthetic data sets each organized into sepa...
The manual process of collecting and labelling data required for machine learning tasks is labour-in...
The Virtual Worlds Exploratorium is a multidisciplinary project dedicated to the study of communicat...
International audienceWe report on the process and design of our visual analytics graph analysis cha...
Recent technological advances have made it possible to more accurately understand visitor travel pat...
Abstract: Agent-based simulation is a technology which is being used as a decision support tool for ...
Final project for INFM737 (Spring 2018). University of Maryland, College Park.There are neglections ...
This record is part of a wider collection that captures the online course, Citizen Science Projects:...
Given vehicle tracking data, loyalty and credit card logs of employees from a fictitious company, GA...
This summary describes a tool we created for the Grand Challenge of the VAST Challenge 2015. We pres...
Crimes, forest fires, accidents, infectious diseases, human interactions with mobile devices (e.g., ...
The 2016 VAST Challenge returns to the (fictional) island of Kronos to pose three Mini-Challenges. I...
This report describes the joint entry from Middlesex University and the University of Leeds for Mini...
The 2014 VAST Mini-challenge 1 asked participants to summarise the structure of two organisations wi...
Social Media is a very good example of a large communication network. Typically, most data generated...
The VAST 2008 Challenge consisted of four heterogeneous synthetic data sets each organized into sepa...
The manual process of collecting and labelling data required for machine learning tasks is labour-in...
The Virtual Worlds Exploratorium is a multidisciplinary project dedicated to the study of communicat...
International audienceWe report on the process and design of our visual analytics graph analysis cha...
Recent technological advances have made it possible to more accurately understand visitor travel pat...
Abstract: Agent-based simulation is a technology which is being used as a decision support tool for ...
Final project for INFM737 (Spring 2018). University of Maryland, College Park.There are neglections ...
This record is part of a wider collection that captures the online course, Citizen Science Projects:...
Given vehicle tracking data, loyalty and credit card logs of employees from a fictitious company, GA...
This summary describes a tool we created for the Grand Challenge of the VAST Challenge 2015. We pres...
Crimes, forest fires, accidents, infectious diseases, human interactions with mobile devices (e.g., ...