Large-Scale Trajectory Analysis via Feature Vectors
Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States), Jessica Jones, USDOD, Benjamin Newton, Kyra Wisniewski, Andrew Wilson, Melissa Ginaldi, Cleveland Waddell, Kenneth Goss, Katrina Ward, Mark Daniel Rintoul · 2021
The explosion of both sensors and GPS-enabled devices has resulted in position/time data being the next big frontier for data analytics. However, many of the problems associated with large numbers of trajectories do not necessarily have an analog with many of the historic big-data applications such as text and image analysis. Modern trajectory analytics exploits much of the cutting-edge research in machine-learning, statistics, computational geometry and other disciplines. We will show that for doing trajectory analytics at scale, it is necessary to fundamentally change the way the information is represented through a feature-vector approach. We then demonstrate the ability to solve large trajectory analytics problems using this representation.