Sampling Stable Properties of Massive Track Datasets

Abstract

Analysis of massive track datasets is a challenging problem, especially when examining n-way relations inherent in social networks. In this paper, we explore ways in which stable properties of sensor observations can be extracted and visualized using a statistical sampling of features from a very large track dataset, using very little ground truth or outside knowledge. Special attention is given to methods that are likely to scale well beyond the size of the Mitsubishi dataset.