Probabilistic reachability query in evolving spatiotemporal contact networks of moving objects

With the rapid development of location sensors, it is now possible to study how various items (such as viruses and messages) spread across populations of moving objects at scale. In such applications, two objects are considered in-contact while they are sufficiently close to each other. Such a dynamic network of objects, so-called a "contact network". In this paper, we define and study probabilistic reachability queries in uncertain contact networks, where contacts between objects are probabilistic. A probabilistic reachability query verifies whether two objects are "reachable" with a probability no less than a threshold η. We introduce Optimized Spatiotemporal Tree Cover, an index structure that leverages the spatiotemporal properties of the contact network to enable efficient processing of the reachability queries on large uncertain contact networks. With an extensive study using both real and synthetic datasets, we demonstrate superiority of our proposed solution versus a baseline solution (i.e., Monte Carlo Sampling) and the only other existing solution for reachability queries on uncertain contact networks, with 350% and 150% improvement in query processing time on average, respectively.