Efficient Processing of Probabilistic Single and Batch Reachability Queries in Large and Evolving Spatiotemporal Contact Networks

With the rapid development of location sensors, it is now possible to accurately study how various items (such as viruses or messages) spread across populations of moving objects. In such applications, an item can propagate through the object population where two objects are close. Such a dynamic network of objects called a "contact network". In this paper, we define and study a family of 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. To enable efficient processing of probabilistic reachability queries on large uncertain contact networks, first, we present a series-parallel reduction technique that significantly reduces the size of the input uncertain contact network in order to shrink the search space while maintaining accuracy and second, we introduce Optimized Spatiotemporal Tree Cover, an index structure that leverages the spatiotemporal properties of the contact network. With an extensive analytical and empirical study, we demonstrate superiority of our proposed solution versus a baseline solution (i.e., Monte Carlo sampling) and the only other existing solution with 400% and 200% improvement in query processing time on average, respectively.