  With the advent of reliable positioning technologies and prevalence of
location-based services, it is now feasible to accurately study the propagation
of items such as infectious viruses, sensitive information pieces, and malwares
through a population of moving objects, e.g., individuals, mobile devices, and
vehicles. In such application scenarios, an item passes between two objects
when the objects are sufficiently close (i.e., when they are, so-called, in
contact), and hence once an item is initiated, it can penetrate the object
population through the evolving network of contacts among objects, termed
contact network. In this paper, for the first time we define and study
reachability queries in large (i.e., disk-resident) contact datasets which
record the movement of a (potentially large) set of objects moving in a spatial
environment over an extended time period. A reachability query verifies whether
two objects are "reachable" through the evolving contact network represented by
such contact datasets. We propose two contact-dataset indexes that enable
efficient evaluation of such queries despite the potentially humongous size of
the contact datasets. With the first index, termed ReachGrid, at the query time
only a small necessary portion of the contact network which is required for
reachability evaluation is constructed and traversed. With the second approach,
termed ReachGraph, we precompute reachability at different scales and leverage
these precalculations at the query time for efficient query processing. We
optimize the placement of both indexes on disk to enable efficient index
traversal during query processing. We study the pros and cons of our proposed
approaches by performing extensive experiments with both real and synthetic
data. Based on our experimental results, our proposed approaches outperform
existing reachability query processing techniques in contact n...[truncated].
