  In this paper we propose and study the problem of trajectory-driven
influential billboard placement: given a set of billboards $U$ (each with a
location and a cost), a database of trajectories $\mathcal{T}$ and a budget
$L$, find a set of billboards within the budget to influence the largest number
of trajectories. One core challenge is to identify and reduce the overlap of
the influence from different billboards to the same trajectories, while keeping
the budget constraint into consideration. We show that this problem is NP-hard
and present an enumeration based algorithm with $(1-1/e)$ approximation ratio.
However, the enumeration should be very costly when $|U|$ is large. By
exploiting the locality property of billboards' influence, we propose a
partition-based framework PartSel. PartSel partitions $U$ into a set of small
clusters, computes the locally influential billboards for each cluster, and
merges them to generate the global solution. Since the local solutions can be
obtained much more efficient than the global one, PartSel should reduce the
computation cost greatly; meanwhile it achieves a non-trivial approximation
ratio guarantee. Then we propose a LazyProbe method to further prune billboards
with low marginal influence, while achieving the same approximation ratio as
PartSel. Experiments on real datasets verify the efficiency and effectiveness
of our methods.
