  An increasing amount of trajectory data is being annotated with text
descriptions to better capture the semantics associated with locations. The
fusion of spatial locations and text descriptions in trajectories engenders a
new type of top-$k$ queries that take into account both aspects. Each
trajectory in consideration consists of a sequence of geo-spatial locations
associated with text descriptions. Given a user location $\lambda$ and a
keyword set $\psi$, a top-$k$ query returns $k$ trajectories whose text
descriptions cover the keywords $\psi$ and that have the shortest match
distance. To the best of our knowledge, previous research on querying
trajectory databases has focused on trajectory data without any text
description, and no existing work has studied such kind of top-$k$ queries on
trajectories. This paper proposes one novel method for efficiently computing
top-$k$ trajectories. The method is developed based on a new hybrid index,
cell-keyword conscious B$^+$-tree, denoted by \cellbtree, which enables us to
exploit both text relevance and location proximity to facilitate efficient and
effective query processing. The results of our extensive empirical studies with
an implementation of the proposed algorithms on BerkeleyDB demonstrate that our
proposed methods are capable of achieving excellent performance and good
scalability.
