8,817 research outputs found
Efficient Spatial Keyword Search in Trajectory Databases
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- 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 and a
keyword set , a top- query returns trajectories whose text
descriptions cover the keywords 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- queries on
trajectories. This paper proposes one novel method for efficiently computing
top- 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.Comment: 12 page
CiNCT: Compression and retrieval for massive vehicular trajectories via relative movement labeling
In this paper, we present a compressed data structure for moving object
trajectories in a road network, which are represented as sequences of road
edges. Unlike existing compression methods for trajectories in a network, our
method supports pattern matching and decompression from an arbitrary position
while retaining a high compressibility with theoretical guarantees.
Specifically, our method is based on FM-index, a fast and compact data
structure for pattern matching. To enhance the compression, we incorporate the
sparsity of road networks into the data structure. In particular, we present
the novel concepts of relative movement labeling and PseudoRank, each
contributing to significant reductions in data size and query processing time.
Our theoretical analysis and experimental studies reveal the advantages of our
proposed method as compared to existing trajectory compression methods and
FM-index variants
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