3,030 research outputs found
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
A Novel Framework for Online Amnesic Trajectory Compression in Resource-constrained Environments
State-of-the-art trajectory compression methods usually involve high
space-time complexity or yield unsatisfactory compression rates, leading to
rapid exhaustion of memory, computation, storage and energy resources. Their
ability is commonly limited when operating in a resource-constrained
environment especially when the data volume (even when compressed) far exceeds
the storage limit. Hence we propose a novel online framework for error-bounded
trajectory compression and ageing called the Amnesic Bounded Quadrant System
(ABQS), whose core is the Bounded Quadrant System (BQS) algorithm family that
includes a normal version (BQS), Fast version (FBQS), and a Progressive version
(PBQS). ABQS intelligently manages a given storage and compresses the
trajectories with different error tolerances subject to their ages. In the
experiments, we conduct comprehensive evaluations for the BQS algorithm family
and the ABQS framework. Using empirical GPS traces from flying foxes and cars,
and synthetic data from simulation, we demonstrate the effectiveness of the
standalone BQS algorithms in significantly reducing the time and space
complexity of trajectory compression, while greatly improving the compression
rates of the state-of-the-art algorithms (up to 45%). We also show that the
operational time of the target resource-constrained hardware platform can be
prolonged by up to 41%. We then verify that with ABQS, given data volumes that
are far greater than storage space, ABQS is able to achieve 15 to 400 times
smaller errors than the baselines. We also show that the algorithm is robust to
extreme trajectory shapes.Comment: arXiv admin note: substantial text overlap with arXiv:1412.032
Compact Trip Representation over Networks
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-46049-9_23[Abstract] We present a new Compact Trip Representation ( CTRCTR ) that allows us to manage users’ trips (moving objects) over networks. These could be public transportation networks (buses, subway, trains, and so on) where nodes are stations or stops, or road networks where nodes are intersections. CTRCTR represents the sequences of nodes and time instants in users’ trips. The spatial component is handled with a data structure based on the well-known Compressed Suffix Array ( CSACSA ), which provides both a compact representation and interesting indexing capabilities. We also represent the temporal component of the trips, that is, the time instants when users visit nodes in their trips. We create a sequence with these time instants, which are then self-indexed with a balanced Wavelet Matrix ( WMWM ). This gives us the ability to solve range-interval queries efficiently. We show how CTRCTR can solve relevant spatial and spatio-temporal queries over large sets of trajectories. Finally, we also provide experimental results to show the space requirements and query efficiency of CTRCTR .Ministerio de EconomĂa y Competitividad; TIN2013-46238-C4-3-RMinisterio de EconomĂa y Competitividad; TIN2013-47090-C3-3-PMinisterio de EconomĂa y Competitividad; IDI-20141259Ministerio de EconomĂa y Competitividad; ITC-20151305Ministerio de EconomĂa y Competitividad; ITC-20151247Xunta de Galicia; GRC2013/053Chile.Fondo Nacional de Desarrollo CientĂfico y TecnolĂłgico; 1140428Chile. Instituto de Sistemas Complejos de IngenierĂa ; FBO 1
PPQ-Trajectory : spatio-temporal quantization for querying in large trajectory repositories
We present PPQ-trajectory, a spatio-temporal quantization based solution for querying large dynamic trajectory data. PPQ-trajectory includes a partition-wise predictive quantizer (PPQ) that generates an error-bounded codebook with autocorrelation and spatial proximity-based partitions. The codebook is indexed to run approximate and exact spatio-temporal queries over compressed trajectories. PPQ-trajectory includes a coordinate quadtree coding for the codebook with support for exact queries. An incremental temporal partition-based index is utilised to avoid full reconstruction of trajectories during queries. An extensive set of experimental results for spatio-temporal queries on real trajectory datasets is presented. PPQ-trajectory shows significant improvements over the alternatives with respect to several performance measures, including the accuracy of results when the summary is used directly to provide approximate query results, the spatial deviation with which spatio-temporal path queries can be answered when the summary is used as an index, and the time taken to construct the summary. Superior results on the quality of the summary and the compression ratio are also demonstrated
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