1,506 research outputs found
GraCT: A Grammar based Compressed representation of Trajectories
We present a compressed data structure to store free trajectories of moving
objects (ships over the sea, for example) allowing spatio-temporal queries. Our
method, GraCT, uses a -tree to store the absolute positions of all objects
at regular time intervals (snapshots), whereas the positions between snapshots
are represented as logs of relative movements compressed with Re-Pair. Our
experimental evaluation shows important savings in space and time with respect
to a fair baseline.Comment: This research has received funding from the European Union's Horizon
2020 research and innovation programme under the Marie Sk{\l}odowska-Curie
Actions H2020-MSCA-RISE-2015 BIRDS GA No. 69094
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
Concept of a laser-plasma based electron source for sub-10 fs electron diffraction
We propose a new concept of an electron source for ultrafast electron
diffraction with sub-10~fs temporal resolution. Electrons are generated in a
laser-plasma accelerator, able to deliver femtosecond electron bunches at 5 MeV
energy with kHz repetition rate. The possibility of producing this electron
source is demonstrated using Particle-In-Cell simulations. We then use particle
tracking simulations to show that this electron beam can be transported and
manipulated in a realistic beamline, in order to reach parameters suitable for
electron diffraction. The beamline consists of realistic static magnetic optics
and introduces no temporal jitter. We demonstrate numerically that electron
bunches with 5~fs duration and containing 1.5~fC per bunch can be produced,
with a transverse coherence length exceeding 2~nm, as required for electron
diffraction
An Overview of Moving Object Trajectory Compression Algorithms
Compression technology is an efficient way to reserve useful and valuable data as well as remove redundant and inessential data from datasets. With the development of RFID and GPS devices, more and more moving objects can be traced and their trajectories can be recorded. However, the exponential increase in the amount of such trajectory data has caused a series of problems in the storage, processing, and analysis of data. Therefore, moving object trajectory compression undoubtedly becomes one of the hotspots in moving object data mining. To provide an overview, we survey and summarize the development and trend of moving object compression and analyze typical moving object compression algorithms presented in recent years. In this paper, we firstly summarize the strategies and implementation processes of classical moving object compression algorithms. Secondly, the related definitions about moving objects and their trajectories are discussed. Thirdly, the validation criteria are introduced for evaluating the performance and efficiency of compression algorithms. Finally, some application scenarios are also summarized to point out the potential application in the future. It is hoped that this research will serve as the steppingstone for those interested in advancing moving objects mining
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