Efficient Parallel K Best Connected Trajectory (K-BCT) Query with GPGPU: A Combinatorial Min-Distance and Progressive Bounding Box Approach

Performing similarity analysis on trajectories consisting of massive numbers of tracking points is computationally challenging. We introduce a progressive minimum bounding rectangle (MBR) and minimum distance (MINDIST) approach to process the K Best Connected Trajectory (K-BCT) query, which aims to...

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Bibliographic Details
Published inISPRS international journal of geo-information Vol. 7; no. 7; p. 239
Main Authors Li, Jing, Wang, Xuantong, Zhang, Tong, Xu, You
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.07.2018
Subjects
Big Data
Combinatorial analysis
Computation
Computer science
CUDA
Data analysis
Detection
Distance
GPGPU
Graphics processing units
Human motion
Information systems
International conferences
Internet
Methods
Microprocessors
parallel computing
Queries
Scientists
Ships
Similarity
Similarity measures
similarity search
Target recognition
Tracking
Traffic congestion
trajectory
Trajectory analysis
Trajectory measurement
Web services
New York
United States > US
China
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Summary:Performing similarity analysis on trajectories consisting of massive numbers of tracking points is computationally challenging. We introduce a progressive minimum bounding rectangle (MBR) and minimum distance (MINDIST) approach to process the K Best Connected Trajectory (K-BCT) query, which aims to find the top K similarity trajectories to a given query trajectory. Our approach has three unique features to speed up the query. First, the approach builds a series of progressive MBRs from the query trajectory to determine the order of reference trajectories to identify the target top K reference trajectories at an earlier stage. Second, this method introduces a grid-based search method to speed up the matched point detection between two trajectories for similarity measures. Third, this approach further leverages the many-core computing power of Graphical Processing Unit (GPU) devices to perform the query in a parallel manner. We have conducted tests with ship tracking data and human movement data using GPU instances from Amazon Web Services. Preliminary results indicate that (a) parallel computing has greatly improved the efficiency of the query, and (b) our optimized approach can further speedup the computation compared to parallel implementations.
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi7070239