Next Article in Journal
CHS Priority Planning Tool (CPPT)—A GIS Model for Defining Hydrographic Survey and Charting Priorities
Previous Article in Journal
A Scalable Architecture for Real-Time Stream Processing of Spatiotemporal IoT Stream Data—Performance Analysis on the Example of Map Matching
Open AccessArticle

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

by 1,*, 1, 2 and 3
1
Department of Geography and the Environment, University of Denver, Denver, CO 80208, USA
2
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430070, Hubei, China
3
Department of Computer Science, University of Denver, Denver, CO 80208, USA
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(7), 239; https://doi.org/10.3390/ijgi7070239
Received: 12 May 2018 / Revised: 8 June 2018 / Accepted: 18 June 2018 / Published: 21 June 2018
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. View Full-Text
Keywords: trajectory; similarity search; parallel computing; GPGPU; CUDA trajectory; similarity search; parallel computing; GPGPU; CUDA
Show Figures

Figure 1

MDPI and ACS Style

Li, J.; Wang, X.; Zhang, T.; Xu, Y. Efficient Parallel K Best Connected Trajectory (K-BCT) Query with GPGPU: A Combinatorial Min-Distance and Progressive Bounding Box Approach. ISPRS Int. J. Geo-Inf. 2018, 7, 239.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
ISPRS Int. J. Geo-Inf., EISSN 2220-9964, Published by MDPI AG
Back to TopTop