GTraclus: A Local Trajectory Clustering Algorithm for GPUSA

Hamza Mustafa, Clark Barrus, Eleazar Leal, Le Gruenwald

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Due to the high availability of location-based sensors like GPS, it has been possible to collect large amounts of spatio-Temporal data in the form of trajectories, each of which is a sequence of spatial locations that a moving object occupies in space as time progresses. Many applications, such as intelligent transportation systems and urban planning, can benefit from clustering the trajectories of cars in each locality of a city in order to learn about traffic behavior in each neighborhood. However, the immense and ever-increasing volume of trajectory data and the concept drift present in city traffic constitute scalability challenges that have not been addressed. In order to fill this gap, we propose the first GPU algorithm for local trajectory clustering, called GTraclus. We present a parallelized trajectory partitioning algorithm which simplifies trajectories into line segments using the Minimum Description Length (MDL) principle. We evaluated our proposed algorithm using two large real-life trajectory datasets and compared it against a multicore CPU version, which we call MC-Traclus, of the popular trajectory clustering algorithm, Traclus; our experiments showed that GTraclus had on average up to 24X faster execution time when compared against MC-Traclus.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE 37th International Conference on Data Engineering Workshops, ICDEW 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages30-35
Number of pages6
ISBN (Electronic)9781665448901
DOIs
StatePublished - Apr 2021
Event37th IEEE International Conference on Data Engineering Workshops, ICDEW 2021 - Virtual, Chania, Greece
Duration: Apr 19 2021Apr 22 2021

Publication series

NameProceedings - 2021 IEEE 37th International Conference on Data Engineering Workshops, ICDEW 2021

Conference

Conference37th IEEE International Conference on Data Engineering Workshops, ICDEW 2021
Country/TerritoryGreece
CityVirtual, Chania
Period4/19/214/22/21

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work is supported in part by the National Science Foundation under Grant No. 1302439 and 1302423.

Publisher Copyright:
© 2021 IEEE.

Keywords

  • GPU
  • spatio-Temporal data
  • trajectory clustering

Fingerprint

Dive into the research topics of 'GTraclus: A Local Trajectory Clustering Algorithm for GPUSA'. Together they form a unique fingerprint.

Cite this