DynMDL: A Parallel Trajectory Segmentation Algorithm

Eleazar Leal; Le Gruenwald

doi:10.1109/BigDataCongress.2018.00036

DynMDL: A Parallel Trajectory Segmentation Algorithm

Eleazar Leal, Le Gruenwald

Computer Science (Duluth)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Scopus citations

Abstract

The purpose of trajectory segmentation algorithms is to replace an input trajectory by a sub-trajectory with fewer points than the input, but that is also a good approximation to the original trajectory. As such, trajectory segmentation is an essential pre-processing step for trajectory mining algorithms, such as clustering. Among the segmentation strategies that are commonly used for trajectory clustering is Minimum Description Length (MDL)-based segmentation, which consists in finding a sub-trajectory such that the sum of its distance to the input trajectory and its overall length is minimum. However, there are no efficient algorithms for optimal MDL-based segmentation; there are only approximate algorithms. In this work we fill this gap by proposing a parallel multicore algorithm for MDL-based trajectory segmentation. We use three real-life datasets to show that our algorithm achieves optimal MDL, and compare its performance against Traclus, the state-of-the-art approximate Description Length (DL) segmentation algorithm.

Original language	English (US)
Title of host publication	Proceedings - 2018 IEEE International Congress on Big Data, BigData Congress 2018 - Part of the 2018 IEEE World Congress on Services
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	215-218
Number of pages	4
ISBN (Electronic)	9781538672327
DOIs	https://doi.org/10.1109/BigDataCongress.2018.00036
State	Published - Sep 7 2018
Event	7th IEEE International Congress on Big Data, BigData Congress 2018 - San Francisco, United States Duration: Jul 2 2018 → Jul 7 2018

Publication series

Name	Proceedings - 2018 IEEE International Congress on Big Data, BigData Congress 2018 - Part of the 2018 IEEE World Congress on Services

Other

Other	7th IEEE International Congress on Big Data, BigData Congress 2018
Country/Territory	United States
City	San Francisco
Period	7/2/18 → 7/7/18

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work is supported in part by the National Science Foundation under Grant No. 1302439 and 1302423.We also thank Martin Werner [8] and Simon Zu.

Funding Information:
This work is supported in part by the National Science Foundation under Grant No. 1302439 and 1302423. We also thank Martin Werner [8] and Simon Zu.

Publisher Copyright:
© 2018 IEEE.

Keywords

MDL principle
multicore algorithms
parallel computing
trajectory data
trajectory segmentation

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Leal, E., & Gruenwald, L. (2018). DynMDL: A Parallel Trajectory Segmentation Algorithm. In Proceedings - 2018 IEEE International Congress on Big Data, BigData Congress 2018 - Part of the 2018 IEEE World Congress on Services (pp. 215-218). Article 8457752 (Proceedings - 2018 IEEE International Congress on Big Data, BigData Congress 2018 - Part of the 2018 IEEE World Congress on Services). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigDataCongress.2018.00036

DynMDL: A Parallel Trajectory Segmentation Algorithm. / Leal, Eleazar; Gruenwald, Le.
Proceedings - 2018 IEEE International Congress on Big Data, BigData Congress 2018 - Part of the 2018 IEEE World Congress on Services. Institute of Electrical and Electronics Engineers Inc., 2018. p. 215-218 8457752 (Proceedings - 2018 IEEE International Congress on Big Data, BigData Congress 2018 - Part of the 2018 IEEE World Congress on Services).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Leal, E & Gruenwald, L 2018, DynMDL: A Parallel Trajectory Segmentation Algorithm. in Proceedings - 2018 IEEE International Congress on Big Data, BigData Congress 2018 - Part of the 2018 IEEE World Congress on Services., 8457752, Proceedings - 2018 IEEE International Congress on Big Data, BigData Congress 2018 - Part of the 2018 IEEE World Congress on Services, Institute of Electrical and Electronics Engineers Inc., pp. 215-218, 7th IEEE International Congress on Big Data, BigData Congress 2018, San Francisco, United States, 7/2/18. https://doi.org/10.1109/BigDataCongress.2018.00036

Leal E, Gruenwald L. DynMDL: A Parallel Trajectory Segmentation Algorithm. In Proceedings - 2018 IEEE International Congress on Big Data, BigData Congress 2018 - Part of the 2018 IEEE World Congress on Services. Institute of Electrical and Electronics Engineers Inc. 2018. p. 215-218. 8457752. (Proceedings - 2018 IEEE International Congress on Big Data, BigData Congress 2018 - Part of the 2018 IEEE World Congress on Services). doi: 10.1109/BigDataCongress.2018.00036

Leal, Eleazar ; Gruenwald, Le. / DynMDL : A Parallel Trajectory Segmentation Algorithm. Proceedings - 2018 IEEE International Congress on Big Data, BigData Congress 2018 - Part of the 2018 IEEE World Congress on Services. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 215-218 (Proceedings - 2018 IEEE International Congress on Big Data, BigData Congress 2018 - Part of the 2018 IEEE World Congress on Services).

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DynMDL: A Parallel Trajectory Segmentation Algorithm

Abstract

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