Impact of Trajectory Segmentation on Discovering Trajectory Sequential Patterns

Somayah Karsoum, Le Gruenwald, Eleazar Leal

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

Abstract

Nowadays, location-aware devices, such as GPS, provide a huge volume of spatial-temporal data. Analyzing the data to understand the behavior of objects (e.g. people) could be beneficial in many application areas. Due to the spatial and temporal nature and their complexity, researchers have developed various data mining techniques such as trajectory segmentation, which splits the trajectories into sub-trajectories, to prepare them for the mining step. A central issue in discovering knowledge is choosing an appropriate trajectory segmentation technique. In this paper, we provide a comparative study on two trajectory segmentation techniques, density-based and grid-based, when applied to sequential patterns discovery. We conducted experiments using two real-life datasets to evaluate the performance of the methods in terms of execution time and their impact on discovering the sequential patterns. The experimental results showed that the density-based is more efficient, while the grid-based is more effective.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
EditorsYang Song, Bing Liu, Kisung Lee, Naoki Abe, Calton Pu, Mu Qiao, Nesreen Ahmed, Donald Kossmann, Jeffrey Saltz, Jiliang Tang, Jingrui He, Huan Liu, Xiaohua Hu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3432-3441
Number of pages10
ISBN (Electronic)9781538650356
DOIs
StatePublished - Jan 22 2019
Event2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States
Duration: Dec 10 2018Dec 13 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

Conference

Conference2018 IEEE International Conference on Big Data, Big Data 2018
CountryUnited States
CitySeattle
Period12/10/1812/13/18

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Keywords

  • Sequential trajectory patterns
  • Trajectory segmentation
  • density-based
  • grid-based

Cite this

Karsoum, S., Gruenwald, L., & Leal, E. (2019). Impact of Trajectory Segmentation on Discovering Trajectory Sequential Patterns. In Y. Song, B. Liu, K. Lee, N. Abe, C. Pu, M. Qiao, N. Ahmed, D. Kossmann, J. Saltz, J. Tang, J. He, H. Liu, & X. Hu (Eds.), Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 (pp. 3432-3441). [8622209] (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2018.8622209