Online trajectory segmentation and summary with applications to visualization and retrieval

Yehezkel S. Resheff

doi:10.1109/BigData.2016.7840801

Online trajectory segmentation and summary with applications to visualization and retrieval

Yehezkel S. Resheff^*

^*Corresponding author for this work

Specialization in Data Science for Business Administration

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

8 Scopus citations

Abstract

Trajectory segmentation is the process of subdividing a trajectory into parts either by grouping points similar with respect to some measure of interest, or by minimizing a global objective function. Here we present a novel online algorithm for segmentation and summary, based on point density along the trajectory, and based on the nature of the naturally occurring structure of intermittent bouts of locomotive and local activity. We show an application to visualization of trajectory datasets, and discuss the use of the summary as an index allowing efficient queries which are otherwise impossible or computationally expensive, over very large datasets.

Original language	English
Title of host publication	Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
Editors	Ronay Ak, George Karypis, Yinglong Xia, Xiaohua Tony Hu, Philip S. Yu, James Joshi, Lyle Ungar, Ling Liu, Aki-Hiro Sato, Toyotaro Suzumura, Sudarsan Rachuri, Rama Govindaraju, Weijia Xu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1832-1840
Number of pages	9
ISBN (Electronic)	9781467390040
DOIs	https://doi.org/10.1109/BigData.2016.7840801
State	Published - 2016
Event	4th IEEE International Conference on Big Data, Big Data 2016 - Washington, United States Duration: 5 Dec 2016 → 8 Dec 2016

Publication series

Name	Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016

Conference

Conference	4th IEEE International Conference on Big Data, Big Data 2016
Country/Territory	United States
City	Washington
Period	5/12/16 → 8/12/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

data-management
retrieval
trajectory analysis
visualization

Access to Document

10.1109/BigData.2016.7840801

Cite this

Resheff, Y. S. (2016). Online trajectory segmentation and summary with applications to visualization and retrieval. In R. Ak, G. Karypis, Y. Xia, X. T. Hu, P. S. Yu, J. Joshi, L. Ungar, L. Liu, A.-H. Sato, T. Suzumura, S. Rachuri, R. Govindaraju, & W. Xu (Eds.), Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016 (pp. 1832-1840). Article 7840801 (Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2016.7840801

Resheff, Yehezkel S. / Online trajectory segmentation and summary with applications to visualization and retrieval. Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016. editor / Ronay Ak ; George Karypis ; Yinglong Xia ; Xiaohua Tony Hu ; Philip S. Yu ; James Joshi ; Lyle Ungar ; Ling Liu ; Aki-Hiro Sato ; Toyotaro Suzumura ; Sudarsan Rachuri ; Rama Govindaraju ; Weijia Xu. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1832-1840 (Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016).

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abstract = "Trajectory segmentation is the process of subdividing a trajectory into parts either by grouping points similar with respect to some measure of interest, or by minimizing a global objective function. Here we present a novel online algorithm for segmentation and summary, based on point density along the trajectory, and based on the nature of the naturally occurring structure of intermittent bouts of locomotive and local activity. We show an application to visualization of trajectory datasets, and discuss the use of the summary as an index allowing efficient queries which are otherwise impossible or computationally expensive, over very large datasets.",

keywords = "data-management, retrieval, trajectory analysis, visualization",

author = "Resheff, {Yehezkel S.}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 4th IEEE International Conference on Big Data, Big Data 2016 ; Conference date: 05-12-2016 Through 08-12-2016",

year = "2016",

doi = "10.1109/BigData.2016.7840801",

language = "אנגלית",

series = "Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Resheff, YS 2016, Online trajectory segmentation and summary with applications to visualization and retrieval. in R Ak, G Karypis, Y Xia, XT Hu, PS Yu, J Joshi, L Ungar, L Liu, A-H Sato, T Suzumura, S Rachuri, R Govindaraju & W Xu (eds), Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016., 7840801, Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016, Institute of Electrical and Electronics Engineers Inc., pp. 1832-1840, 4th IEEE International Conference on Big Data, Big Data 2016, Washington, United States, 5/12/16. https://doi.org/10.1109/BigData.2016.7840801

Online trajectory segmentation and summary with applications to visualization and retrieval. / Resheff, Yehezkel S.
Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016. ed. / Ronay Ak; George Karypis; Yinglong Xia; Xiaohua Tony Hu; Philip S. Yu; James Joshi; Lyle Ungar; Ling Liu; Aki-Hiro Sato; Toyotaro Suzumura; Sudarsan Rachuri; Rama Govindaraju; Weijia Xu. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1832-1840 7840801 (Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016).

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

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AB - Trajectory segmentation is the process of subdividing a trajectory into parts either by grouping points similar with respect to some measure of interest, or by minimizing a global objective function. Here we present a novel online algorithm for segmentation and summary, based on point density along the trajectory, and based on the nature of the naturally occurring structure of intermittent bouts of locomotive and local activity. We show an application to visualization of trajectory datasets, and discuss the use of the summary as an index allowing efficient queries which are otherwise impossible or computationally expensive, over very large datasets.

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A2 - Joshi, James

A2 - Ungar, Lyle

A2 - Liu, Ling

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Resheff YS. Online trajectory segmentation and summary with applications to visualization and retrieval. In Ak R, Karypis G, Xia Y, Hu XT, Yu PS, Joshi J, Ungar L, Liu L, Sato AH, Suzumura T, Rachuri S, Govindaraju R, Xu W, editors, Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1832-1840. 7840801. (Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016). doi: 10.1109/BigData.2016.7840801

Online trajectory segmentation and summary with applications to visualization and retrieval

Abstract

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Bibliographical note

Keywords

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