Making sense of trajectory data: a partition-and-summarization approach

Su, Han, Zheng, Kai, Zeng, Kai, Huang, Jiamin, Sadiq, Shazia, Yuan, Nicholas Jing and Zhou, Xiaofang (2015). Making sense of trajectory data: a partition-and-summarization approach. In: 2015 IEEE 31st International Conference on Data Engineering, ICDE 2015. IEEE International Conference on Data Engineering, Seoul, South Korea, (963-974). 13-17 April 2015. doi:10.1109/ICDE.2015.7113348

Author Su, Han
Zheng, Kai
Zeng, Kai
Huang, Jiamin
Sadiq, Shazia
Yuan, Nicholas Jing
Zhou, Xiaofang
Title of paper Making sense of trajectory data: a partition-and-summarization approach
Conference name IEEE International Conference on Data Engineering
Conference location Seoul, South Korea
Conference dates 13-17 April 2015
Convener IEEE
Proceedings title 2015 IEEE 31st International Conference on Data Engineering, ICDE 2015   Check publisher's open access policy
Journal name Proceedings - International Conference on Data Engineering   Check publisher's open access policy
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2015
Sub-type Fully published paper
DOI 10.1109/ICDE.2015.7113348
Open Access Status Not Open Access
ISBN 9781479979639
ISSN 1084-4627
Volume 2015-May
Start page 963
End page 974
Total pages 12
Abstract/Summary Due to the prevalence of GPS-enabled devices and wireless communication technology, spatial trajectories that describe the movement history of moving objects are being generated and accumulated at an unprecedented pace. However, a raw trajectory in the form of sequence of timestamped locations does not make much sense for humans without semantic representation. In this work we aim to facilitate human's understanding of a raw trajectory by automatically generating a short text to describe it. By formulating this task as the problem of adaptive trajectory segmentation and feature selection, we propose a partition-and-summarization framework. In the partition phase, we first define a set of features for each trajectory segment and then derive an optimal partition with the aim to make the segments within each partition as homogeneous as possible in terms of their features. In the summarization phase, for each partition we select the most interesting features by comparing against the common behaviours of historical trajectories on the same route and generate short text description for these features. For empirical study, we apply our solution to a real trajectory dataset and have found that the generated text can effectively reflect the important parts in a trajectory.
Subjects 1710 Information Systems
1711 Signal Processing
1712 Software
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

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