Efficient Mining of Regional Movement Patterns in Semantic Trajectories
File(s)
Published version
Accepted version
Author(s)
Choi, Dong-Wan
Pei, Jian
Heinis, Thomas
Type
Conference Paper
Abstract
Semantic trajectory pattern mining is becoming more and more important with the rapidly growing volumes of semantically rich trajectory data. Extracting sequential patterns in semantic trajectories plays a key role in understanding semantic behaviour of human movement, which can widely be used in many applications such as location-based advertising, road capacity optimisation, and urban planning. However, most of existing works on semantic trajectory pattern mining focus on the entire spatial area, leading to missing some locally significant patterns within a region. Based on this motivation, this paper studies a regional semantic trajectory pattern mining problem, aiming at identifying all the regional sequential patterns in semantic trajectories. Specifically, we propose a new density scheme to quantify the frequency of a particular pattern in space, and thereby formulate a new mining problem of finding all the regions in which such a pattern densely occurs. For the proposed problem, we develop an ecient mining algorithm, called RegMiner (Regional Semantic Trajectory Pattern Miner), which e↵ectively reveals movement patterns that are locally frequent in such a region but not necessarily dominant in the entire space. Our empirical study using real trajectory data shows that RegMiner finds many interesting local patterns that are hard to find by a state-of-the-art global pattern mining scheme, and it also runs several orders of magnitude faster than the global pattern mining algorithm.
Date Issued
2017-08-28
Date Acceptance
2017-08-28
Citation
PVLDB, 2017, 10, pp.2073-2084
ISSN
2150-8097
Publisher
VLDB Endowment
Start Page
2073
End Page
2084
Journal / Book Title
PVLDB
Volume
10
Issue
13
Copyright Statement
This work is licensed under the Creative Commons AttributionNonCommercial-NoDerivatives
4.0 International License. To view a copy
of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. For
any use beyond those covered by this license, obtain permission by emailing
info@vldb.org.
Proceedings of the VLDB Endowment, Vol. 10, No. 13
Copyright 2017 VLDB Endowment 2150-8097/17/08.
4.0 International License. To view a copy
of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. For
any use beyond those covered by this license, obtain permission by emailing
info@vldb.org.
Proceedings of the VLDB Endowment, Vol. 10, No. 13
Copyright 2017 VLDB Endowment 2150-8097/17/08.
Sponsor
Engineering & Physical Science Research Council (E
European Research Office
Grant Number
EP/N023242/1
720270
Source
43rd International Conference on Very Large Data Bases
Publication Status
Published
Start Date
2017-08-28
Finish Date
2017-09-01
Coverage Spatial
Munich, Germany