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Efficient Mining of Regional Movement Patterns in Semantic Trajectories

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Title: Efficient Mining of Regional Movement Patterns in Semantic Trajectories
Authors: Heinis
Item 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.
Issue Date: 1-Sep-2017
Date of Acceptance: 1-Aug-2017
URI: http://hdl.handle.net/10044/1/53701
DOI: https://dx.doi.org/10.14778/3151106.3151111
ISSN: 2150-8097
Publisher: VLDB Endowment
Start Page: 2073
End Page: 2084
Journal / Book Title: Proceedings of the 43rd International Conference on Very Large Data Bases, Munich, Germany
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.
Sponsor/Funder: Engineering & Physical Science Research Council (E
European Research Office
Funder's Grant Number: EP/N023242/1
Conference Name: Conference on Very Large Databases
Publication Status: Published
Start Date: 2017-08-28
Finish Date: 2017-09-01
Conference Place: Munich, Germany
Appears in Collections:Computing
Faculty of Engineering