ETRI-Knowledge Sharing Plaform

KOREAN
논문 검색
Type SCI
Year ~ Keyword

Detail

Journal Article Spatiotemporal Pattern Mining Technique for Location-Based Service System
Cited 20 time in scopus Download 1 time Share share facebook twitter linkedin kakaostory
Authors
Thi Hong Nhan Vu, Jun Wook Lee, Keun Ho Ryu
Issue Date
2008-06
Citation
ETRI Journal, v.30, no.3, pp.421-431
ISSN
1225-6463
Publisher
한국전자통신연구원 (ETRI)
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.4218/etrij.08.0107.0238
Project Code
07MD1400, Development of USN Middleware Platform Technology, Park Jong-Hyun
Abstract
In this paper, we offer a new technique to discover frequent spatiotemporal patterns from a moving object database. Though the search space for spatiotemporal knowledge is extremely challenging, imposing spatial and timing constraints on moving sequences makes the computation feasible. The proposed technique includes two algorithms, AllMOP and MaxMOP, to find all frequent patterns and maximal patterns, respectively. In addition, to support the service provider in sending information to a user in a push-driven manner, we propose a rule-based location prediction technique to predict the future location of the user. The idea is to employ the algorithm AllMOP to discover the frequent movement patterns in the user's historical movements, from which frequent movement rules are generated. These rules are then used to estimate the future location of the user. The performance is assessed with respect to precision and recall. The proposed techniques could be quite efficiently applied in a location-based service (LBS) system in which diverse types of data are integrated to support a variety of LBSs.
KSP Keywords
Location Prediction, Location-Based Services, Movement patterns, Moving object database, Precision and recall, Prediction technique, Rule-based, Search Space, Service Provider, Service System, frequent patterns