Spatiotemporal Pattern Mining Technique for Location-Based Service System

다국어 초록 (Multilingual 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.

참고문헌 (Reference)

1 Kyoung Wook Min, "Multilevel Location Trigger in Distributed Mobile Environments for Location-Based Services" 한국전자통신연구원 29 (29): 107-109, 2007

2 S. Jensen, "Multidimensional Data Modeling for Location-Based Services" 13 (13): 1-21, 2004

3 N. Mamoulis, "Mining, Indexing, and Querying Historical Spatiotemporal Data" 236-245, 2004

4 R. Srikant, "Mining Sequential Patterns: Generalizations and Performance Improvements" 3-17, 1996

5 J.F. Allen, "Maintaining Knowledge about Temporal Intervals" 26 : 832-843, 1983

6 R. Agrawal, "Fast Algorithms for Mining Association Rules" 487-499, 1994

7 I. Tsoukatos, "Efficient Mining of Spatiotemporal Patterns" LNCS 425-442, 2001

8 D. Katsaros, "Clustering Mobile Trajectories for Resource Allocation in Mobile Environments" 319-329, 2003

9 D. Pfoser, "Capturing the Uncertainty of Moving- Object Representations" 111-132, 1999

10 N. Meratnia, "Aggregation and Comparison of Trajectories" ACM 49-54, 2002

11 M. Ester, "A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise" 226-231, 1996

12 G. Yava, "A Data Mining Approach for Location Prediction in Mobile Environments" 54 (54): 121-146, 2005