Browse > Article

Temporal Pattern Mining of Moving Objects for Location based Services  

Lee, Jun-Uk (Dept. of Computer Engineering, Chungbuk National University)
Baek, Ok-Hyeon (Agency for Defense Development)
Ryu, Geun-Ho (Dept. of Electrical Elecronic Computer Engineering, Chungbuk National University)
Publication Information
Journal of KIISE:Databases / v.29, no.5, 2002 , pp. 335-346 More about this Journal
Abstract
LBS(Location Based Services) provide the location-based information to its mobile users. The primary functionality of these services is to provide useful information to its users at a minimum cost of resources. The functionality can be implemented through data mining techniques. However, conventional data mining researches have not been considered spatial and temporal aspects of data simultaneously. Therefore, these techniques are inappropriate to apply on the objects of LBS, which change spatial attributes over time. In this paper, we propose a new data mining technique for identifying the temporal patterns from the series of the locations of moving objects that have both temporal and spatial dimension. We use a spatial operation of contains to generalize the location of moving point and apply time constraints between the locations of a moving object to make a valid moving sequence. Finally, the spatio-temporal technique proposed in this paper is very practical approach in not only providing more useful knowledge to LBS, but also improving the quality of the services.
Keywords
Temporal Pattern Mining; Moving Object; Location based service; Moving Pattern;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Novel Approaches in Query Processing for Moving Objects /
[ D.Pfoser;C.S.Jensen;Y.Theodoridis ] / CHOROCHRONOS Technical Report CH-00-03
2 J.S. Song,Y. J. Lee, and K. H. Ryu, 'Discovering Temporal Relation Rules from Interval Data,' submitted to the ETRI Journal, 2001
3 안윤애, 류근호, '이동 객체의 불확실한 위치 정보 관리', 충북대학교 컴퓨터정보통신 연구, 제 9권, 제 1호, pp.81-91, 2001
4 Seong Seung Park, Yun Ae Ahn, and Keun Ho Ryu, 'Moving Objects Spatiotemporal Reasoning Model for Battlefield Analysis,' In Proc. of Military, Government and Aerospace Simulation part of ASTC2001, pp.108-113, Apr., 2001
5 M. Erwig, R.H. Guting, M. Schneider, and M. Vazirgiannis, 'Spatio-Temporal Data Types : An Approach to Modeling and Querying Moving Objects in Databases,' GeoInformation, Vol.3, No.3, pp. 269-296, 1999   DOI   ScienceOn
6 R. H. Guting, M. H. Bohlen, M. Erwig, C. S. Jensen, N. A. Lorentzos, M. Schneider, and M. Vazirgiannis, 'A Foundation for Representing and Querying Moving Objects,' ACM Transactions on Database Systems, 2000   DOI   ScienceOn
7 L. Forlizzi, R. H. Guting, E. Nardelli and M. Schneider, 'A Data Model and Data Structures for Moving Objects Databases,' Proceedings of the ACM-SIGMOD International Conference on Management of Data, pp.319-330, 2000   DOI
8 M. N. Garofalakis, R. Rastogi, and K. Shim, 'SPIRIT : Sequential Pattern Mining with Regular Expression Constraints,' Proceedings of the 25th International Conference on Very Large Datbases, 1999
9 A. Guttman, R-trees: a Dynamic Index Structure for Spatial Searching,' In Preceedings of the ACM SIGMOD Conference on the Management of Data, pp.47-57, 1984   DOI
10 D. Pfoser, Y. Theodoridis, and C. S. Jensen, 'Indexing Trajectories of Moving Point Objects,' CHOROCHRONOS Technical Report CH-99-03, October, 1999
11 김욱, 지규인, 이장규, '위치 기반 무선 인터넷 서비스', Telecommunications Review, 제10권, 제6호, pp.1260-1269, 2000
12 O. Wolfson, A. P. Sistla, B. Xu, J, Zhou, and S. Chamberlain, 'DOMINO : Databases fOr MovlNg Objects tracking,' Proceedings of the ACM-SIGMOD International Conference on Management of Data, pp.547-549, 1999   DOI
13 InBae Oh, YoonAe Ahn, EungJae Lee, KeunHo Ryu, HongGi Kim, 'Prediction of Uncertain Moving Object Location,' In Proc. of Int. Conf. on East-Asian Language Processing and Internet Information Technology 2002 (EALPIIT2002 HANOI), 2002
14 류근호, 이준욱, 이용준, 'eCRM을 위한 시간 데이타 마이닝 기술', 한국 정보과학회 데이타베이스연구회지, 제17권, 제1호, 2001
15 이용준, 서성보, 류근호, 김혜규, '시간간격을 고려한 시간관계 규칙 탐사 기법', 한국정보과학회 논문지, 제28권, 제 3호, pp.301-314, 2001   과학기술학회마을
16 N. Beckmann, H. P. Kriegel, R. Schneider, and B. Seeger, 'The $R^*-tree$: An Efficient and Robust Access Method for Points and Rectangles,' ACM SIGMOD Conference, pp.322-331, 1990   DOI
17 D. Pfoser, C. S. Jensen, and Y. Theodoridis, 'Novel Approaches in Query Processing for Moving Objects,' CHOROCHRONOS Technical Report CH-00-3, February, 2000
18 R. J. Bayardo Jr., 'Efficiently Mining Long Patterns from Databases,' Proceedings of the ACM-SIGMOD International Conference on Management of Data, pp. 85-93, 1998   DOI
19 안병익 'LBS기술동향과 전망 - LBS 구조 및 구성', 한국지리정보, 10월호, pp.52-56, 2001
20 R. Agrawal and R. Srikant, 'Mining Sequential Patters,' Proceedings of the 11 th International Conference on Data Engineering, pp.3-14, 1995   DOI
21 J, Borges, M. Levene, 'A Fine Grained Heuristic to Capture Web Navigation Patterns,' SIGKDD Explorations, Vol.2, No.1, pp.40-50, 2000   DOI
22 J. Pei, J, Han, B. Mortazavi-Asl and H. Zhu, 'Mining Access Patterns Efficiently from Web Logs,' Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining( PAKDD), 2000   DOI   ScienceOn
23 R. Agrawal and R. Srikant, 'Fast Algorithms for Mining Association Rules,' Proceedings of the 20th International Conference on Very Large Databases, pp. 487-499, Santiago, Chile, 1994
24 R. Srikant and R. Agrawal, 'Mining Sequential Patterns:Generalizations and Performance Improvements,' International Conference on Extending Database Technology, Springer-Verlag, 1996   DOI
25 M.-S. Chen, J. S. Park, and P. S. Yu, 'Efficient Data Mining for Path Traversal Patterns,' IEEE Transactions on Knowledge and Data Engineering, Vol.10, No.2, pp. 209-221, 1998   DOI   ScienceOn
26 P.S. Kam and A. Fu, 'Discovering Temporal Patterns for Interval-Based Events,' Proceedings of the 2nd International Conference on Data Warehousing and Knowledge Discovery, (Dawak), Springer Verlag, LNCS, London, UK, 4-6 Sept, 2000
27 T. Abraham and J. F. Roddick, 'Discovering Meta-rules in Mining Temporal and Spatio-temporal data,' Proceedings of the International Database Workshop, Data Mining, Data Warehousing and Client/Server Databases, (IDW'97), pp.30-41, 1997
28 H. Mannila, H. Toivonen, and A. I. Verkamo, 'Discovery of Frequent Episodes in Event Sequences,' Data Mining and Knowledge Discovery, Vol.1, No.3, pp.259-289, 1997   DOI
29 J. Han, G. Dong, and Y. Yin, 'Efficient Mining of Partial Periodic Patterns in Time Series Database,' Proceedings of the 11th International Conference on Data Engineering, 1999   DOI
30 B. Ozden., S. Ramaswamy, and A. Silberschatz, 'Cyclic Association Rules,' Proceedings of the 14th International Conference on Data Engineering, 1998   DOI
31 X. Chen, I. Petrounias, and H. Heathfield, 'Discovering Temporal Association Rules in Temporal Databases,' Proceedings of the International Workshop on Issues and Applications of Database Technology(IADT'98), pp.312-319, 1998
32 J. F. Allen, 'Maintaining Knowledge about Temporal Intervals,' Communication of the Association of Computing Machinery, Vol.26, No.11, 1983   DOI   ScienceOn
33 E. Tsoukatos and D. Gunopoulos, 'Efficient Mining of SpatioTemporal Patterns,' Proceedings of the 7th International Symposium on Spatial and Temporal Databases(SSTD), pp.425-442, 2001