Mining, indexing, and querying historical spatiotemporal data

KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2004, Pages 236-245

  Mamoulis, Nikos ^a   Hadjieleftheriou, Marios ^b   Cao, Huiping ^a   Tao, Yufei ^c   Kollios, George ^d   Cheung, David W ^a  

^a UNIVERSITY OF HONG KONG   (Hong Kong)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c CITY UNIVERSITY OF HONG KONG   (Hong Kong)

^d BOSTON UNIVERSITY   (United States)

Author keywords

Indexing; Pattern mining; Spatiotemporal data; Trajectories

Indexed keywords

ALGORITHMS; DATA COMPRESSION; DATA REDUCTION; INDEXING (OF INFORMATION); OPTIMIZATION; PROBLEM SOLVING; QUERY LANGUAGES; TREES (MATHEMATICS);

DATASETS; OBJECT TRAJECTORIES; PATTERN MINING; SPATIOTEMPORAL DATA;

DATA MINING;

EID: 12244264433     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1014052.1014080     Document Type: Conference Paper

References (16)

1. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proc. of Very Large Data Bases, pages 487-499, 1994.
2. M. Ester, H. P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proc. of ACM Knowledge Discovery and Data Mining, pages 226-231, 1996.
3. A. Guttman. R-trees: A dynamic index structure for spatial searching. In Proc. of ACM Management of Data, pages 47-57, 1984.
4. M. Hadjieleftheriou, G. Kollios, V. J. Tsotras, and D. Gunopulos. Efficient indexing of spatiotemporal objects. In Proc. of Extending Database Technology, pages 251-268, 2002.
5. J. Han, G. Dong, and Y. Yin. Efficient mining of partial periodic patterns in time series database. In Proc. of International Conference on Data Engineering, pages 106-115, 1999.
6. P. Indyk, N. Koudas, and S. Muthukrishnan. Identifying representative trends in massive time series data sets using sketches. In Proc. of Very Large Data Bases, pages 363-372, 2000.
7. S. Ma and J. L. Hellerstein. Mining partially periodic event patterns with unknown periods. In Proc. of International Conference on Data Engineering, pages 205-214, 2001.
8. B. Özden, S. Ramaswamy, and A. Silberschatz. Cyclic association rules. In Proc. of International Conference on Data Engineering, pages 94-101, 1998.
9. W.-C. Peng and M.-S. Chen. Developing data allocation schemes by incremental mining of user moving patterns in a mobile computing system. IEEE Transactions on Knowledge and Data Engineering, 15(1):70-85, 2003.
10. D. Pfoser, C. S. Jensen, and Y. Theodoridis. Novel approaches in query processing for moving object trajectories. In The VLDB Journal, pages 395-406, 2000.
11. B. Salzberg and V. J. Tsotras. Comparison of access methods for time-evolving data. ACM Computing Surveys, 31(2):158-221, 1999.
12. Y. Tao, G. Kollios, J. Considine, F. Li, and D. Papadias. Spatio-temporal aggregation using sketches. In Proc. of International Conference on Data Engineering, pages 449-460, 2004.
13. Y. Tao and D. Papadias. MV3R-tree: A spatio-temporal access method for timestamp and interval queries. In Proc. of Very Large Data Bases, pages 431-440, 2001.
14. I. Tsoukatos and D. Gunopulos. Efficient mining of spatiotemporal patterns. In Proc. of Symposium on Advances in Spatial and Temporal Databases, pages 425-442, 2001.
15. J. Yang, W. Wang, and P. S. Yu. Mining asynchronous periodic patterns in time series data. In KDD, 2000.
16. M. J. Zaki. SPADE: An efficient algorithm for mining frequent sequences. Machine Learning, 42(1/2):31-60, 2001.