A new approach for processing ranked subsequence matching based on ranked union

Proceedings of the ACM SIGMOD International Conference on Management of Data

Volumn , Issue , 2011, Pages 457-468

  Han, Wook Shin ^a   Lee, Jinsoo ^a   Moon, Yang Sae ^b   Hwang, Seung Won ^c   Yu, Hwanjo ^c  

^a Kyungpook National University   (South Korea)

^b Kangwon National University   (South Korea)

^c Pohang University of Science and Technology (POSTECH)   (South Korea)

Author keywords

ranked subsequence matching; ranked union; time series data

Indexed keywords

PROBLEM SOLVING;

LOW BOUND; MINIMUMS DISTANCE; NEW APPROACHES; PRIORITY QUEUES; QUERY SEQUENCE; RANKED SUBSEQUENCE MATCHING; RANKED UNION; SUBSEQUENCE MATCHING; TIME-SERIES DATA;

EQUIVALENCE CLASSES;

EID: 79959918495     PISSN: 07308078     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1989323.1989371     Document Type: Conference Paper

References (24)

1. I. Assent, M. Wichterich, R. Krieger, H. Kremer, and T. Seidl. Anticipatory dtw for efficient similarity search in time series databases. PVLDB, 2(1):826-837, 2009.
2. V. Athitsos, P. Papapetrou, M. Potamias, G. Kollios, and D. Gunopulos. Approximate embedding-based subsequence matching of time series. In SIGMOD Conference, pages 365-378, 2008.
3. I. Bartolini and M. Patella. Warp: Accurate retrieval of shapes using phase of fourier descriptors and time warping distance. IEEE PAMI., 27(1):142-147, 2005. (Pubitemid 40195905)
4. D. J. Berndt and J. Clifford. Finding patterns in time series: a dynamic programming approach. pages 229-248, 1996.
5. N. Bruno, L. Gravano, and A. Marian. Evaluating top-k queries over web-accessible databases. In ICDE 2002, 2002.
6. R. Fagin, A. Lote, and M. Naor. Optimal aggregation algorithms for middleware. In PODS, 2001.
7. C. Faloutsos, M. Ranganathan, and Y. Manolopoulos. Fast subsequence matching in time-series databases. In SIGMOD, 1994.
8. G. Graefe. Query evaluation techniques for large databases. ACM Comput. Surv., 25(2):73-170, 1993. (Pubitemid 23712749)
9. U. Güntzer, W.-T. Balke, and W. Kießling. Optimizing multi-feature queries in image databases. In VLDB, 2000.
10. U. Güntzer, W.-T. Balke, and W. Kießling. Towards efficient multi-feature queries in heterogeneous environments. In ITCC, pages 622-628, 2001.
11. W. Han, J. Lee, Y. Moon, S. Hwang, and H. Yu. A new approach for processing ranked subsequence matching based on ranked union. http://www-db.knu.ac.kr/rankedunion/runion-tr.pdf, 2011.
12. W. Han, J. Lee, Y. Moon, and H. Jiang. Ranked subsequence matching in time-series database. In VLDB, 2007.
13. E. Keogh. Exact indexing of dynamic time warping. In VLDB, 2002.
14. E. Keogh. A decade of progress in indexing and mining large time series databases. In VLDB, 2006.
15. C. Li, K. C.-C. Chang, I. F. Ilyas, and S. Song. Ranksql: Query algebra and optimization for relational top-k queries. In SIGMOD Conference, pages 131-142, 2005.
16. Y. Moon, K. Whang, and W. Han. General match: a subsequence matching method in time-series databases based on generalized windows. In SIGMOD, 2002.
17. Y.-S. Moon, K.-Y.Whang, and W.-K. Loh. Duality-based subsequence matching in time-series databases. In ICDE, 2001.
18. L. Rabiner and B.-H. Juang. Fundamentals of speech recognition. Prentice-Hall, Inc., 1993.
19. D. Rafiei and A. O. Mendelzon. Querying time series data based on similarity. TKDE, 12(5):675-693, 2000.
20. S. won Hwang and K. C.-C. Chang. Optimizing top-k queries for middleware access: A unified cost-based approach. ACM Trans. Database Syst., 32(1):5, 2007.
21. T. S. F. Wong and M. H. Wong. Efficient subsequence matching for sequences databases under time warping. IDEAS, 2003.
22. D. Xin, J. Han, and K. C.-C. Chang. Progressive and selective merge: computing top-k with ad-hoc ranking functions. In SIGMOD, 2007.
23. B.-K. Yi and C. Faloutsos. Fast time sequence indexing for arbitrary lp norms. In VLDB, 2000.
24. Y. Zhu and D. Shasha. Warping indexes with envelope transforms for query by humming. In SIGMOD, 2003.