Efficiently supporting ad hoc queries in large datasets of time sequences
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
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Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
On Similarity Queries for Time-Series Data: Constraint Specification and Implementation
CP '95 Proceedings of the First International Conference on Principles and Practice of Constraint Programming
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Clustering time series from ARMA models with clipped data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Multiresolution Symbolic Representation of Time Series
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Robust and fast similarity search for moving object trajectories
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
On the marriage of Lp-norms and edit distance
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
On nonmetric similarity search problems in complex domains
ACM Computing Surveys (CSUR)
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Similarity query is a frequent subroutine in time series database to find the similar time series of the given one. In this process, similarity measure plays a very important part. The previous methods do not consider the relation between point correspondences and the importance (role) of the points on the content of time series during measuring similarity, resulting in their low accuracies in many real applications. In the paper, we propose a General Hierarchical Model (GHM), which determines the point correspondences by the hierarchies of points. It partitions the points of time series into different hierarchies, and then the points are restricted to be compared with the ones in the same hierarchy. The practical methods can be implemented based on the model with any real requirements, e.g. FFT Hierarchical Measures (FHM) given in this paper. And the hierarchical filtering methods of GHM are provided for range and k-NN queries respectively. Finally, two common data sets were used in k-NN query and clustering experiments to test the effectiveness of our approach and others. The time performance comparisons of all the tested methods were performed using the synthetic data set with various sizes. The experimental results show the superiority of our approach over the competitors. And we also give the experimental powers of the filtering methods proposed in the queries.