The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Fast time-series searching with scaling and shifting
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
ADC '01 Proceedings of the 12th Australasian database conference
General match: a subsequence matching method in time-series databases based on generalized windows
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Querying Time Series Data Based on Similarity
IEEE Transactions on Knowledge and Data Engineering
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Efficient Retrieval of Similar Time Sequences Under Time Warping
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Duality-Based Subsequence Matching in Time-Series Databases
Proceedings of the 17th International Conference on Data Engineering
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Haar Wavelets for Efficient Similarity Search of Time-Series: With and Without Time Warping
IEEE Transactions on Knowledge and Data Engineering
On Similarity-Based Queries for Time Series Data
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Similarity search of time-warped subsequences via a suffix tree
Information Systems
A Subsequence Matching Algorithm that Supports Normalization Transform in Time-Series Databases
Data Mining and Knowledge Discovery
Information Systems - Databases: Creation, management and utilization
Online event-driven subsequence matching over financial data streams
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Atomic Wedgie: Efficient Query Filtering for Streaming Times Series
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
A decade of progress in indexing and mining large time series databases
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Using multiple indexes for efficient subsequence matching in time-series databases
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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Normalization transform is known to be very useful for finding the overall trend of time-series data since it enables finding sequences with similar fluctuation patterns. Previous subsequence matching methods with normalization transform, however, would incur index overhead both in storage space and in update maintenance since they should build multiple indexes for supporting query sequences of arbitrary length. To solve this problem, we adopt a single-index approach in the normalization-transformed subsequence matching that supports query sequences of arbitrary length. For the single-index approach, we first provide the notion of inclusion-normalization transform by generalizing the original definition of normalization transform. To normalize a window, the inclusion-normalization transform uses the mean and the standard deviation of a subsequence that includes the window while the original transform uses those of the window itself. Next, we formally prove the correctness of the proposed normalization-transformed subsequence matching method that uses the inclusion-normalization transform. We then propose subsequence matching and index-building algorithms to implement the proposed method. Experimental results for real stock data show that our method improves performance by up to 2.5 ~ 2.8 times compared with the previous method.