Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
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
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Continually evaluating similarity-based pattern queries on a streaming time series
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
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
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Haar Wavelets for Efficient Similarity Search of Time-Series: With and Without Time Warping
IEEE Transactions on Knowledge and Data Engineering
WALRUS: A Similarity Retrieval Algorithm for Image Databases
IEEE Transactions on Knowledge and Data Engineering
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
A parallel dimensionality reduction for time-series data and some of its applications
International Journal of Intelligent Information and Database Systems
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In this paper we propose a formal approach that transforms a high-dimensional MBR itself to a low-dimensional MBR directly, and show that the approach significantly reduces the number of lower-dimensional transformations in similar sequence matching. To achieve this goal, we first formally define a new notion of MBR-safe. We say that a transform is MBR-safe if it constructs a low-dimensional MBR by containing all the low-dimensional sequences to which an infinite number of high-dimensional sequences in an MBR are transformed. We then propose an MBR-safe transform based on DFT. For this, we prove the original DFT-based lower-dimensional transformation is not MBR-safe and define a new transform, called mbrDFT, by extending definition of DFT. We also formally prove this mbrDFT is MBR-safe. Analytical and experimental results show that our mbrDFT reduces the number of lower-dimensional transformations drastically and improves performance significantly compared with the traditional method.