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316
Fast subsequence matching in timeseries databases
 PROCEEDINGS OF THE 1994 ACM SIGMOD INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA
, 1994
"... We present an efficient indexing method to locate 1dimensional subsequences within a collection of sequences, such that the subsequences match a given (query) pattern within a specified tolerance. The idea is to map each data sequence into a small set of multidimensional rectangles in feature space ..."
Abstract

Cited by 533 (24 self)
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We present an efficient indexing method to locate 1dimensional subsequences within a collection of sequences, such that the subsequences match a given (query) pattern within a specified tolerance. The idea is to map each data sequence into a small set of multidimensional rectangles in feature
Efficient time series matching by wavelets
 Proc. of 15th Int'l Conf. on Data Engineering
, 1999
"... Time series stored as feature vectors can be indexed by multidimensional index trees like RTrees for fast retrieval. Due to the dimensionality curse problem, transformations are applied to time series to reduce the number of dimensions of the feature vectors. Different transformations like Discrete ..."
Abstract

Cited by 286 (1 self)
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. In this paper, we propose to use Haar Wavelet Transform for time series indexing. The major contributions are: (1) we show that Euclidean distance is preserved in the Haar transformed domain and no false dismissal will occur, (2) we show that Haar transform can outperform DFT through experiments, (3) a new
Efficient TimeSeries Subsequence Matching using Duality in Constructing Windows
 Information Systems
, 2000
"... Subsequence matching in timeseries databases is an important problem in data mining and has attracted a lot of research interest. It is a problem of finding the data sequences containing subsequences similar to a given query sequence and of finding the offsets of these subsequences in the origin ..."
Abstract

Cited by 7 (1 self)
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Subsequence matching in timeseries databases is an important problem in data mining and has attracted a lot of research interest. It is a problem of finding the data sequences containing subsequences similar to a given query sequence and of finding the offsets of these subsequences
A Single Index Approach for DistortionFree TimeSeries Subsequence Matching
"... In this paper we propose a new method for distortionfree timeseries subsequence matching. Our method is distortionfree in the sense that it performs preprocessing on timeseries to remove the distortions of offset translation and amplitude scaling at the same time. We call this preprocessing as ..."
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believe that our single index approach for distortionfree subsequence matching can be widely used in many applications for finding ‘real ’ similar timeseries. 1
Index Interpolation: An Approach to Subsequence Matching Supporting Normalization Transform in TimeSeries Databases
"... In this paper, w epropose a subsequence matching algorithm that supports normalization transform in timeseries databases. Normalization transform enables nding sequences with similar uctuation patterns although they are not close to each other before the normalization transform. Application of the e ..."
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In this paper, w epropose a subsequence matching algorithm that supports normalization transform in timeseries databases. Normalization transform enables nding sequences with similar uctuation patterns although they are not close to each other before the normalization transform. Application
QUANTIZING TIME SERIES FOR EFFICIENT SUBSEQUENCE MATCHING
"... Indexing time series data is an interesting problem that has attracted much interest in the research community for the last decade. Traditional indexing methods organize the data space using different metrics. However, searching highdimensional spaces using a hierarchical index is not always effici ..."
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Cited by 1 (0 self)
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Indexing time series data is an interesting problem that has attracted much interest in the research community for the last decade. Traditional indexing methods organize the data space using different metrics. However, searching highdimensional spaces using a hierarchical index is not always
Approximate embeddingbased subsequence matching of time series
 In SIGMOD ’08: Proceedings of the 2008 ACM SIGMOD international conference on Management of data
, 2008
"... A method for approximate subsequence matching is introduced, that significantly improves the efficiency of subsequence matching in large time series data sets under the dynamic time warping (DTW) distance measure. Our method is called EBSM, shorthand for EmbeddingBased Subsequence Matching. The key ..."
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Cited by 21 (6 self)
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to efficiently identify relatively few areas of interest in the database sequences. Those areas of interest are then fully explored using the exact DTWbased subsequence matching algorithm. Experiments on a large, public time series data set produce speedups of over one order of magnitude compared to brute
SHash: An Indexing Scheme for Approximate Subsequence Matching in Large Sequence Databases
, 1997
"... Large sequence databases are becoming increasingly common. They range from protein and gene sequences in biology, to time series data in soil sciences, to MIDI sequences in multimedia applications, to text documents in information retrieval. An important operation on a sequence database is approxima ..."
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Cited by 2 (2 self)
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is approximate subsequence matching, where all subsequences that are within some distance from a given query string are retrieved. This paper introduces SHash, a scheme that enables efficient approximate subsequence matching in large sequence databases. SHash employs a hash function to condense each data
Efficient Time Series Matching by Wavelets
"... Time series stored as feature vectors can be indexed by multidimensional index trees like RTrees for fast retrieval. Due to the dimensionality curse problem, transformations are applied to time series to reduce the number of dimensions of the feature vectors. Different transformations like Discrete ..."
Abstract
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. In this paper, we propose to use Haar Wavelet Transform for time series indexing. The major contributions are: (1) we show that Euclidean distance is preserved in the Haar transformed domain and no false dismissal will occur, (2) we show that Haar transform can outperform DFT through experiments, (3) a new
Generic Subsequence Matching Framework: Modularity, Flexibility, Efficiency
"... Abstract. Subsequence matching has appeared to be an ideal approach for solving many problems related to the fields of data mining and similarity retrieval. It has been shown that almost any data class (audio, image, biometrics, signals) is or can be represented by some kind of time series or strin ..."
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Abstract. Subsequence matching has appeared to be an ideal approach for solving many problems related to the fields of data mining and similarity retrieval. It has been shown that almost any data class (audio, image, biometrics, signals) is or can be represented by some kind of time series
Results 1  10
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316