E. J. Keogh, and M. J. Pazzani: "An Indexing Scheme for Fast Similarity Search in Large Time Series Databases", Proc. SSDBM, Clevelant, Ohio, 1999.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....is high. 1 Introduction Spatio temporal data mining [14, 16, 15, 17, 13, 7] is important in many application domains such as epidemiology, ecology, climatology, or census statistics, where datasets which are spatio temporal in nature are routinely collected. The development of e#cient tools [1, 4, 8, 10, 11] to explore these datasets, the focus of this work, is crucial to organizations which make decisions based on large spatio temporal datasets. A spatial framework [19] consists of a collection of locations and a neighbor relationship. A time series is a sequence of observations taken sequentially ....

E. Keogh and M. Pazzani. An Indexing Scheme for Fast Similarity Search in Large Time Series Databases. In Proc. of 11th Int'l Conference on Scientific and Statistical Database Management, 1999.

....is high. 1 Introduction Spatio temporal data mining [14, 16, 15, 17, 13, 7] is important in many application domains such as epidemiology, ecology, climatology, or census statistics, where datasets which are spatio temporal in nature are routinely collected. The development of ecient tools [1, 4, 8, 10, 11] to explore these datasets, the focus of this work, is crucial to organizations which make decisions based on large spatio temporal datasets. A spatial framework [19] consists of a collection of locations and a neighbor relationship. A time series is a sequence of observations taken sequentially ....

E. Keogh and M. Pazzani. An Indexing Scheme for Fast Similarity Search in Large Time Series Databases. In Proc. of 11th Int'l Conference on Scienti c and Statistical Database Management, 1999.

....datasets [12, 21, 22, 24, 25, 26, 29] collected by satellites, sensor nets, retailers, mobile device servers, and medical instruments on a daily basis is important for many application domains such as epidemiology, ecology, climatology, and census statistics. The development of ecient tools [3, 7, 13, 16, 18, 14] to explore these datasets, the focus of this work, is crucial to organizations which make decisions based on large spatio temporal datasets. A spatial framework [30] consists of a collection of locations and a neighbor relationship. A time series is a sequence of observations taken sequentially ....

E. Keogh and M. Pazzani. An Indexing Scheme for Fast Similarity Search in Large Time Series Databases. In Proc. of 11th Int'l Conference on Scienti c and Statistical Database Management, 1999.

....introduced in [16] where every line segment in the approximation is augmented with a weight representing its relative importance; for instance, a combined sequence may be constructed representing a class of sequences, and some line segments may be more representative of the class than others. In [17] Keogh and Pazzani introduce an indexing method based on hashing, in addition to the piecewise linear approximation. A equispaced template grid window is moved across the sequence, and for each position a hash key is generated to decide which bin the corresponding subsequence is put. The hash key ....

Eamonn J. Keogh and Michael J. Pazzani. An indexing scheme for fast similarity search in large time series databases. In Statistical and Scientific Database Management, pages 56--67, 1999.

....and very problemdependent. A second general problem is that DTW focuses only on one speci c type of pattern variability, namely elasticity in time, whereas in practice other deformations may also be present. There has bee substantial interest in this problem in the data mining literature (e.g. [1, 7, 2, 17, 20, 5, 10, 13]) Much of this work can be characterized procedurally in the following general manner: 1) nd an approximate and robust representation for the time series (e.g. Fourier coecients, piecewise linear models, etc. 2) de ne a exible matching function which can handle various pattern deformations ....

E. J. Keogh and M. J. Pazzani. An indexing scheme for fast similarity search in large time series databases. In Proc. Eleventh International Conference on Scientic and Statistical Database Management, pages 56-67, Jul 1999.

....time series. The behavior of the processes recorded in time series can be revealed by investigating the characteristic features of the time series. A considerable research effort has been directed recently to the development of methods for matching characteristic patterns in time series databases [2, 7, 1, 8, 5, 10]. The methods vary in the representation techniques for time series, the algorithms for measuring similarity between the time series, and in the search mechanisms used for mining the patterns. The problem which is related to matching subsequences in time series databases is the discovery of common ....

....of the time series, the comparison of time series, and in the search mechanisms. The representations of the time series used in the methods are: the normalized series [2] the representation based on the feature space obtained by Discrete Fourier Transformation [7] the piecewise linear models [8, 10], and the piecewise linear models augmented with weights [9] Our algorithm represents time series as normalized detrended sequences of real values. The methods for the identification of similar time series are as follows. First, a simple distance function (difference) is used for the comparison ....

[Article contains additional citation context not shown here]

Keogh, E.J., Pazzani, M.J, `An Indexing Scheme for Fast Similarity Search in Large Time Series Databases', In Proc. of Conf. on Scientific and Statistical Database Management, 1999.

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Keogh, E., & Pazzani, M. (1999). An indexing scheme for fast similarity search in large time series databases. In Proc. of the 11 International Conference on Scientific and Statistical Database Management.

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E. J. Keogh, and M. J. Pazzani: "An Indexing Scheme for Fast Similarity Search in Large Time Series Databases", Proc. SSDBM, Clevelant, Ohio, 1999.

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Keogh, E., and Pazzani, M., "An Indexing Scheme for Fast Similarity Search in Large Time Series Databases," Proceedings of 11th Int'l Conference on Scientific and Statistical Database Management, 1999.

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Keogh, E., and Pazzani, M., "An Indexing Scheme for Fast Similarity Search in Large Time Series Databases," Proceedings of 11th Int'l Conference on Scientific and Statistical Database Management, 1999.

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Keogh, E., and Pazzani, M., "An Indexing Scheme for Fast Similarity Search in Large Time Series Databases," Proceedings of 11th Int'l Conference on Scientific and Statistical Database Management, 1999.

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E. J. Keogh, and M. J. Pazzani: "An Indexing Scheme for Fast Similarity Search in Large Time Series Databases", Proc. SSDBM, Clevelant, Ohio, 1999.

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E. J. Keogh and M. Pazzani, An indexing scheme for fast similarity search in large time series databases, 11th International Conference on Scientific and Statistical Database Management, SSDBM'99 (Cleveland, OH), IEEE Computer Society, 1999, pp. 56--67.

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Keogh, E. J. and Pazzani, M. J. (1999a). An indexing scheme for fast similarity search in large time series databases. In Proc. of the 11th Int. Conf. on Scientific and Statistical Database Management, Cleveland, Ohio.

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E.J. Keogh and M.J. Pazzani. An indexing scheme for fast similarity search in large time series databases. In Proc. Eleventh International Conference on Scientific and Statistical Database Management, pages 56--67, 1999.

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