Continually evaluating similarity-based pattern queries on a streaming time series

Gao, Like; Wang, X. Sean

doi:10.1145/564691.564734

Cited by 66 publications

(7 citation statements)

References 37 publications

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“…For stream correlation analysis, Papadimitriou et al [15] started from the auto-covariance matrices of single data stream, and analyzed the local correlation of two data stream by comparing the eigenvector matrices. Gao et al [4] addressed the problem of monitoring the streams to discover relevant events/patterns. It applied FFT to eff ciently f nd the cross correlations of time series, and f nd the NN of time series at many time positions.…”

Section: Related Workmentioning

confidence: 99%

Local correlation detection with linearity enhancement in streaming data

Xie

Shang

Yuan

et al. 2013

Proceedings of the 22nd ACM International Conference on Information &Amp; Knowledge Management

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This paper addresses the challenges in detecting the potential correlation between numerical data streams, which facilitates the research of data stream mining and pattern discovery. We focus on local correlation with delay, which may occur in burst at different time in different streams, and last for a limited period. The uncertainty on the correlation occurrence and the time delay make it diff cult to monitor the correlation online. Furthermore, the conventional correlation measure lacks the ability of ref ecting visual linearity, which is more desirable in reality. This paper proposes effective methods to continuously detect the correlation between data streams. Our approach is based on the Discrete Fourier Transform to make rapid cross-correlation calculation with time delay allowed. In addition, we introduce a shape-based similarity measure into the framework, which ref nes the results by representative trend patterns to enhance the signif cance of linearity. The similarity of proposed linear representations can quickly estimate the correlation, and the window sliding strategy in segment level improves the eff ciency for online detection. The empirical study demonstrates the accuracy of our detection approach, as well as more than 30% improvement of eff ciency.

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Section: Related Workmentioning

confidence: 99%

Local correlation detection with linearity enhancement in streaming data

Xie

Shang

Yuan

et al. 2013

Proceedings of the 22nd ACM International Conference on Information &Amp; Knowledge Management

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“…Similarity queries in streaming time series have been studied in (Gao 2002) where whole-match queries are investigated by using the Euclidean distance as the similarity measure. A prediction-based approach is used for query processing.…”

Section: S I M I L a R I T Y S E A R C H I N T I M E S E R I E Smentioning

confidence: 99%

“…Therefore, when a new value for a time series arrives, the value must be used to determine which queries are satisfied. Continuous queries over data streams are studied in (Chandrasekaran 2002, Gao 2002, Gao 2002b, Gao 2002c, Babcock 2002.…”

Section: F U T U R E T R E N D Smentioning

confidence: 99%

Similarity Search in Time Series Databases

Kontaki

Papadopoulos

Manolopoulos

2005

Encyclopedia of Database Technologies and Applications

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“…In our prior research [9], we considered the similar continuous nearest and near neighbor queries with the assumption that the number of pattern series is small enough so that all the pattern series can fit in the memory. We proposed a prediction-based batch processing method which yields su-perior algorithms compared with a direct in-memory scan method.…”

Section: Related Workmentioning

confidence: 99%

Evaluating continuous nearest neighbor queries for streaming time series via pre-fetching

Gao¹,

Yao²,

Wang³

2002

Proceedings of the Eleventh International Conference on Information and Knowledge Management - CIKM '02

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For many applications, it is important to quickly locate the nearest neighbor of a given time series. When the given time series is a streaming one, nearest neighbors may need to be found continuously at all time positions. Such a standing request is called a continuous nearest neighbor query. This paper seeks fast evaluation of continuous queries on large databases. The initial strategy is to use the result of one evaluation to restrict the search space for the next. A more fundamental idea is to extend the existing indexing methods, used in many traditional nearest neighbor algorithms, with pre-fetching. Specifically, pre-fetching is to predict the next value of the stream before it arrives, and to process the query as if the predicted value were the real one in order to load the needed index pages and time series into the allocated cache memory. Furthermore, if the pre-fetched candidates cannot fit into the cache memory, they are stored in a sequential file to facilitate fast access to them. Experiments show that prefetching improves the response time greatly over the direct use of traditional algorithms, even if the caching provided by the operating system is taken into consideration.

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Continually evaluating similarity-based pattern queries on a streaming time series

Cited by 66 publications

References 37 publications

Local correlation detection with linearity enhancement in streaming data

Local correlation detection with linearity enhancement in streaming data

Similarity Search in Time Series Databases

Evaluating continuous nearest neighbor queries for streaming time series via pre-fetching

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