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SIGMOD '94 Proceedings of the 1994 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
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Proceedings of the 2002 ACM SIGMOD international conference on Management of data
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
OP-Cluster: Clustering by Tendency in High Dimensional Space
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
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CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Exact indexing of dynamic time warping
Knowledge and Information Systems
BRAID: stream mining through group lag correlations
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
QEST '05 Proceedings of the Second International Conference on the Quantitative Evaluation of Systems
Continuous query processing in data streams using duality of data and queries
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Local Correlation Tracking in Time Series
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Information Sciences: an International Journal
Maximal Subspace Coregulated Gene Clustering
IEEE Transactions on Knowledge and Data Engineering
Adaptive correlation analysis in stream time series with sliding windows
Computers & Mathematics with Applications
Efficient Online Subsequence Searching in Data Streams under Dynamic Time Warping Distance
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Fast approximate correlation for massive time-series data
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
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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 difficult to monitor the correlation online. Furthermore, the conventional correlation measure lacks the ability of reflecting 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 refines the results by representative trend patterns to enhance the significance of linearity. The similarity of proposed linear representations can quickly estimate the correlation, and the window sliding strategy in segment level improves the efficiency for online detection. The empirical study demonstrates the accuracy of our detection approach, as well as more than $30\%$ improvement of efficiency.