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SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
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SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
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FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Surfing Wavelets on Streams: One-Pass Summaries for Approximate Aggregate Queries
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IEEE Transactions on Knowledge and Data Engineering
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ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Online Data Mining for Co-Evolving Time Sequences
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VLDB '05 Proceedings of the 31st international conference on Very large data bases
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VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
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VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
XWAVE: optimal and approximate extended wavelets
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
On the marriage of Lp-norms and edit distance
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
A novel bit level time series representation with implication of similarity search and clustering
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Local correlation detection with linearity enhancement in streaming data
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Correlation analysis is a very useful technique for similarity search in the field of data stream mining. The traditional method is not suitable for real time processing especially when the amount of stream sequences is very large. In this paper, we propose HBR (Hierarchical Boolean Representation), a novel technique for correlation analysis in stream time series. The original stream sequences are transformed into the Macro-Boolean series and the Micro-Boolean series successively, and the candidate correlation set can be easily obtained by simple bit operations. With huge amount of stream series, this method can quickly get the correlation pairs of series efficiently by reducing complicated calculation in a little space. Meanwhile, this approach can update the Boolean series incrementally with very low cost and adjust some important coefficients adaptively by the stream feature. The experimental evaluations show that HBR has excellent computation complexity with high accuracy.