Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Efficiently supporting ad hoc queries in large datasets of time sequences
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Processing complex aggregate queries over data streams
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Efficient Similarity Search In Sequence Databases
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
Proceedings of the 27th International Conference on Very Large Data Bases
Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Online Data Mining for Co-Evolving Time Sequences
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Correlating synchronous and asynchronous data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A dimensionality reduction technique for efficient similarity analysis of time series databases
Proceedings of the thirteenth ACM international conference on Information and knowledge management
BRAID: stream mining through group lag correlations
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Integrating DCT and DWT for approximating cube streams
Proceedings of the 14th ACM international conference on Information and knowledge management
Optimal multi-scale patterns in time series streams
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A framework for clustering evolving data streams
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
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
Event Correlations in Sensor Networks
ICCS 2009 Proceedings of the 9th International Conference on Computational Science
A review on time series data mining
Engineering Applications of Artificial Intelligence
Mining named entities with temporally correlated bursts from multilingual web news streams
Proceedings of the fourth ACM international conference on Web search and data mining
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Correlation analysis is a basic problem in the field of data stream mining. Typical approaches add sliding window to data streams to get the recent results, but the window length defined by users is always fixed which is not suitable for the changing stream environment. We propose a Boolean representation based data-adaptive method for correlation analysis among a large number of time series streams. The periodical trends of each stream series to are monitored to choose the most suitable window size and group the series with the same trends together. Instead of adopting complex pair-wise calculation, we can also quickly get the correlation pairs of series at the optimal window sizes. All the processing is realized by simple Boolean operations. Both the theory analysis and the experimental evaluations show that our method has good computation efficiency with high accuracy.