Generalized Dimension-Reduction Framework for Recent-Biased Time Series Analysis
IEEE Transactions on Knowledge and Data Engineering
Adaptive similarity search in streaming time series with sliding windows
Data & Knowledge Engineering
Efficient Similarity Search over Future Stream Time Series
IEEE Transactions on Knowledge and Data Engineering
Evaluation of similarity searching methods for music data in P2P networks
International Journal of Business Intelligence and Data Mining
Analysis of time-dependent query trends in P2P file sharing systems
APNOMS'09 Proceedings of the 12th Asia-Pacific network operations and management conference on Management enabling the future internet for changing business and new computing services
Resource adaptive periodicity estimation of streaming data
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Similarity search in streaming time series based on MP_C dimensionality reduction method
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I
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Query processing in data streams is a very importantresearch direction.The challenge in a databaseof data streams is to provide efficient algorithms andaccess methods for query processing, taking into considerationthe fact that the database changes continuously as new data arrive.Traditional access methodsthat continuously update the data costs.In thispaper we present IDC-Index, an efficient techniquefor similarity query processing in streaming time sequences,which is based on a multidimensional accessmethod enhanced with a deferred update policy andan incremental computation of the Discrete FourierTransform (DFT), which is used as a feature extractionmethod.The method manages to reduce the numberof false alarms examined and therefore achieveshigh answers/candidates ratio.Moreover, an extensiveperformance evaluation based on synthetic randomwalk and real time sequences have shown that theproposed technique outperforms significantly existingapproaches for similarity range query processing.