StreaMon: an adaptive engine for stream query processing
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Near-optimal algorithms for shared filter evaluation in data stream systems
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Categorizing and mining concept drifting data streams
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Data Streams with Labeled and Unlabeled Training Examples
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
SKIF: a data imputation framework for concept drifting data streams
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Robust ensemble learning for mining noisy data streams
Decision Support Systems
Active learning from stream data using optimal weight classifier ensemble
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Classifier and Cluster Ensembles for Mining Concept Drifting Data Streams
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Enabling fast prediction for ensemble models on data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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In multimedia stream matching applications, user experience is becoming more and more important. In this paper, we study the problem of predictive stream matching, where a large number of queries are evaluated before the actual stream data arrive. This is equivalent to developing predictive algorithms for the filters registered on data streams. To this end, We propose to efficiently order the pipeline filters in multimedia streams under the Marko assumption. Our experimental evaluations validate that our predictive data stream filtering framework is able to provide an efficient solution for evaluating large number of queries on streams.