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Event-based lossy compression for effective and efficient OLAP over data streams by Alfredo Cuzzocrea; Sharma Chakravarthy is a Computer Science article available to read on EtoBox.

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Data stream query processing Data stream compression methodologies and techniques Knowledge discovery from data streams OLAP over data streams Event-based data stream processing Event-based data stream compression a b s t r a c t An innovative event-based lossy compression model for effective and efficient OLAP over data streams, called ECM-DS, is presented and experimentally assessed in this paper. The main novelty of our compression approach with respect to traditional data stream compression techniques relies on exploiting the semantics of the reference application scenario in order to drive the compression process by means of the ''degree of interestingness" of events occurring in the target stream. This finally improves the quality of retrieved approximate answers to OLAP queries over data streams, and, in turn, the quality of complex knowledge discovery tasks over data streams developed on top of ECM-DS, and implemented via ad-hoc data stream mining algorithms. Overall, the compression strategy we propose in this research puts the basis for a novel class of intelligent applications over data streams where the knowledge on actual streams is integrated-with and correlated-to th

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Author

Alfredo Cuzzocrea; Sharma Chakravarthy

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0169-023X)

Published

2010

Language

EN

Field

Computer Science (Physical Sciences)