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Quality-driven resource-adaptive data stream mining?

ARAN - Access to Research at NUI Galway

Show simple item record Karnstedt, Marcel 2011-10-18T15:58:07Z 2011-10-18T15:58:07Z 2011-01
dc.identifier.citation Junghans, C., Karnstedt, M., & Gertz, M. Quality-driven resource-adaptive data stream mining? SIGKDD Explor. Newsl., 13(1), 72-82. en_US
dc.identifier.issn 1931-0145
dc.description.abstract Data streams have become ubiquitous in recent years and are handled on a variety of platforms, ranging from dedicated high-end servers to battery-powered mobile sensors. Data stream processing is therefore required to work under virtually any dynamic resource constraints. Few approaches exist for stream mining algorithms that are capable to adapt to given constraints, and none of them reflects from the resource adaptation to the resulting output quality. In this paper, we propose a general model to achieve resource and quality awareness for stream mining algorithms in dynamic setups. The general applicability is granted by classifying influencing parameters and quality measures as components of a multiobjective optimization problem. By the use of CluStream as an example algorithm, we demonstrate the practicability of the proposed model. en_US
dc.format application/pdf en_US
dc.language.iso English en_US
dc.publisher IEEE / ACM en_US
dc.subject Data stream processing en_US
dc.subject Data mining en_US
dc.subject Digital Enterprise Research Institute (DERI) en_US
dc.title Quality-driven resource-adaptive data stream mining? en_US
dc.type Article
dc.local.publishedsource en_US
dc.description.peer-reviewed peer-reviewed en_US

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