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dc.contributor.authorKarnstedt, Marcel
dc.identifier.citationJunghans, C., Karnstedt, M., & Gertz, M. Quality-driven resource-adaptive data stream mining? SIGKDD Explor. Newsl., 13(1), 72-82.en_US
dc.description.abstractData 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.publisherIEEE / ACMen_US
dc.subjectData stream processingen_US
dc.subjectData miningen_US
dc.subjectDigital Enterprise Research Institute (DERI)en_US
dc.titleQuality-driven resource-adaptive data stream mining?en_US

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