ARAN - Access to Research at NUI Galway

Quality-driven resource-adaptive data stream mining?

ARAN - Access to Research at NUI Galway

Show simple item record

dc.contributor.author Karnstedt, Marcel
dc.date.accessioned 2011-10-18T15:58:07Z
dc.date.available 2011-10-18T15:58:07Z
dc.date.issued 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.identifier.uri http://hdl.handle.net/10379/2234
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 http://dx.doi.org/10.1145/2031331.2031342 en_US
dc.description.peer-reviewed peer-reviewed en_US

Files in this item

This item appears in the following Collection(s)

Show simple item record