Mining data streams is a critical task of actual Big Data applications. Usually, data stream mining algorithms work on resource-constrained environments, which call for novel requirements like availability of resources and adaptivity. Following this main trend, in this paper we propose a distributed data stream classification technique that has been tested on a real sensor network platform, namely, Sun SPOT. The proposed technique shows several points of research innovation, with are also confirmed by its effectiveness and efficiency assessed in our experimental campaign.

Distributed classification of data streams: An adaptive technique

Cuzzocrea Alfredo;
2015

Abstract

Mining data streams is a critical task of actual Big Data applications. Usually, data stream mining algorithms work on resource-constrained environments, which call for novel requirements like availability of resources and adaptivity. Following this main trend, in this paper we propose a distributed data stream classification technique that has been tested on a real sensor network platform, namely, Sun SPOT. The proposed technique shows several points of research innovation, with are also confirmed by its effectiveness and efficiency assessed in our experimental campaign.
9783319227283
Computer Science (all)
Theoretical Computer Science
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/312881
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