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EnglishMining 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.
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| Titolo: | Distributed classification of data streams: An adaptive technique |
| Autori: | |
| Data di pubblicazione: | 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. |
| Handle: | http://hdl.handle.net/20.500.11770/312881 |
| ISBN: | 9783319227283 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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http://hdl.handle.net/20.500.11770/312881
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