Distributed classification of data streams: An adaptive technique

Alfredo Cuzzocrea*, Mohamed Medhat Gaber, Ary Mazharuddin Shiddiqi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationBig Data Analytics and Knowledge Discovery - 17th International Conference, DaWaK 2015, Proceedings
EditorsTakahiro Hara, Sanjay Madria
PublisherSpringer Verlag
Pages296-309
Number of pages14
ISBN (Print)9783319227283
DOIs
Publication statusPublished - 2015
Event17th International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2015 - Valencia, Spain
Duration: 1 Sept 20154 Sept 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9263
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Big Data Analytics and Knowledge Discovery, DaWaK 2015
Country/TerritorySpain
CityValencia
Period1/09/154/09/15

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