Publications

@inproceedings{Spezzano2018,

title = {A Nature-Inspired, Anytime and Parallel Algorithm for Data Stream Clustering},

author = {G. Spezzano and A. Vinci},

editor = {Sanzio Bassini, Marco Danelutto, Patrizio Dazzi, Gerhard R. Joubert, Frans Peters},

doi = {10.3233/978-1-61499-843-3-317},

issn = {1879808X},

year  = {2018},

date = {2018-01-01},

urldate = {2018-01-01},

booktitle = {Parallel Computing is Everywhere. Preceedings of Parallel Computing Conference (ParCo2017)},

journal = {Advances in Parallel Computing},

volume = {32},

publisher = {IOS Press},

series = {Advances in Parallel Computing},

abstract = {In the context of time-critical applications there exists the need of clustering data streams so as to provide approximated solutions in the shortest possible time, in order to capture in real-time the evolution of physical or social phenomena. In this work, a nature-inspired algorithm for clustering of evolving big data stream is presented, which is designed to be executed on many-core GPU architectures.},

keywords = {Anytime Algorithm, Clustering, CUDA, Data Stream, general purpose GPU computing},

pubstate = {published},

tppubtype = {inproceedings}

}