DocumentCode
251876
Title
NEMICO: Mining Network Data through Cloud-Based Data Mining Techniques
Author
Baralis, Elena ; Cagliero, Luca ; Cerquitelli, Tania ; Chiusano, Silvia ; Garza, Paolo ; Grimaudo, Luigi ; Pulvirenti, Fabio
Author_Institution
Dipt. di Autom. e Inf., Politec. di Torino, Turin, Italy
fYear
2014
fDate
8-11 Dec. 2014
Firstpage
503
Lastpage
504
Abstract
Thanks to the rapid advances in Internet-based applications, data acquisition and storage technologies, petabyte-sized network data collections are becoming more and more common, thus prompting the need for scalable data analysis solutions. By leveraging today´s ubiquitous many-core computer architectures and the increasingly popular cloud computing paradigm, the applicability of data mining algorithms to these large volumes of network data can be scaled up to gain interesting insights. This paper proposes NEMICO, a comprehensive Big Data mining system targeted to network traffic flow analyses (e.g., Traffic flow characterization, anomaly detection, multiple-level pattern mining). NEMICO comprises new approaches that contribute to a paradigm-shift in distributed data mining by addressing most challenging issues related to Big Data, such as data sparsity, horizontal scaling, and parallel computation.
Keywords
Big Data; cloud computing; data acquisition; data analysis; data mining; multiprocessing systems; storage management; telecommunication traffic; ubiquitous computing; Big Data mining system; Internet-based applications; NEMICO; cloud computing paradigm; cloud-based data mining techniques; data acquisition; data analysis solutions; distributed data mining; network data mining; network mining in the cloud; network traffic flow analysis; petabyte-sized network data collections; storage technologies; ubiquitous many-core computer architectures; Algorithm design and analysis; Association rules; Cloud computing; Clustering algorithms; Computer architecture; Taxonomy; Cloud Computing; Data mining;
fLanguage
English
Publisher
ieee
Conference_Titel
Utility and Cloud Computing (UCC), 2014 IEEE/ACM 7th International Conference on
Conference_Location
London
Type
conf
DOI
10.1109/UCC.2014.72
Filename
7027539
Link To Document