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
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;
Conference_Titel :
Utility and Cloud Computing (UCC), 2014 IEEE/ACM 7th International Conference on
Conference_Location :
London
DOI :
10.1109/UCC.2014.72