Large volumes of data are being produced by modern applications at an ever increasing rate. The automatic analysis of such huge data volume is a challenging task since a large amount of interesting knowledge can be extracted. Association rule mining allows discovering interesting and hidden correlations among data. Since this mining process is characterized by computationally intensive tasks, efficient distributed approaches are needed to increase its scalability. This paper proposes a novel cloud-based service, named SeARuM, to efficiently mine association rules on a distributed computing model. SeARuM consists of a series of distributed MapReduce jobs run in the cloud. Each job performs a different step in the association rule mining process. As a case study, the proposed approach has been applied to the network data scenario. The experimental validation, performed on two real network datasets, shows the effectiveness and the efficiency of SeARuM in mining association rules on a distributed computing model.

Association Rule Mining as a Cloud-Based Service / Apiletti, D.; Baralis, ELENA MARIA; Cerquitelli, Tania; Chiusano, SILVIA ANNA; Grimaudo, Luigi. - STAMPA. - (2013), pp. 461-468. ((Intervento presentato al convegno 21st Italian Symposium on Advanced Database Systems tenutosi a Roccella Jonica, Reggio Calabria, Italy nel June 30th – July 04th, 2013.

Association Rule Mining as a Cloud-Based Service

Apiletti D.;BARALIS, ELENA MARIA;CERQUITELLI, TANIA;CHIUSANO, SILVIA ANNA;GRIMAUDO, LUIGI
2013

Abstract

Large volumes of data are being produced by modern applications at an ever increasing rate. The automatic analysis of such huge data volume is a challenging task since a large amount of interesting knowledge can be extracted. Association rule mining allows discovering interesting and hidden correlations among data. Since this mining process is characterized by computationally intensive tasks, efficient distributed approaches are needed to increase its scalability. This paper proposes a novel cloud-based service, named SeARuM, to efficiently mine association rules on a distributed computing model. SeARuM consists of a series of distributed MapReduce jobs run in the cloud. Each job performs a different step in the association rule mining process. As a case study, the proposed approach has been applied to the network data scenario. The experimental validation, performed on two real network datasets, shows the effectiveness and the efficiency of SeARuM in mining association rules on a distributed computing model.
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2518921
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo