Large volumes of data are being produced by various modern applications at an ever increasing rate. These applications range from wireless sensors networks to social networks. 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 is an exploratory data analysis method able to discover interesting and hidden correlations among data. Since this data 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.

SEARUM: a cloud-based SErvice for Association RUle Mining / Apiletti, Daniele; Baralis, ELENA MARIA; Cerquitelli, Tania; Chiusano, SILVIA ANNA; Grimaudo, Luigi. - STAMPA. - (2013), pp. 1283-1290. ((Intervento presentato al convegno 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (ISPA 2013) tenutosi a Melbourne (Australia) nel 16-18 July 2013 [10.1109/TrustCom.2013.153].

SEARUM: a cloud-based SErvice for Association RUle Mining

APILETTI, DANIELE;BARALIS, ELENA MARIA;CERQUITELLI, TANIA;CHIUSANO, SILVIA ANNA;GRIMAUDO, LUIGI
2013

Abstract

Large volumes of data are being produced by various modern applications at an ever increasing rate. These applications range from wireless sensors networks to social networks. 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 is an exploratory data analysis method able to discover interesting and hidden correlations among data. Since this data 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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2518918
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