<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pietro Michiardi</style></author><author><style face="normal" font="default" size="100%">Antonio Barbuzzi</style></author><author><style face="normal" font="default" size="100%">Alessandro Finamore</style></author><author><style face="normal" font="default" size="100%">Stefano Traverso</style></author><author><style face="normal" font="default" size="100%">Daniele Apiletti</style></author><author><style face="normal" font="default" size="100%">Elena Baralis</style></author><author><style face="normal" font="default" size="100%">Tania Cerquitelli</style></author><author><style face="normal" font="default" size="100%">Silvia Chiusano</style></author><author><style face="normal" font="default" size="100%">Luigi Grimaudo</style></author><author><style face="normal" font="default" size="100%">A. Rufini</style></author><author><style face="normal" font="default" size="100%">Francesco Matera</style></author><author><style face="normal" font="default" size="100%">A. Valentii</style></author><author><style face="normal" font="default" size="100%">Maurizio Dusi</style></author><author><style face="normal" font="default" size="100%">Mohamed Ahmed</style></author><author><style face="normal" font="default" size="100%">Tivadar Szemethy</style></author><author><style face="normal" font="default" size="100%">L. Németh</style></author><author><style face="normal" font="default" size="100%">R. Szalay</style></author><author><style face="normal" font="default" size="100%">Ilias Leontiadis</style></author><author><style face="normal" font="default" size="100%">Yan Grunenberger</style></author><author><style face="normal" font="default" size="100%">P. Casas</style></author><author><style face="normal" font="default" size="100%">Alessandro D’Alconzo</style></author><author><style face="normal" font="default" size="100%">A Bär</style></author><author><style face="normal" font="default" size="100%">D Rossi</style></author><author><style face="normal" font="default" size="100%">YiXi Gong</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Basic Network Data Analysis</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">big data</style></keyword><keyword><style  face="normal" font="default" size="100%">storage</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">05/2013</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">D3.1</style></number><publisher><style face="normal" font="default" size="100%">mPlane Consortium</style></publisher><pub-location><style face="normal" font="default" size="100%">Torino</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This document describes the requirements, input, output for the algorithms needed to perform analytic tasks on a large amount of data, in the context of WP3. Starting from the use cases defined in WP1, we identify the algorithms needed to address the various scenario requirements. Operating on a large amount of data, these algorithms strive for parallel and scalable approaches; the designing and implementation of the algorithm itself can be a challenging research task since today very little is known concerning how to develop efficient and scalable algorithms that runs on parallel processing frameworks.&lt;br /&gt;The algorithm in the storage layer are characterized by the fact that they operate on a large amount of data, and produce a concise representation of it, extracting features and aggregating it, so that the produced output is easier to handle and understand. Depending on the amount of data produced, on the scenario characteristics and on the time constraints, algorithms can require a real time (or near real time) or a batch processing.&lt;br /&gt;For each algorithm and use case, the input data and the initial state, the computation to run and the output produced are described.&lt;/p&gt;</style></abstract><work-type><style face="normal" font="default" size="100%">Public Deliverable</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniele Apiletti</style></author><author><style face="normal" font="default" size="100%">Elena Baralis</style></author><author><style face="normal" font="default" size="100%">Tania Cerquitelli</style></author><author><style face="normal" font="default" size="100%">Silvia Chiusano</style></author><author><style face="normal" font="default" size="100%">Luigi Grimaudo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SEARUM: a cloud-based SErvice for Association RUle Mining</style></title><secondary-title><style face="normal" font="default" size="100%">The 11th IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA-13)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">association rule mining</style></keyword><keyword><style  face="normal" font="default" size="100%">cloud-based service</style></keyword><keyword><style  face="normal" font="default" size="100%">distributed computing model</style></keyword><keyword><style  face="normal" font="default" size="100%">network data analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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&amp;nbsp;SEARUM in mining association rules on a distributed computing model.&lt;/p&gt;</style></abstract></record></records></xml>