http://www.cnr.it/ontology/cnr/individuo/prodotto/ID168848

Mining@home: towards a public-resource computing framework for distributed data mining (Articolo in rivista)

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Mining@home: towards a public-resource computing framework for distributed data mining (Articolo in rivista) (literal)

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Alternative label

Lucchese C.; Mastroianni C.; Orlando S.; Talia D. (2010)
Mining@home: towards a public-resource computing framework for distributed data mining
in Concurrency and computation (Online); John Wiley & Sons, Inc., Hoboken (Stati Uniti d'America)
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In: Concurrency and Computation-Practice & Experience, vol. 22 (5) pp. 658 - 682. Wiley, 2010. (literal)

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Note

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1. CNR, ISTI, HPC Lab, I-56100 Pisa, Italy 2. CNR, ICAR, Arcavacata Di Rende, Italy 3. Univ Venice, Dept Comp Sci, I-30123 Venice, Italy - CNR-ISTI, Pisa 4. Univ Calabria, DEIS, I-87036 Arcavacata Di Rende, Italy (literal)

Titolo

Mining@home: towards a public-resource computing framework for distributed data mining (literal)

Abstract

Several classes of scientific and commercial applications require the execution of a large number of independent tasks. One highly successful and low-cost mechanism for acquiring the necessary computing power for these applications is the 'public-resource computing', or 'desktop Grid' paradigm, which exploits the computational power of private computers. So far, this paradigm has not been applied to data mining applications for two main reasons. First, it is not straightforward to decompose a data mining algorithm into truly independent sub-tasks. Second, the large volume of the involved data makes it difficult to handle the communication costs of a parallel paradigm. This paper introduces a general framework for distributed data mining applications called Mining@home. In particular, we focus on one of the main data mining problems: the extraction of closed frequent itemsets from transactional databases. We show that it is possible to decompose this problem into independent tasks, which however need to share a large volume of the data. We thus introduce a data-intensive computing network, which adopts a P2P topology based on super peers with caching capabilities, aiming to support the dissemination of large amounts of information. Finally, we evaluate the execution of a pattern extraction task on such network. (literal)

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