Paper Info
Title
Mining@home: toward a public-resource computing framework for distributed data mining
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. Copyright © 2009 John Wiley & Sons, Ltd.
Year
DOI
Venue
2010
10.1002/cpe.v22:5
Concurrency and Computation: Practice and Experience
Keywords
DocType
Volume
public-resource computing framework,involved data,main data mining problem,independent task,independent sub-tasks,data-intensive computing network,large volume,large number,large amount,data mining algorithm,data mining application
Journal
22
Issue
ISSN
Citations 
5
1532-0626
2
PageRank 
References 
Authors
0.36
2
4
Name
Order
Citations
PageRank
Claudio Lucchese1110473.76
C. Mastroianni2557.12
Salvatore Orlando31595202.29
D. Talia4863.98