Distributed Data Mining Using a Public Resource Computing Framework

The public resource computing paradigm is often used as a successful and low cost mechanism for the management of several classes of scientific and commercial applications that require the execution of a large number of independent tasks. Public computing frameworks, also known as "Desktop Grids", exploit the computational power and storage facilities of private computers, or "workers". Despite the inherent decentralized nature of the applications for which they are devoted, these systems often adopt a centralized mechanism for the assignment of jobs and distribution of input data, as is the case for BOINC, the most popular framework in this realm. We present a decentralized framework that aims at increasing the flexibility and robustness of public computing applications, thanks to two basic features: (i) the adoption of a P2P protocol for dynamically matching the job specifications with the worker characteristics, without relying on centralized resources; (ii) the use of distributed cache servers for an efficient dissemination and reutilization of data files. This framework is exploitable for a wide set of applications. In this work, we describe how a Java prototype of the framework was used to tackle the problem of mining frequent itemsets from a transactional dataset, and show some preliminary yet interesting performance results that prove the efficiency improvements that can derive from the presented architecture.