Data mining tools: from web to grid architectures

Authors:
Davide Anguita;Arianna Poggi;Fabio Rivieccio;Anna Marina Scapolla
Affiliations:
DIBE – Dept. of Biophysical and Electronic Engineering, University of Genoa, Genova, Italy;DIBE – Dept. of Biophysical and Electronic Engineering, University of Genoa, Genova, Italy;DIBE – Dept. of Biophysical and Electronic Engineering, University of Genoa, Genova, Italy;DIBE – Dept. of Biophysical and Electronic Engineering, University of Genoa, Genova, Italy
Venue:
EGC'05 Proceedings of the 2005 European conference on Advances in Grid Computing
Year:
2005

Citing 7
Cited 0

Support-Vector Networks

Machine Learning
Evaluating the Generalization Ability of Support Vector Machines through the Bootstrap

Neural Processing Letters
Alpha seeding for support vector machines

Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Model Selection and Error Estimation

Machine Learning
The NEOS Server

IEEE Computational Science & Engineering
A parallel solver for large quadratic programs in training support vector machines

Parallel Computing - Special issue: Parallel computing in numerical optimization
Classes of kernels for machine learning: a statistics perspective

The Journal of Machine Learning Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

The paradigm of Grid computing is establishing as a novel, reliable and effective method to exploit a pool of hardware resources and make them available to the users. Data-mining benefits from the Grid as it often requires to run time consuming algorithms on large amounts of data which maybe reside on a different resource from the one having the proper data-mining algorithms. Also, in recent times, machine learning methods have been available to the purposes of knowledge discovery, which is a topic of interest for a large community of users. The present work is an account of the evolution of the ways in which a user can be provided with a data-mining service: from a web interface to a Grid service, the exploitation of a complex resource from a technical and a user-friendliness point of view is considered. More specifically, the goal is to show the interest/advantage of running data mining algorithm on the Grid. Such an environment can employ computational and storage resources in an efficient way, making it possible to open data mining services to Grid users and providing services to business contexts.