Distributed knowledge discovery with the parallel KDDML system

Authors:
A. Romei;M. Sciolla;F. Turini;M. Valentini
Affiliations:
Department of Computer Science, University of Pisa, Largo B. Pontecorvo, Pisa, Italy;Department of Computer Science, University of Pisa, Largo B. Pontecorvo, Pisa, Italy;Department of Computer Science, University of Pisa, Largo B. Pontecorvo, Pisa, Italy;Department of Computer Science, University of Pisa, Largo B. Pontecorvo, Pisa, Italy
Venue:
PDCN'06 Proceedings of the 24th IASTED international conference on Parallel and distributed computing and networks
Year:
2006

Citing 4
Cited 0

A database perspective on knowledge discovery

Communications of the ACM
Efficient C4.5

IEEE Transactions on Knowledge and Data Engineering
The programming model of ASSIST, an environment for parallel and distributed portable applications

Parallel Computing - Special issue: Advanced environments for parallel and distributed computing
Adaptive and Resource-Aware Mining of Frequent Sets

ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining

Quantified Score

Hi-index	0.00

Visualization

Abstract

KDDML is a middleware language and system for knowledge discovery. The challenge that motivated the development of a distributed version of the originally "stand-alone" KDDML (KDD Markup Language) environment was on one side to exploit the parallelism, and on the other side to overcome the problem of data immovability, a quite frequent restriction on real-world data collections that has principally a privacy-preserving purpose. The last question is addressed by moving the code and "mining" the data "on the place", that is by adapting the computation to the availability and localization of the data.