A pipelined data-parallel algorithm for ILP

• Type: Article in International Conference Proceedings Book
• Date: 2006

Authors

Nuno A. Fonseca (Author) FCUP; Fernando Silva (Author) FCUP; Vitor Santos Costa (Author) FCUP; Rui Camacho (Author) FEUP

Conference proceedings International

Title: 2005 IEEE INTERNATIONAL CONFERENCE ON CLUSTER COMPUTING (CLUSTER)

Pages: 253-262

IEEE International Conference on Cluster Computing

Burlington, MA, SEP 27-30, 2005

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 9 Citations

INSPEC

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-004-QYS

DOI: 10.1109/clustr.2005.347059

Abstract (EN): The amount of data collected and stored in databases is growing considerably for almost all areas of human activity. Processing this amount of data is very expensive, both humanly and computationally. This justifies the increased interest both on the automatic discovery of useful knowledge from databases, and on using parallel processing for this task. Multi Relational Data Mining (MRDM) techniques, such as Inductive Logic Programming (ILP), can learn rules from relational databases consisting of multiple tables. However current ILP systems are designed to run in main memory and can have long running times. We propose a pipelined data-parallel algorithm for ILP. The algorithm was implemented and evaluated on a commodity PC cluster with 8 processors. The results show that our algorithm yields excellent speedups, while preserving the quality of learning.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 10