A pipelined data-parallel algorithm for ILP

Author

Fonseca, Nuno A. ; Silva, Fernando ; Costa, Vitor Santos ; Camacho, Rui

Author_Institution

DCC-FC, Univ. do Porto

fYear

2005

fDate

Sept. 2005

Firstpage

1

Lastpage

10

Abstract

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 rides from relational databases consisting of multiple tables. However, 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

Keywords

data mining; inductive logic programming; parallel algorithms; pipeline processing; relational databases; inductive logic programming; knowledge discovery; multirelational data mining; parallel processing; pipelined data-parallel algorithm; relational databases; Clustering algorithms; Data mining; Humans; Logic programming; Parallel processing; Parallel programming; Relational databases; Scalability; Sequential analysis; System testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Cluster Computing, 2005. IEEE International

Conference_Location

Burlington, MA

ISSN

1552-5244

Print_ISBN

0-7803-9486-0

Electronic_ISBN

1552-5244

Type

conf

DOI

10.1109/CLUSTR.2005.347059

Filename

4154102