A pipelined data-parallel algorithm for ILP

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 data...

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Bibliographic Details
Published in:2005 IEEE International Conference on Cluster Computing pp. 1 - 10
Main Authors: Fonseca, N.A., Silva, F., Costa, V.S., Camacho, R.
Format: Conference Proceeding
Language:English
Published: IEEE 01.09.2005
Subjects:
Clustering algorithms
Data mining
Humans
Logic programming
Parallel processing
Parallel programming
Relational databases
Scalability
Sequential analysis
System testing
ISBN:9780780394858, 0780394852
ISSN:1552-5244
Online Access:Get full text
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Summary: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
ISBN:9780780394858
0780394852
ISSN:1552-5244
DOI:10.1109/CLUSTR.2005.347059