Using SQL primitives and parallel DB servers to speed up knowledge discovery in large relational databases

Freitas, Alex A. and Lavington, Simon H. (1996) Using SQL primitives and parallel DB servers to speed up knowledge discovery in large relational databases. In: Trappl, Robert, ed. Cybernetics and systems '96 : proceedings of the Thirteenth European Meeting on Cybernetics and Systems Research. Austrian Society for Cybernetic Studies, Vienna, Austria, pp. 955-960. ISBN 3-85206-133-4. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:21376)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.

Abstract

Efficiency is crucial in KDD (Knowledge Discovery in Databases), due to the huge amount of data stored in commercial databases. We argue that high efficiency in KDD can be achieved by combining two approaches, namely mapping KDD functionality onto standard DBMS operations and executing KDD tasks on a parallel SQL server. We propose generic KDD primitives which underly the candidate-rule evaluation procedures of many KDD algorithms, and we evaluate the speed up achieved by a parallel SQL server when executing a decision-tree learner algorithm implemented via these primitives.

Item Type:	Book section
Subjects:	Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions:	Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User:	Mark Wheadon
Date Deposited:	03 Sep 2009 19:32 UTC
Last Modified:	05 Nov 2024 09:59 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/21376 (The current URI for this page, for reference purposes)

University of Kent Author Information

Freitas, Alex A..

Creator's ORCID:	https://orcid.org/0000-0001-9825-4700
CReDIT Contributor Roles:

Depositors only (login required):

Download Statistics

Total unique views for this document in KAR since July 2020. For more details click on the image.