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Parallel Data Mining for Very Large Relational Databases

Freitas, Alex A. and Lavington, Simon H. (1996) Parallel Data Mining for Very Large Relational Databases. In: Liddell, Heather, ed. High-Performance Computing and Networking International Conference and Exhibition. Lecture Notes in Computer Science . Springer, Berlin, Germany, pp. 158-163. ISBN 978-3-540-61142-4. E-ISBN 978-3-540-49955-8. (doi:10.1007/3-540-61142-8_542) (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:21306)

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. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1007/3-540-61142-8_542

Abstract

Data mining, or Knowledge Discovery in Databases (KDD), is of little benefit to commercial enterprises unless it can be carried out efficiently on realistic volumes of data. Operational factors also dictate that KDD should be performed within the context of standard DBMS. Fortunately, relational DBMS have a declarative query interface (SQL) that has allowed designers of parallel hardware to exploit data parallelism efficiently. Thus, an effective approach to the problem of efficient KDD consists of arranging that KDD tasks execute on a parallel SQL server. In this paper we devise generic KDD primitives, map these to SQL and present some results of running these primitives on a commercially-available parallel SQL server.

Item Type: Book section
DOI/Identification number: 10.1007/3-540-61142-8_542
Uncontrolled keywords: Communication Overhead; Labour Force Survey; Candidate Attribute; Candidate Rule; Multiple Count
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: 24 Aug 2009 19:34 UTC
Last Modified: 16 Feb 2021 12:31 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/21306 (The current URI for this page, for reference purposes)
Freitas, Alex A.: https://orcid.org/0000-0001-9825-4700
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