A requirements analysis for parallel KDD systems

William A. Maniatty, Mohammed J. Zaki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

The current generation of data mining tools have limited capacit y and performance, since these tools tend to be sequential. This paper explores a migration path out of this bottlenec kby considering an in tegrated hardware and softw are approach to parallelize data mining. Our analysis shows that parallel data mining solutions require the following components: parallel data mining algorithms, parallel and distributed data bases, parallel file systems, parallel I/O, tertiary storage, management of online data, support for heterogeneous data representations, securit y, qualit yof service and pricing metrics. State of the art technology in these areas is surveyed with an eye tow ards an integration strategy leading to a complete solution.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages358-365
Number of pages8
Volume1800 LNCS
Publication statusPublished - 1 Dec 2000
Externally publishedYes
Event15 Workshops Held in Conjunction with the IEEE International Parallel and Distributed Processing Symposium, IPDPS 2000 - Cancun, Mexico
Duration: 1 May 20005 May 2000

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1800 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other15 Workshops Held in Conjunction with the IEEE International Parallel and Distributed Processing Symposium, IPDPS 2000
CountryMexico
CityCancun
Period1/5/005/5/00

Fingerprint

Requirements Analysis
Parallel Systems
Data mining
Data Mining
Parallel algorithms
Parallel File System
Parallel I/O
Storage management
Distributed Databases
Parallel Algorithms
Pricing
Migration
Hardware
Tend
Metric
Path
Costs

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Maniatty, W. A., & Zaki, M. J. (2000). A requirements analysis for parallel KDD systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1800 LNCS, pp. 358-365). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1800 LNCS).

A requirements analysis for parallel KDD systems. / Maniatty, William A.; Zaki, Mohammed J.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1800 LNCS 2000. p. 358-365 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 1800 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Maniatty, WA & Zaki, MJ 2000, A requirements analysis for parallel KDD systems. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 1800 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1800 LNCS, pp. 358-365, 15 Workshops Held in Conjunction with the IEEE International Parallel and Distributed Processing Symposium, IPDPS 2000, Cancun, Mexico, 1/5/00.
Maniatty WA, Zaki MJ. A requirements analysis for parallel KDD systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1800 LNCS. 2000. p. 358-365. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Maniatty, William A. ; Zaki, Mohammed J. / A requirements analysis for parallel KDD systems. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 1800 LNCS 2000. pp. 358-365 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{1b753ff9542e446b8277f4259adcb5b3,
title = "A requirements analysis for parallel KDD systems",
abstract = "The current generation of data mining tools have limited capacit y and performance, since these tools tend to be sequential. This paper explores a migration path out of this bottlenec kby considering an in tegrated hardware and softw are approach to parallelize data mining. Our analysis shows that parallel data mining solutions require the following components: parallel data mining algorithms, parallel and distributed data bases, parallel file systems, parallel I/O, tertiary storage, management of online data, support for heterogeneous data representations, securit y, qualit yof service and pricing metrics. State of the art technology in these areas is surveyed with an eye tow ards an integration strategy leading to a complete solution.",
author = "Maniatty, {William A.} and Zaki, {Mohammed J.}",
year = "2000",
month = "12",
day = "1",
language = "English",
isbn = "354067442X",
volume = "1800 LNCS",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "358--365",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}

TY - GEN

T1 - A requirements analysis for parallel KDD systems

AU - Maniatty, William A.

AU - Zaki, Mohammed J.

PY - 2000/12/1

Y1 - 2000/12/1

N2 - The current generation of data mining tools have limited capacit y and performance, since these tools tend to be sequential. This paper explores a migration path out of this bottlenec kby considering an in tegrated hardware and softw are approach to parallelize data mining. Our analysis shows that parallel data mining solutions require the following components: parallel data mining algorithms, parallel and distributed data bases, parallel file systems, parallel I/O, tertiary storage, management of online data, support for heterogeneous data representations, securit y, qualit yof service and pricing metrics. State of the art technology in these areas is surveyed with an eye tow ards an integration strategy leading to a complete solution.

AB - The current generation of data mining tools have limited capacit y and performance, since these tools tend to be sequential. This paper explores a migration path out of this bottlenec kby considering an in tegrated hardware and softw are approach to parallelize data mining. Our analysis shows that parallel data mining solutions require the following components: parallel data mining algorithms, parallel and distributed data bases, parallel file systems, parallel I/O, tertiary storage, management of online data, support for heterogeneous data representations, securit y, qualit yof service and pricing metrics. State of the art technology in these areas is surveyed with an eye tow ards an integration strategy leading to a complete solution.

UR - http://www.scopus.com/inward/record.url?scp=84876366917&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84876366917&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84876366917

SN - 354067442X

SN - 9783540674429

VL - 1800 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 358

EP - 365

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -