Goal-Driven Data Partitioning for Quantitative Rule Derivation

Junping Sun

Goal-Driven Data Partitioning for Quantitative Rule Derivation

Junping Sun

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In this paper, we present a goal-driven approach for quantitative rule derivation in terms of multidimensional attributes and data partitioning. Our goal-driven quantitative rule derivation method utilizes domain knowledge such as concept hierarchies and cardinality values of attributes in a database. We employ a multidimensional data structure to store the derived knowledge and information about the set of derived rules in the primary memory. So the derived quantitative rules can be retrieved to support on-line analytic processing and real-time decision making.

Original language	English
Title of host publication	15th International Conference on Computers and Their Applications 2000, CATA 2000
Editors	Sung Y. Shin
Publisher	The International Society for Computers and Their Applications (ISCA)
Pages	128-133
Number of pages	6
ISBN (Electronic)	9781618395467
State	Published - 2000
Event	15th International Conference on Computers and Their Applications, CATA 2000 - New Orleans, United States Duration: Mar 29 2000 → Mar 31 2000

Publication series

Name	15th International Conference on Computers and Their Applications 2000, CATA 2000

Conference

Conference	15th International Conference on Computers and Their Applications, CATA 2000
Country/Territory	United States
City	New Orleans
Period	3/29/00 → 3/31/00

Bibliographical note

Publisher Copyright:
Copyright © (2000) by the International Society for Computers and Their Applications. All rights reserved.

ASJC Scopus Subject Areas

General Computer Science

Cite this

Goal-Driven Data Partitioning for Quantitative Rule Derivation. / Sun, Junping.
15th International Conference on Computers and Their Applications 2000, CATA 2000. ed. / Sung Y. Shin. The International Society for Computers and Their Applications (ISCA), 2000. p. 128-133 (15th International Conference on Computers and Their Applications 2000, CATA 2000).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sun, J 2000, Goal-Driven Data Partitioning for Quantitative Rule Derivation. in SY Shin (ed.), 15th International Conference on Computers and Their Applications 2000, CATA 2000. 15th International Conference on Computers and Their Applications 2000, CATA 2000, The International Society for Computers and Their Applications (ISCA), pp. 128-133, 15th International Conference on Computers and Their Applications, CATA 2000, New Orleans, United States, 3/29/00.

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AB - In this paper, we present a goal-driven approach for quantitative rule derivation in terms of multidimensional attributes and data partitioning. Our goal-driven quantitative rule derivation method utilizes domain knowledge such as concept hierarchies and cardinality values of attributes in a database. We employ a multidimensional data structure to store the derived knowledge and information about the set of derived rules in the primary memory. So the derived quantitative rules can be retrieved to support on-line analytic processing and real-time decision making.

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M3 - Conference contribution

AN - SCOPUS:85131953668

T3 - 15th International Conference on Computers and Their Applications 2000, CATA 2000

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BT - 15th International Conference on Computers and Their Applications 2000, CATA 2000

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T2 - 15th International Conference on Computers and Their Applications, CATA 2000

Y2 - 29 March 2000 through 31 March 2000

ER -

Goal-Driven Data Partitioning for Quantitative Rule Derivation

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus Subject Areas

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Cite this