Discovering Imprecise Temporal Associations

Thomas Sudkamp; Robert J. Hammell

doi:10.1109/NAFIPS.2003.1226763

Discovering Imprecise Temporal Associations

Thomas Sudkamp, Robert J. Hammell

Wright State University

Research output: Contribution to journal › Article › peer-review

Abstract

This paper examines the discovery of associations defined by fuzzy temporal predicates in time-tagged information from multiple distributed data sources. The use of fuzzy predicates provides the ability to represent and analyze imprecise temporal relation-ships, including temporal ordering and duration. In particular, we are concerned with the adaptability of common data mining strategies to relations defined by fuzzy temporal predicates.

Original language	American English
Journal	Default journal
DOIs	https://doi.org/10.1109/NAFIPS.2003.1226763
State	Published - Jan 1 2003

Keywords

Algorithm design and analysis
Computer science
Data analysis
Data engineering
Data mining
Databases
Distributed computing
Fuzzy sets
Information analysis
Marine vehicles

Disciplines

Computer Sciences
Engineering
Mathematics
Physical Sciences and Mathematics

Access to Document

10.1109/NAFIPS.2003.1226763License: Unspecified

http://ieeexplore.ieee.org/document/1226763/

Cite this

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title = "Discovering Imprecise Temporal Associations",

abstract = " This paper examines the discovery of associations defined by fuzzy temporal predicates in time-tagged information from multiple distributed data sources. The use of fuzzy predicates provides the ability to represent and analyze imprecise temporal relation-ships, including temporal ordering and duration. In particular, we are concerned with the adaptability of common data mining strategies to relations defined by fuzzy temporal predicates.",

keywords = "Algorithm design and analysis, Computer science, Data analysis, Data engineering, Data mining, Databases, Distributed computing, Fuzzy sets, Information analysis, Marine vehicles",

author = "Thomas Sudkamp and Hammell, {Robert J.}",

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AB - This paper examines the discovery of associations defined by fuzzy temporal predicates in time-tagged information from multiple distributed data sources. The use of fuzzy predicates provides the ability to represent and analyze imprecise temporal relation-ships, including temporal ordering and duration. In particular, we are concerned with the adaptability of common data mining strategies to relations defined by fuzzy temporal predicates.

KW - Algorithm design and analysis

KW - Computer science

KW - Data analysis

KW - Data engineering

KW - Data mining

KW - Databases

KW - Distributed computing

KW - Fuzzy sets

KW - Information analysis

KW - Marine vehicles

UR - https://corescholar.libraries.wright.edu/cse/433

UR - http://ieeexplore.ieee.org/document/1226763/

U2 - 10.1109/NAFIPS.2003.1226763

DO - 10.1109/NAFIPS.2003.1226763

M3 - Article

JO - Default journal

JF - Default journal

ER -

Discovering Imprecise Temporal Associations

Abstract

Keywords

Disciplines

Access to Document

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