Methodology of Bivalent Data Management in Oracle Database

Relational databases are widespread, based on the entities and relationships forming the data model. Data to be handled can originate from various sources, delimited by the precision and format. The boolean data type is not part of the Structured Query Language (SQL) standard, resulting in the necessity to define the management on your own. Domain integrity is typically enhanced by the check constraint on the data model level or by trigger forming additional database object. This paper deals with the existing approaches emulating boolean data type in Oracle Database, but also focuses on mapping and specific boolean data type introduced in Oracle Database 23c. It aims to provide a methodology and performance study for using and modeling bivalent or trivalent values.

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Publication Details

Published
2024-10-16
DOI
https://doi.org/10.23919/ntsp61680.2024.10726314
Citations
1
Primary Topic
Data Mining Algorithms and Applications
Type
article
Field-Weighted Citation Impact
0.78
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Methodology of Bivalent Data Management in Oracle Database

Michal Kvet
1 citations
Data Mining Algorithms and Applications
0.78
article

Methodology of Bivalent Data Management in Oracle Database

Michal Kvet
article en
1 citations

Abstract

Relational databases are widespread, based on the entities and relationships forming the data model. Data to be handled can originate from various sources, delimited by the precision and format. The boolean data type is not part of the Structured Query Language (SQL) standard, resulting in the necessity to define the management on your own. Domain integrity is typically enhanced by the check constraint on the data model level or by trigger forming additional database object. This paper deals with the existing approaches emulating boolean data type in Oracle Database, but also focuses on mapping and specific boolean data type introduced in Oracle Database 23c. It aims to provide a methodology and performance study for using and modeling bivalent or trivalent values.

Openalex Percentile: Top 20%
Data Mining Algorithms and Applications
0.78
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