GARMT: Grouping-Based Association Rule Mining to Predict Future Tables in Database Queries

In modern data management systems, structured query language (SQL) databases, as a mature and stable technology, have become the standard for processing structured data. These databases ensure data integrity through strongly typed schema definitions and support complex transaction management and eff...

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Veröffentlicht in:Computers (Basel) Jg. 14; H. 6; S. 220
Hauptverfasser: He, Peixiong, Sun, Libo, Gao, Xian, Zhou, Yi, Qin, Xiao
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.06.2025
Schlagworte:
Algorithms
Data management
Data mining
Datasets
Efficiency
Energy consumption
Management systems
Methods
Optimization
Queries
Query languages
Query processing
SQL database
Structured data
Structured query language
Tables (data)
Workloads
United States
ISSN:2073-431X, 2073-431X
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Abstract In modern data management systems, structured query language (SQL) databases, as a mature and stable technology, have become the standard for processing structured data. These databases ensure data integrity through strongly typed schema definitions and support complex transaction management and efficient query processing capabilities. However, data sparsity—where most fields in large table sets remain unused by most queries—leads to inefficiencies in access optimization. We propose a grouping-based approach (GARMT) that partitions SQL queries into fixed-size groups and applies a modified FP-Growth algorithm (GFP-Growth) to identify frequent table access patterns. Experiments on a real-world dataset show that grouping significantly reduces runtime—by up to 40%—compared to the ungrouped baseline while preserving rule relevance. These results highlight the practical value of query grouping for efficient pattern discovery in sparse database environments.
AbstractList In modern data management systems, structured query language (SQL) databases, as a mature and stable technology, have become the standard for processing structured data. These databases ensure data integrity through strongly typed schema definitions and support complex transaction management and efficient query processing capabilities. However, data sparsity—where most fields in large table sets remain unused by most queries—leads to inefficiencies in access optimization. We propose a grouping-based approach (GARMT) that partitions SQL queries into fixed-size groups and applies a modified FP-Growth algorithm (GFP-Growth) to identify frequent table access patterns. Experiments on a real-world dataset show that grouping significantly reduces runtime—by up to 40%—compared to the ungrouped baseline while preserving rule relevance. These results highlight the practical value of query grouping for efficient pattern discovery in sparse database environments.
Audience Academic
Author Qin, Xiao
Sun, Libo
Zhou, Yi
Gao, Xian
He, Peixiong
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  surname: Qin
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ContentType Journal Article
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SubjectTerms Algorithms
Data management
Data mining
Datasets
Efficiency
Energy consumption
Management systems
Methods
Optimization
Queries
Query languages
Query processing
SQL database
Structured data
Structured query language
Tables (data)
Workloads
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Title GARMT: Grouping-Based Association Rule Mining to Predict Future Tables in Database Queries
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