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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| Format: | Article |
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MDPI AG
2025-06-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/14/6/220 |
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| author | Peixiong He Libo Sun Xian Gao Yi Zhou Xiao Qin |
| author_facet | Peixiong He Libo Sun Xian Gao Yi Zhou Xiao Qin |
| author_sort | Peixiong He |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e0d629b9076f4917a58083b844f1fbd8 |
| institution | Kabale University |
| issn | 2073-431X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-e0d629b9076f4917a58083b844f1fbd82025-08-20T03:27:30ZengMDPI AGComputers2073-431X2025-06-0114622010.3390/computers14060220GARMT: Grouping-Based Association Rule Mining to Predict Future Tables in Database QueriesPeixiong He0Libo Sun1Xian Gao2Yi Zhou3Xiao Qin4Department of Computer Science and Software Engineering, Auburn University, Auburn, AL 36849, USADepartment of Computer Science and Software Engineering, Auburn University, Auburn, AL 36849, USATSYS School of Computer Science, Columbus State University, Columbus, GA 31907, USATSYS School of Computer Science, Columbus State University, Columbus, GA 31907, USADepartment of Computer Science and Software Engineering, Auburn University, Auburn, AL 36849, USAIn 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.https://www.mdpi.com/2073-431X/14/6/220SQL databasedata mining |
| spellingShingle | Peixiong He Libo Sun Xian Gao Yi Zhou Xiao Qin GARMT: Grouping-Based Association Rule Mining to Predict Future Tables in Database Queries Computers SQL database data mining |
| title | GARMT: Grouping-Based Association Rule Mining to Predict Future Tables in Database Queries |
| title_full | GARMT: Grouping-Based Association Rule Mining to Predict Future Tables in Database Queries |
| title_fullStr | GARMT: Grouping-Based Association Rule Mining to Predict Future Tables in Database Queries |
| title_full_unstemmed | GARMT: Grouping-Based Association Rule Mining to Predict Future Tables in Database Queries |
| title_short | GARMT: Grouping-Based Association Rule Mining to Predict Future Tables in Database Queries |
| title_sort | garmt grouping based association rule mining to predict future tables in database queries |
| topic | SQL database data mining |
| url | https://www.mdpi.com/2073-431X/14/6/220 |
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