Year: 2025
Type: article
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.
Source: Computers
Authors: Peixiong He, Libo Sun, Xian Gao, Yi Zhou, Xiao Qin
Institutions: Auburn University, Columbus State University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,172
OpenAlex ID: W4411077892