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Updated: Jun 12, 2026
09:20
Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications
Published on: February 23, 2019
A bounded and adaptive memory-based approach to mine frequent patterns from very large databases.
Muhaimenul Adnan1, Reda Alhajj
1Department of Computer Science, University of Calgary, Calgary, AB T2N 1N4, Canada. madnan@ucalgary.ca
Summary
This study presents a new association rules mining (ARM) model that efficiently handles large transactional databases without relying on virtual memory managers. The approach optimizes memory usage, improving performance and allowing concurrent tasks.
Area of Science:
- Computer Science
- Data Mining
- Database Management
Background:
- Existing association rules mining (ARM) methods often require large primary memory, leading to performance issues like thrashing due to virtual memory manager (VMM) reliance.
- The VMM's default paging mechanism can cause unpredictable behavior and inefficient data handling for memory-intensive ARM tasks.
Purpose of the Study:
- To propose an ARM model that efficiently mines transactional databases of any size without depending on the VMM.
- To introduce a specialized memory management system for ARM that optimizes primary and secondary memory usage.
- To enable concurrent execution of high-priority tasks with minimal interference from the ARM process.
Main Methods:
- Developed a novel ARM model with a specialized memory management system.
- Implemented a data structure that prioritizes primary memory allocation and strategically offloads to secondary memory when necessary.
- Optimized secondary memory access through block-by-block processing to enhance spatial and temporal locality.
Main Results:
- The proposed ARM model successfully mines large transactional databases using only a bounded portion of primary memory.
- The specialized memory management system effectively controls virtual memory access, preventing thrashing and improving mining process efficiency.
- Experimental results confirm the approach's applicability and effectiveness, demonstrating improved performance and reduced interference with other tasks.
Conclusions:
- The novel ARM model offers an efficient and scalable solution for mining large transactional databases.
- By managing memory explicitly, the approach overcomes the limitations of traditional VMM-based methods.
- The framework enhances system resource utilization, allowing for more efficient multitasking environments.
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