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A technique for parallel query optimization using MapReduce framework and a semantic-based clustering method.

Elham Azhir1, Nima Jafari Navimipour2, Mehdi Hosseinzadeh3

  • 1Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

Peerj. Computer Science
|June 18, 2021
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Summary
This summary is machine-generated.

This study presents a parallel MapReduce model for clustering large query datasets, improving database query optimization. The approach enhances scalability for access plan recommendation techniques.

Keywords:
Access plan recommendationCluster computingDBSCAN AlgorithmMapReduceParallel ProcessingQuery optimization

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Area of Science:

  • Computer Science
  • Database Systems
  • Data Mining

Background:

  • Query optimization is crucial for efficient database performance, aiming to find the best Query Execution Plan (QEP).
  • Access plan recommendation reuses existing QEPs for new queries by clustering similar query workloads.
  • Traditional clustering algorithms struggle with large datasets due to high processing times.

Purpose of the Study:

  • To address the scalability challenges of clustering large query datasets for access plan recommendation.
  • To evaluate a parallel clustering model using MapReduce for query workload analysis.

Main Methods:

  • Implemented and tested a parallel clustering model utilizing the MapReduce framework.
  • Applied the model to variant sizes of large query datasets for performance evaluation.

Main Results:

  • Demonstrated the effectiveness of the parallel implementation for clustering query workloads.
  • Achieved good scalability in processing large datasets, overcoming traditional algorithm limitations.

Conclusions:

  • Parallel clustering using MapReduce is an effective and scalable solution for large-scale query workload analysis.
  • This approach significantly improves the efficiency of access plan recommendation in database systems.