数据仓库中物化视图优化的多目标遗传算法
Multi-Objective Genetic Algorithm for Materialized View Optimization in Data Warehouses
AI总结：
本文提出一种基于多目标遗传算法的物化视图选择方法，通过染色体编码、自适应变异率和lexicase选择优化执行时间与总成本，在TPC-H基准上比现有方法平均执行时间提升11%、成本降低1600万。
摘要：
物化视图可以显著提高数据库查询性能，但确定要物化的最优视图集具有挑战性。先前在自动化和优化物化视图选择方面的工作在执行时间和总成本上存在局限性。本文提出了一种新颖的基于遗传算法的物化视图选择方法，旨在最小化执行时间和总成本。我们的技术将物化视图配置编码为染色体，并通过多代进化种群来发现高质量解。我们采用自适应变异率、多目标适应度函数和lexicase选择来增强遗传搜索。在TPC-H基准上的综合实验证明了我们算法的有效性。与最先进的方法相比，我们的方法将平均执行时间提高了11%，并将总物化视图成本平均降低了1600万。这些收益凸显了数据驱动进化方法的优势。我们的遗传算法框架在效率和总成本降低方面均显著优于当前的物化视图选择技术。这项工作代表了在企业系统中实现物化视图高性能和成本效益利用方面的重要进展。
英文摘要：
Materialized views can significantly improve database query performance but identifying the optimal set of views to materialize is challenging. Prior work on automating and optimizing materialized view selection has limitations in execution time and total cost. In this paper, we present a novel genetic algorithm based approach to materialized view selection that aims to minimize execution time and total cost. Our technique encodes materialized view configurations as chromosomes and evolves the population over generations to discover high quality solutions. We employ an adaptive mutation rate, multi-objective fitness function, and lexicase selection to enhance genetic search. Comprehensive experiments on the TPC-H benchmark demonstrate the effectiveness of our algorithm. Compared to stateof-the-art methods, our approach improves average execution time by 11% and reduces total materialized view costs by an average of 16 million. These gains highlight the benefits of a datadriven evolutionary approach. Our genetic algorithm framework significantly outperforms current materialized view selection techniques in both efficiency and total cost reduction. This work represents an important advance in enabling performant and cost-effective utilization of materialized views in enterprise systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
