Query Optimization in Crowd-Sourcing Using Multi-Objective Ant Lion Optimizer

Deepak Kumar, D. Mehrotra, Rohit Bansal
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引用次数: 5

Abstract

Nowadays, query optimization is a biggest concern for crowd-sourcing systems, which are developed for relieving the user burden of dealing with the crowd. Initially, a user needs to submit a structured query language (SQL) based query and the system takes the responsibility of query compiling, generating an execution plan, and evaluating the crowd-sourcing market place. The input queries have several alternative execution plans and the difference in crowd-sourcing cost between the worst and best plans. In relational database systems, query optimization is essential for crowd-sourcing systems, which provides declarative query interfaces. Here, a multi-objective query optimization approach using an ant-lion optimizer was employed for declarative crowd-sourcing systems. It generates a query plan for developing a better balance between the latency and cost. The experimental outcome of the proposed methodology was validated on UCI automobile and Amazon Mechanical Turk (AMT) datasets. The proposed methodology saves 30%-40% of cost in crowd-sourcing query optimization compared to the existing methods.
基于多目标蚁狮优化器的众包查询优化
查询优化是当前众包系统最关注的问题,众包系统是为了减轻用户处理人群的负担而开发的。最初,用户需要提交一个基于结构化查询语言(SQL)的查询,系统负责查询编译、生成执行计划和评估众包市场。输入查询有几个可选的执行计划,以及最差和最佳计划之间的众包成本差异。在关系数据库系统中,查询优化对于提供声明性查询接口的众包系统至关重要。本文采用了一种基于蚁狮优化器的多目标查询优化方法,用于声明性众包系统。它生成查询计划,以便更好地平衡延迟和成本。在UCI汽车和Amazon Mechanical Turk (AMT)数据集上验证了所提出方法的实验结果。与现有的众包查询优化方法相比,该方法节省了30%-40%的成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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