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

Deepak Kumar, Deepti Mehrotra and Rohit Bansal
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Deepak Kumar: Amity University Uttar Pradesh, Noida, India
Deepti Mehrotra: Amity University Uttar Pradesh, Noida, India
Rohit Bansal: Rohit Bansal, Rajiv Gandhi Institute of Petroleum Technology, Amethi, India

International Journal of Information Technology and Web Engineering (IJITWE), 2019, vol. 14, issue 4, 50-63

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.

Date: 2019
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