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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Deepak Kumar & Deepti Mehrotra & Rohit Bansal, 2019. "Query Optimization in Crowd-Sourcing Using Multi-Objective Ant Lion Optimizer," International Journal of Information Technology and Web Engineering (IJITWE), IGI Global, vol. 14(4), pages 50-63, October.
    • Handle: RePEc:igg:jitwe0:v:14:y:2019:i:4:p:50-63
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