Context-Aware Query Performance Optimization for Transportation in Big Data Analytics
Abstract
Transit data is stored in multiple data sources doing exploratory analytics on these datasets require querying more than one database. Users need to be updated regarding route services. Hence there is a need for a smarter query framework which can query multiple databases and retrieve the results in less time. The aim of this study is to improve a cost-based query optimizer that utilizes external optimized copy registered in a database and enhances the performance in terms of execution time of the analytical query for data analytics for public transportation datasets stored in different databases. The improvement is initiated in this study by rewriting the query plan to utilize the externally registered optimized copy of data in the relational expression tree during the analytical query execution.
Subject Area
Computer science|Computer Engineering|Information science
Recommended Citation
Muniswamaiah, Manoj, "Context-Aware Query Performance Optimization for Transportation in Big Data Analytics" (2024). ETD Collection for Pace University. AAI30992561.
https://digitalcommons.pace.edu/dissertations/AAI30992561
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