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Creators/Authors contains: "Victor Giannakouris, Immanuel Trummer"

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  1. (, CEUR workshop proceedings)
    The goal of this thesis is to introduce a new design for building federated query optimizers, based on machine learning. We propose a modular and flexible architecture, allowing a federated query optimizer to integrate with any database system that supports SQL, with close-to-zero engineering effort. By observing the performance of the external systems, our optimizer learns and builds cost models on-the-fly, enabling federated query optimization with negligible communication with the external systems. To demonstrate the potential of this research plan, we present a prototype of our federated query optimizer built on top of Spark SQL. Our implementation effectively accelerates federated queries, achieving up to 7.5x better query execution times compared to the vanilla implementation of Spark SQL. 
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    Accepted Manuscript Full Text Available