GRELA: Exploiting graph representation learning in effective approximate query processing

Abstract Approximate query processing ( AQP ) plays a critical role in modern data analytics. Although machine learning models are used for AQP, existing methods fail to uncover implicit relationships among the underlying data, the aggregate functions in queries, and the query predicates. In this work, we propose a G raph RE presentation L earning-based A QP model ( GRELA for short) for answering queries with multiple aggregate functions. GRELA models the aggregate functions and the query predicates as task and clause nodes respectively in a graph and then learns appropriate node representations via its two modules. In particular, the $$\texttt {Encoder}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Encoder</mml:mi> </mml:math> module coalesces query predicates and underlying data into the representations of clause nodes. The $$\mathbf {\texttt {Graph}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>Graph</mml:mi> </mml:math> module bridges task nodes and clause nodes such that each task node can aggregate the information from its neighborhood into its representation. Through the inner products of clause and task representations, GRELA is able to make accurate estimates for queries with multiple aggregate functions. Extensive experimental results verify that GRELA outperforms the state-of-the-art AQP methods on different kinds of datasets.