@article{Karamyan_2021, title={Cardinality Estimation of an SQL Query Using Recursive Neural Networks}, volume={54}, url={http://mpcs.sci.am/index.php/mpcs/article/view/461}, DOI={10.51408/1963-0058}, abstractNote={<p>To learn complex interactions between predicates and accurately estimate the cardinality of an SQL query, we develop a novel framework based on recursive tree-structured neural networks, which take into account the natural properties of logical expressions: compositionality and n-ary associativity. The proposed architecture is an extension of MSCN (multi-set convolutional network) for queries containing both conjunction and disjunction operators. The goal is to represent an arbitrary logical expression in a continuous vector space by combining sub-expression vectors according to the operator type. We compared the proposed approach with the histogram-based approach on the real-world dataset and showed that our approach significantly outperforms histograms with a large margin.</p>}, journal={Mathematical Problems of Computer Science}, author={Karamyan, Davit S.}, year={2021}, month={Dec.}, pages={41–52} }