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Due to the outstanding capability of capturing underlying data distributions,deep learning techniques have been recently utilized for a series oftraditional database problems. In this paper, we investigate the possibilitiesof utilizing deep learning for cardinality estimation of similarity selection.Answering this problem accurately and efficiently is essential to many datamanagement applications, especially for query optimization. Moreover, in someapplications the estimated cardinality is supposed to be consistent andinterpretable. Hence a monotonic estimation w.r.t. the query threshold ispreferred. We propose a novel and generic method that can be applied to anydata type and distance function. Our method consists of a feature extractionmodel and a regression model. The feature extraction model transforms originaldata and threshold to a Hamming space, in which a deep learning-basedregression model is utilized to exploit the incremental property of cardinalityw.r.t. the threshold for both accuracy and monotonicity. We develop a trainingstrategy tailored to our model as well as techniques for fast estimation. Wealso discuss how to handle updates. We demonstrate the accuracy and theefficiency of our method through experiments, and show how it improves theperformance of a query optimizer.

Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach