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Robust Cardinality Estimator by Non-autoregressive Model

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Part of the Communications in Computer and Information Science book series (CCIS,volume 1457)


In database systems, cardinality estimation is a fundamental technology that significantly impacts query performance. Recently, machine-learning techniques are employed for cardinality estimation, which learns dependencies among attributes. However, they have a problem that the estimation accuracy is unstable and the inference speed is slow. In this paper, we propose a stable and fast cardinality estimation method that learns dependencies among attributes by a non-autoregressive model and performs estimation in fewer steps and proper order according to a given query at the inference phase.


  • Cardinality estimation
  • Machine learning

This paper is based on results obtained from a project, JPNP16007, commissioned by the New Energy and Industrial Technology Development Organization (NEDO).

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  • DOI: 10.1007/978-3-030-93849-9_3
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  1. 1.

    Any non-autoregressive model (e.g., Transformer-based model) can be used.

  2. 2.

    Only the decoder.


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Correspondence to Ryuichi Ito .

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Ito, R., Xiao, C., Onizuka, M. (2022). Robust Cardinality Estimator by Non-autoregressive Model. In: Fletcher, G., Nakano, K., Sasaki, Y. (eds) Software Foundations for Data Interoperability. SFDI 2021. Communications in Computer and Information Science, vol 1457. Springer, Cham.

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  • Print ISBN: 978-3-030-93848-2

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