@inproceedings{0c08d0d8f26048f6a10910a7afe6ee06,
title = "Investigating Machine Learning Models for Cardinality Estimation: An interactive Approach",
abstract = "Recently developed machine learning (ML) models for cardinality estimation improve the accuracy significantly, which inspires database developers to tap learned cardinality estimators into RDBMS. However, query optimization is a complicated task involving multiple factors, and accurate estimations may not necessarily procure optimal query plans. In this demonstration, we present an interactive platform that facilitates the investigation of various learned cardinality estimators and their utilities in query optimization in real RDBMS. We bridge the model zoo and the optimizer of PostgreSQL and provide a graphical interface for users to configure parameters and visualize the results interactively. In addition, our platform exposes extensible interfaces for users to deploy plug-and-play ML estimators and datasets. The demonstration video can be found on YouTube https://www.youtube.com/watch?v=ay3w7V1JhUU.",
keywords = "Cardinality estimation, Machine learning",
author = "Rui Li and Kangfei Zhao and Mengyu Li and Yu, \{Jeffrey Xu\}",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.; 31st International Conference on Database Systems for Advanced Applications, DASFAA 2026 ; Conference date: 27-04-2026 Through 30-04-2026",
year = "2026",
doi = "10.1007/978-981-92-0378-9\_53",
language = "English",
isbn = "9789819203772",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "713--717",
editor = "Hyungsoo Jung and Tianzheng Wang and Masashi Toyoda and Hyuk-Yoon Kwon and Jae-woong Lee",
booktitle = "Database Systems for Advanced Applications - 31st International Conference, DASFAA 2026, Proceedings",
address = "Germany",
}