{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T02:23:30Z","timestamp":1779330210896,"version":"3.51.4"},"reference-count":9,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:p>Cardinality Estimation (CE) is a fundamental but critical problem in DBMS query optimization, while deep learning techniques have made significant breakthroughs in the research of CE. However, apart from requiring sufficiently large training data to cover all possible query regions for accurate estimation, current query-driven CE methods also suffer from workload drifts. In fact, retraining or fine-tuning needs cardinality labels as ground truth and obtaining the labels through DBMS is also expensive. Therefore, we propose CEDA, a novel domain-adaptive CE system. CEDA can achieve more accurate estimations by automatically generating workloads as training data according to the data distribution in the database, and incorporating histogram information into an attention-based cardinality estimator. To solve the problem of workload drifts in real-world environments, CEDA adopts a domain adaptation strategy, making the model more robust and perform well on an unlabeled workload with a large difference from the feature distribution of the training set.<\/jats:p>","DOI":"10.14778\/3611540.3611589","type":"journal-article","created":{"date-parts":[[2023,9,15]],"date-time":"2023-09-15T11:32:37Z","timestamp":1694777557000},"page":"3934-3937","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["CEDA: Learned Cardinality Estimation with Domain Adaptation"],"prefix":"10.14778","volume":"16","author":[{"given":"Zilong","family":"Wang","sequence":"first","affiliation":[{"name":"Beijing Jiaotong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qixiong","family":"Zeng","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haowen","family":"Lu","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yue","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Jiaotong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,8]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2006.190"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.14778\/3329772.3329780"},{"key":"e_1_2_1_3_1","volume-title":"Learned cardinalities: Estimating correlated joins with deep learning. arXiv preprint arXiv:1809.00677","author":"Kipf Andreas","year":"2018","unstructured":"Andreas Kipf, Thomas Kipf, Bernhard Radke, Viktor Leis, Peter Boncz, and Alfons Kemper. 2018. Learned cardinalities: Estimating correlated joins with deep learning. arXiv preprint arXiv:1809.00677 (2018)."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.14778\/2850583.2850594"},{"key":"e_1_2_1_5_1","volume-title":"VLDB","volume":"98","author":"Slutz Donald R","year":"1998","unstructured":"Donald R Slutz. 1998. Massive stochastic testing of SQL. In VLDB, Vol. 98. Citeseer, 618--622."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.14778\/3485450.3485459"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3517870"},{"key":"e_1_2_1_8_1","article-title":"Visualizing data using t-SNE","volume":"9","author":"der Maaten Laurens Van","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research 9, 11 (2008).","journal-title":"Journal of machine learning research"},{"key":"e_1_2_1_9_1","volume-title":"Deep unsupervised cardinality estimation. arXiv preprint arXiv:1905.04278","author":"Yang Zongheng","year":"2019","unstructured":"Zongheng Yang, Eric Liang, Amog Kamsetty, Chenggang Wu, Yan Duan, Xi Chen, Pieter Abbeel, Joseph M Hellerstein, Sanjay Krishnan, and Ion Stoica. 2019. Deep unsupervised cardinality estimation. arXiv preprint arXiv:1905.04278 (2019)."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3611540.3611589","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T22:35:54Z","timestamp":1757543754000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3611540.3611589"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":9,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["10.14778\/3611540.3611589"],"URL":"https:\/\/doi.org\/10.14778\/3611540.3611589","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2023,8]]},"assertion":[{"value":"2023-08-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}