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However, state-of-the-art CC algorithms struggle to perform well across diverse workloads, and most do not consider workload drifts.<\/jats:p>\n                  <jats:p>In this paper, we propose NeurCC, a novel learned concurrency control algorithm that achieves high performance across diverse workloads. The algorithm is quick to optimize, making it robust against dynamic workloads. It learns a function that captures a large number of design choices from existing CC algorithms. The function is implemented as an efficient in-database lookup table that maps database states to concurrency control actions. The learning process is based on a combination of Bayesian optimization and a novel graph reduction search algorithm, which converges quickly to a function that achieves high transaction throughput. We compare NeurCC against five state-of-the-art CC algorithms and show that it consistently outperforms the baselines both in transaction throughput and in optimization time.<\/jats:p>","DOI":"10.1145\/3802088","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T18:19:16Z","timestamp":1779128356000},"page":"1-28","source":"Crossref","is-referenced-by-count":0,"title":["Modeling Concurrency Control as a Learnable Function"],"prefix":"10.1145","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6350-4887","authenticated-orcid":false,"given":"Hexiang","family":"Pan","sequence":"first","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8605-076X","authenticated-orcid":false,"given":"Shaofeng","family":"Cai","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8158-3636","authenticated-orcid":false,"given":"Tien Tuan Anh","family":"Dinh","sequence":"additional","affiliation":[{"name":"Deakin University, Melbourne, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1436-3916","authenticated-orcid":false,"given":"Yuncheng","family":"Wu","sequence":"additional","affiliation":[{"name":"Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7823-8068","authenticated-orcid":false,"given":"Yeow Meng","family":"Chee","sequence":"additional","affiliation":[{"name":"Singapore University of Technology and Design, Singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7483-0045","authenticated-orcid":false,"given":"Gang","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4446-1100","authenticated-orcid":false,"given":"Beng Chin","family":"Ooi","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,5,18]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2026. 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