{"ID":2836958,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.20293","arxiv_id":"2511.20293","title":"Forgetting by Pruning: Data Deletion in Join Cardinality Estimation","abstract":"Machine unlearning in learned cardinality estimation (CE) systems presents unique challenges due to the complex distributional dependencies in multi-table relational data. Specifically, data deletion, a core component of machine unlearning, faces three critical challenges in learned CE models: attribute-level sensitivity, inter-table propagation and domain disappearance leading to severe overestimation in multi-way joins. We propose Cardinality Estimation Pruning (CEP), the first unlearning framework specifically designed for multi-table learned CE systems. CEP introduces Distribution Sensitivity Pruning, which constructs semi-join deletion results and computes sensitivity scores to guide parameter pruning, and Domain Pruning, which removes support for value domains entirely eliminated by deletion. We evaluate CEP on state-of-the-art architectures NeuroCard and FACE across IMDB and TPC-H datasets. Results demonstrate CEP consistently achieves the lowest Q-error in multi-table scenarios, particularly under high deletion ratios, often outperforming full retraining. Furthermore, CEP significantly reduces convergence iterations, incurring negligible computational overhead of 0.3%-2.5% of fine-tuning time.","short_abstract":"Machine unlearning in learned cardinality estimation (CE) systems presents unique challenges due to the complex distributional dependencies in multi-table relational data. Specifically, data deletion, a core component of machine unlearning, faces three critical challenges in learned CE models: attribute-level sensitivi...","url_abs":"https://arxiv.org/abs/2511.20293","url_pdf":"https://arxiv.org/pdf/2511.20293v1","authors":"[\"Chaowei He\",\"Yuanjun Liu\",\"Qingzhi Ma\",\"Shenyuan Ren\",\"Xizhao Luo\",\"Lei Zhao\",\"An Liu\"]","published":"2025-11-25T13:25:59Z","proceeding":"cs.DB","tasks":"[\"cs.DB\",\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false}