{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:03:42Z","timestamp":1750309422670,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":46,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,21]]},"DOI":"10.1145\/3627673.3679771","type":"proceedings-article","created":{"date-parts":[[2024,10,20]],"date-time":"2024-10-20T19:34:21Z","timestamp":1729452861000},"page":"2981-2990","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Cause-Focused Query Optimizer Alert System"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8936-4533","authenticated-orcid":false,"given":"Runfan","family":"Ye","sequence":"first","affiliation":[{"name":"Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2203-5506","authenticated-orcid":false,"given":"Zibo","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3909-4021","authenticated-orcid":false,"given":"Xu","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3557-6598","authenticated-orcid":false,"given":"Shuncheng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0217-3998","authenticated-orcid":false,"given":"Kai","family":"Zheng","sequence":"additional","affiliation":[{"name":"Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,10,21]]},"reference":[{"unstructured":"2024. PostgreSQL. https:\/\/www.postgresql.org\/.","key":"e_1_3_2_1_1_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_2_1","DOI":"10.14778\/3598581.3598597"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_3_1","DOI":"10.14778\/3594512.3594525"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_4_1","DOI":"10.1145\/329.318578"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_5_1","DOI":"10.1145\/375663.375685"},{"key":"e_1_3_2_1_6_1","volume-title":"Learning confidence for out-of-distribution detection in neural networks. arXiv preprint arXiv:1802.04865","author":"DeVries Terrance","year":"2018","unstructured":"Terrance DeVries and Graham W Taylor. 2018. Learning confidence for out-of-distribution detection in neural networks. arXiv preprint arXiv:1802.04865 (2018)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_7_1","DOI":"10.1145\/3588963"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_8_1","DOI":"10.14778\/3329772.3329780"},{"volume-title":"DACE: A Database-Agnostic Cost Estimator. In 2024 IEEE 40th International Conference on Data Engineering (ICDE).","author":"Zibo","unstructured":"Zibo Liang et al. 2024. DACE: A Database-Agnostic Cost Estimator. In 2024 IEEE 40th International Conference on Data Engineering (ICDE).","key":"e_1_3_2_1_9_1"},{"key":"e_1_3_2_1_10_1","volume-title":"Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference. ArXiv abs\/1506.02158","author":"Gal Yarin","year":"2015","unstructured":"Yarin Gal and Zoubin Ghahramani. 2015. Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference. ArXiv abs\/1506.02158 (2015)."},{"key":"e_1_3_2_1_11_1","volume-title":"International Conference on Machine Learning.","author":"Gal Yarin","year":"2015","unstructured":"Yarin Gal and Zoubin Ghahramani. 2015. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. In International Conference on Machine Learning."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_12_1","DOI":"10.1109\/ICCV48922.2021.00823"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_13_1","DOI":"10.1609\/aaai.v32i1.11757"},{"key":"e_1_3_2_1_14_1","volume-title":"DeepDB: Learn from Data, not from Queries! arXiv: Databases,arXiv: Databases (Sep","author":"Hilprecht Benjamin","year":"2019","unstructured":"Benjamin Hilprecht, Andreas Schmidt, Moritz Kulessa, Alejandro Molina, Kristian Kersting, and Carsten Binnig. 2019. DeepDB: Learn from Data, not from Queries! arXiv: Databases,arXiv: Databases (Sep 2019)."},{"key":"e_1_3_2_1_15_1","volume-title":"Long short-term memory. Neural computation 9, 8","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735--1780."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_16_1","DOI":"10.14778\/3484224.3484233"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_17_1","DOI":"10.1145\/169725.169708"},{"key":"e_1_3_2_1_18_1","volume-title":"Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload","author":"Kang KokZhi","year":"2021","unstructured":"JohanKokZhi Kang, Gaurav Gaurav, SienYi Tan, Feng Cheng, Shixuan Sun, and Bingsheng He. 2021. Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload. Cornell University - arXiv,Cornell University - arXiv (Mar 2021)."},{"key":"e_1_3_2_1_19_1","volume-title":"Learned Cardinalities: Estimating Correlated Joins with Deep Learning. ArXiv abs\/1809.00677","author":"Kipf Andreas","year":"2018","unstructured":"Andreas Kipf, Thomas Kipf, Bernhard Radke, Viktor Leis, Peter A. Boncz, and Alfons Kemper. 2018. Learned Cardinalities: Estimating Correlated Joins with Deep Learning. ArXiv abs\/1809.00677 (2018)."},{"key":"e_1_3_2_1_20_1","volume-title":"Learning to Optimize Join Queries With Deep Reinforcement Learning. arXiv: Databases,arXiv: Databases (Aug","author":"Krishnan Sanjay","year":"2018","unstructured":"Sanjay Krishnan, Zongheng Yang, Ken Goldberg, JosephM. Hellerstein, and Ion Stoica. 2018. Learning to Optimize Join Queries With Deep Reinforcement Learning. arXiv: Databases,arXiv: Databases (Aug 2018)."},{"key":"e_1_3_2_1_21_1","volume-title":"Learning to Optimize Join Queries With Deep Reinforcement Learning. arXiv: Databases,arXiv: Databases (Aug","author":"Krishnan Sanjay","year":"2018","unstructured":"Sanjay Krishnan, Zongheng Yang, Ken Goldberg, JosephM. Hellerstein, and Ion Stoica. 2018. Learning to Optimize Join Queries With Deep Reinforcement Learning. arXiv: Databases,arXiv: Databases (Aug 2018)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_22_1","DOI":"10.14778\/2850583.2850594"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_23_1","DOI":"10.1007\/s00778-017-0480--7"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_24_1","DOI":"10.1145\/3514221.3526179"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_25_1","DOI":"10.14778\/3476249.3476254"},{"key":"e_1_3_2_1_26_1","volume-title":"Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness. ArXiv abs\/2006.10108","author":"Liu Jeremiah Zhe","year":"2020","unstructured":"Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, and Balaji Lakshminarayanan. 2020. Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness. ArXiv abs\/2006.10108 (2020)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_27_1","DOI":"10.1145\/3448016.3452838"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_28_1","DOI":"10.14778\/3342263.3342644"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_29_1","DOI":"10.1145\/3211954.3211957"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_30_1","DOI":"10.14778\/3583140.3583164"},{"key":"e_1_3_2_1_31_1","volume-title":"New TPC benchmarks for decision support and web commerce. ACM SIGMOD Record (Dec","author":"Poess Meikel","year":"2000","unstructured":"Meikel Poess and Chris Floyd. 2000. New TPC benchmarks for decision support and web commerce. ACM SIGMOD Record (Dec 2000), 64--71. https:\/\/doi.org\/10. 1145\/369275.369291"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_32_1","DOI":"10.1145\/564691.564759"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_33_1","DOI":"10.14778\/3368289.3368296"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_34_1","DOI":"10.14778\/3368289.3368296"},{"key":"e_1_3_2_1_35_1","volume-title":"Proceedings of the VLDB Endowment (Aug","author":"Tzoumas Kostas","year":"2011","unstructured":"Kostas Tzoumas, Amol Deshpande, and Christian S. Jensen. 2011. Lightweight graphical models for selectivity estimation without independence assumptions. Proceedings of the VLDB Endowment (Aug 2011), 852--863. https:\/\/doi.org\/10. 14778\/3402707.3402724"},{"key":"e_1_3_2_1_36_1","volume-title":"Attention is All you Need. Neural Information Processing Systems,Neural Information Processing Systems (Jun","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, AidanN. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. Neural Information Processing Systems,Neural Information Processing Systems (Jun 2017)."},{"key":"e_1_3_2_1_37_1","volume-title":"Stage: Query Execution Time Prediction in Amazon Redshift. arXiv preprint arXiv:2403.02286","author":"Marcus Ryan","year":"2024","unstructured":"ZiniuWu, Ryan Marcus, Zhengchun Liu, Parimarjan Negi, Vikram Nathan, Pascal Pfeil, Gaurav Saxena, Mohammad Rahman, Balakrishnan Narayanaswamy, and Tim Kraska. 2024. Stage: Query Execution Time Prediction in Amazon Redshift. arXiv preprint arXiv:2403.02286 (2024)."},{"key":"e_1_3_2_1_38_1","volume-title":"BayesCard: A Unified Bayesian Framework for Cardinality Estimation. arXiv: Databases,arXiv: Databases (Dec","author":"Wu Zhanjun","year":"2020","unstructured":"Zhanjun Wu and Amir Shaikhha. 2020. BayesCard: A Unified Bayesian Framework for Cardinality Estimation. arXiv: Databases,arXiv: Databases (Dec 2020)."},{"key":"e_1_3_2_1_39_1","volume-title":"Generalized out-of-distribution detection: A survey. arXiv preprint arXiv:2110.11334","author":"Yang Jingkang","year":"2021","unstructured":"Jingkang Yang, Kaiyang Zhou, Yixuan Li, and Ziwei Liu. 2021. Generalized out-of-distribution detection: A survey. arXiv preprint arXiv:2110.11334 (2021)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_40_1","DOI":"10.1145\/3514221.3517885"},{"key":"e_1_3_2_1_41_1","volume-title":"NeuroCard: One Cardinality Estimator for All Tables. arXiv: Databases,arXiv: Databases (Jun","author":"Yang Zongheng","year":"2020","unstructured":"Zongheng Yang, Amog Kamsetty, Sifei Luan, Eric Liang, Yan Duan, Xi Chen, and Ion Stoica. 2020. NeuroCard: One Cardinality Estimator for All Tables. arXiv: Databases,arXiv: Databases (Jun 2020)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_42_1","DOI":"10.14778\/3368289.3368294"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_43_1","DOI":"10.1109\/icde48307.2020"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_44_1","DOI":"10.1145\/3514221.3526156"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_45_1","DOI":"10.14778\/3397230.3397238"},{"key":"e_1_3_2_1_46_1","volume-title":"FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation. Very Large Data Bases,Very Large Data Bases (Jan","author":"Zhu Rong","year":"2020","unstructured":"Rong Zhu, ZhanjunWu, Yuxing Han, Kai Zeng, Andreas Pfadler, Zhuzhong Qian, Jingren Zhou, and Bin Cui. 2020. FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation. Very Large Data Bases,Very Large Data Bases (Jan 2020)."}],"event":{"sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"],"acronym":"CIKM '24","name":"CIKM '24: The 33rd ACM International Conference on Information and Knowledge Management","location":"Boise ID USA"},"container-title":["Proceedings of the 33rd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679771","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3627673.3679771","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:58:28Z","timestamp":1750294708000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679771"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,21]]},"references-count":46,"alternative-id":["10.1145\/3627673.3679771","10.1145\/3627673"],"URL":"https:\/\/doi.org\/10.1145\/3627673.3679771","relation":{},"subject":[],"published":{"date-parts":[[2024,10,21]]},"assertion":[{"value":"2024-10-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}