{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:03:33Z","timestamp":1750309413293,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":33,"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\/3627673.3679700","type":"proceedings-article","created":{"date-parts":[[2024,10,20]],"date-time":"2024-10-20T19:34:11Z","timestamp":1729452851000},"page":"218-227","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Online and Safe Configuration Tuning with Semi-supervised Anomaly Detection"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-5371-2098","authenticated-orcid":false,"given":"Haitian","family":"Chen","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\/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\/0000-0003-2203-5506","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\/0009-0000-2305-7702","authenticated-orcid":false,"given":"Xiushi","family":"Feng","sequence":"additional","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\/0000-0002-9006-095X","authenticated-orcid":false,"given":"Jiandong","family":"Xie","sequence":"additional","affiliation":[{"name":"Huawei Technologies Co., Ltd., Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5579-0378","authenticated-orcid":false,"given":"Han","family":"Su","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":[{"volume-title":"An introduction to outlier analysis","author":"Aggarwal Charu C","key":"e_1_3_2_1_1_1","unstructured":"Charu C Aggarwal and Charu C Aggarwal. 2017. An introduction to outlier analysis. Springer."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Charu C Aggarwal and Saket Sathe. 2017. Outlier ensembles: An introduction. (2017).","DOI":"10.1007\/978-3-319-54765-7"},{"key":"e_1_3_2_1_3_1","volume-title":"Pasting small votes for classification in large databases and on-line. Machine learning","author":"Breiman Leo","year":"1999","unstructured":"Leo Breiman. 1999. Pasting small votes for classification in large databases and on-line. Machine learning, Vol. 36 (1999), 85--103."},{"key":"e_1_3_2_1_4_1","volume-title":"Random forests. Machine learning","author":"Breiman Leo","year":"2001","unstructured":"Leo Breiman. 2001. Random forests. Machine learning, Vol. 45 (2001), 5--32."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3517882"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.14778\/3457390.3457404"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.14778\/3598581.3598597"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.14778\/3594512.3594525"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.14778\/2732240.2732246"},{"key":"e_1_3_2_1_11_1","volume-title":"A tutorial on Bayesian optimization. arXiv preprint arXiv:1807.02811","author":"Frazier Peter I","year":"2018","unstructured":"Peter I Frazier. 2018. A tutorial on Bayesian optimization. arXiv preprint arXiv:1807.02811 (2018)."},{"key":"e_1_3_2_1_12_1","first-page":"2011","article-title":"Sequential model-based optimization for general algorithm configuration. In Learning and Intelligent Optimization: 5th International Conference","volume":"21","author":"Hutter Frank","year":"2011","unstructured":"Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown. 2011. Sequential model-based optimization for general algorithm configuration. In Learning and Intelligent Optimization: 5th International Conference, LION 5, Rome, Italy, January 17--21, 2011. Selected Papers 5. Springer, 507--523.","journal-title":"LION 5, Rome, Italy"},{"key":"e_1_3_2_1_13_1","volume-title":"12th USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage 20)","author":"Kanellis Konstantinos","year":"2020","unstructured":"Konstantinos Kanellis, Ramnatthan Alagappan, and Shivaram Venkataraman. 2020. Too many knobs to tune? towards faster database tuning by pre-selecting important knobs. In 12th USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage 20)."},{"key":"e_1_3_2_1_14_1","volume-title":"Carlo Curino, and Shivaram Venkataraman","author":"Kanellis Konstantinos","year":"2022","unstructured":"Konstantinos Kanellis, Cong Ding, Brian Kroth, Andreas M\u00fcller, Carlo Curino, and Shivaram Venkataraman. 2022. LlamaTune: sample-efficient DBMS configuration tuning. arXiv preprint arXiv:2203.05128 (2022)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3380591"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.14778\/2850583.2850594"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.14778\/3352063.3352129"},{"key":"e_1_3_2_1_18_1","volume-title":"Efficient Cardinality and Cost Estimation with Bidirectional Compressor-based Ensemble Learning. In 2023 IEEE International Conference on Data Mining (ICDM). IEEE, 388--397","author":"Liang Zibo","year":"2023","unstructured":"Zibo Liang, Xu Chen, Yan Zhao, Jiandong Xie, Kai Zeng, and Kai Zheng. 2023. Efficient Cardinality and Cost Estimation with Bidirectional Compressor-based Ensemble Learning. In 2023 IEEE International Conference on Data Mining (ICDM). IEEE, 388--397."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.5555\/3586589.3586643"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/167293.167637"},{"key":"e_1_3_2_1_21_1","volume-title":"Proceedings of the ACM SIGKDD 2014 Workshop on Outlier Detection and Description under Data Diversity (ODD2)","author":"Micenkov\u00e1 Barbora","year":"2014","unstructured":"Barbora Micenkov\u00e1, Brian McWilliams, and Ira Assent. 2014. Learning outlier ensembles: The best of both worlds--supervised and unsupervised. In Proceedings of the ACM SIGKDD 2014 Workshop on Outlier Detection and Description under Data Diversity (ODD2). New York, NY, USA. Citeseer, 51--54."},{"key":"e_1_3_2_1_22_1","volume-title":"Learning representations for outlier detection on a budget. arXiv preprint arXiv:1507.08104","author":"Micenkov\u00e1 Barbora","year":"2015","unstructured":"Barbora Micenkov\u00e1, Brian McWilliams, and Ira Assent. 2015. Learning representations for outlier detection on a budget. arXiv preprint arXiv:1507.08104 (2015)."},{"key":"e_1_3_2_1_23_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_24_1","volume-title":"Sequence to sequence learning with neural networks. Advances in neural information processing systems","author":"Sutskever Ilya","year":"2014","unstructured":"Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. Sequence to sequence learning with neural networks. Advances in neural information processing systems, Vol. 27 (2014)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/1807167.1807327"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3064029"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3299869.3300085"},{"key":"e_1_3_2_1_28_1","volume-title":"Facilitating database tuning with hyper-parameter optimization: a comprehensive experimental evaluation. arXiv preprint arXiv:2110.12654","author":"Zhang Xinyi","year":"2021","unstructured":"Xinyi Zhang, Zhuo Chang, Yang Li, Hong Wu, Jian Tan, Feifei Li, and Bin Cui. 2021. Facilitating database tuning with hyper-parameter optimization: a comprehensive experimental evaluation. arXiv preprint arXiv:2110.12654 (2021)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457291"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3514221.3526176"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2018.8489605"},{"key":"e_1_3_2_1_32_1","volume-title":"Pyod: A python toolbox for scalable outlier detection. arXiv preprint arXiv:1901.01588","author":"Zhao Yue","year":"2019","unstructured":"Yue Zhao, Zain Nasrullah, and Zheng Li. 2019. Pyod: A python toolbox for scalable outlier detection. arXiv preprint arXiv:1901.01588 (2019)."},{"key":"e_1_3_2_1_33_1","volume-title":"Lero: A Learning-to-Rank Query Optimizer. arXiv preprint arXiv:2302.06873","author":"Zhu Rong","year":"2023","unstructured":"Rong Zhu, Wei Chen, Bolin Ding, Xingguang Chen, Andreas Pfadler, Ziniu Wu, and Jingren Zhou. 2023. Lero: A Learning-to-Rank Query Optimizer. arXiv preprint arXiv:2302.06873 (2023)."}],"event":{"name":"CIKM '24: The 33rd ACM International Conference on Information and Knowledge Management","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"],"location":"Boise ID USA","acronym":"CIKM '24"},"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\/3627673.3679700","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3627673.3679700","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:58:13Z","timestamp":1750294693000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679700"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,21]]},"references-count":33,"alternative-id":["10.1145\/3627673.3679700","10.1145\/3627673"],"URL":"https:\/\/doi.org\/10.1145\/3627673.3679700","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"}}]}}