{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T01:29:50Z","timestamp":1769736590761,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":36,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T00:00:00Z","timestamp":1715558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006374","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62072264"],"award-info":[{"award-number":["62072264"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,5,13]]},"DOI":"10.1145\/3589334.3645392","type":"proceedings-article","created":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T07:08:13Z","timestamp":1715152093000},"page":"2859-2869","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Supervised Fine-Tuning for Unsupervised KPI Anomaly Detection for Mobile Web Systems"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3179-3894","authenticated-orcid":false,"given":"Zhaoyang","family":"Yu","sequence":"first","affiliation":[{"name":"Tsinghua University & BNRist, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0330-0028","authenticated-orcid":false,"given":"Shenglin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Nankai University & HL-IT, Tianjin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4205-7182","authenticated-orcid":false,"given":"Mingze","family":"Sun","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8069-9424","authenticated-orcid":false,"given":"Yingke","family":"Li","sequence":"additional","affiliation":[{"name":"Nankai University, Tianjin, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0408-9137","authenticated-orcid":false,"given":"Yankai","family":"Zhao","sequence":"additional","affiliation":[{"name":"Nankai University, Tianjin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0251-5484","authenticated-orcid":false,"given":"Xiaolei","family":"Hua","sequence":"additional","affiliation":[{"name":"China Mobile Research Institute, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1167-1953","authenticated-orcid":false,"given":"Lin","family":"Zhu","sequence":"additional","affiliation":[{"name":"China Mobile Research Institute, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0527-947X","authenticated-orcid":false,"given":"Xidao","family":"Wen","sequence":"additional","affiliation":[{"name":"BizSeer Technology, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5113-838X","authenticated-orcid":false,"given":"Dan","family":"Pei","sequence":"additional","affiliation":[{"name":"Tsinghua University & BNRist, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2024,5,13]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"S\u00e9 bastien Marti, and Maria A. Zuluaga.","author":"Audibert Julien","year":"2020","unstructured":"Julien Audibert, Pietro Michiardi, Fr\u00e9 d\u00e9 ric Guyard, S\u00e9 bastien Marti, and Maria A. Zuluaga. 2020. USAD: UnSupervised Anomaly Detection on Multivariate Time Series. In KDD. ACM, 3395--3404."},{"key":"e_1_3_2_2_2_1","volume-title":"Gridmask data augmentation. arXiv preprint arXiv:2001.04086","author":"Chen Pengguang","year":"2020","unstructured":"Pengguang Chen, Shu Liu, Hengshuang Zhao, and Jiaya Jia. 2020. Gridmask data augmentation. arXiv preprint arXiv:2001.04086 (2020)."},{"key":"e_1_3_2_2_3_1","volume-title":"44th IEEE\/ACM 44th International Conference on Software Engineering, ICSE 2022","author":"Chen Zhuangbin","year":"2022","unstructured":"Zhuangbin Chen, Jinyang Liu, Yuxin Su, Hongyu Zhang, Xiao Ling, and Michael R. Lyu. 2022. Adaptive Performance Anomaly Detection for Online Service Systems via Pattern Sketching. In 44th IEEE\/ACM 44th International Conference on Software Engineering, ICSE 2022, Pittsburgh, PA, USA, May 25--27, 2022. IEEE, 61--72."},{"key":"e_1_3_2_2_4_1","first-page":"3","article-title":"STL: A seasonal-trend decomposition","volume":"6","author":"Cleveland Robert B","year":"1990","unstructured":"Robert B Cleveland, William S Cleveland, Jean E McRae, and Irma Terpenning. 1990. STL: A seasonal-trend decomposition. J. Off. Stat, Vol. 6, 1 (1990), 3--73.","journal-title":"J. Off. Stat"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1049\/ell2.12683"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219845"},{"key":"e_1_3_2_2_7_1","volume-title":"Yoon Kwon, Carlee Joe-Wong, Ted Taekyoung Kwon, and Sangtae Ha.","author":"Im Youngbin","year":"2017","unstructured":"Youngbin Im, Jinyoung Han, Ji Hoon Lee, Yoon Kwon, Carlee Joe-Wong, Ted Taekyoung Kwon, and Sangtae Ha. 2017. FLARE: Coordinated Rate Adaptation for HTTP Adaptive Streaming in Cellular Networks. In ICDCS. IEEE Computer Society, 298--307."},{"key":"e_1_3_2_2_8_1","volume-title":"20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)","author":"Khani Mehrdad","year":"2023","unstructured":"Mehrdad Khani, Ganesh Ananthanarayanan, Kevin Hsieh, Junchen Jiang, Ravi Netravali, Yuanchao Shu, Mohammad Alizadeh, and Victor Bahl. 2023. $$RECL$$: Responsive $$Resource-Efficient$$ Continuous Learning for Video Analytics. In 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23). 917--932."},{"key":"e_1_3_2_2_9_1","volume-title":"Parisa Rahimzadeh, Xiaoxi Zhang, Max Hollingsworth, Carlee Joe-Wong, Dirk Grunwald, and Sangtae Ha.","author":"Lee Jihoon","year":"2019","unstructured":"Jihoon Lee, Jinsung Lee, Youngbin Im, Sandesh Dhawaskar Sathyanarayana, Parisa Rahimzadeh, Xiaoxi Zhang, Max Hollingsworth, Carlee Joe-Wong, Dirk Grunwald, and Sangtae Ha. 2019. CASTLE over the Air: Distributed Scheduling for Cellular Data Transmissions. In MobiSys. ACM, 417--429."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30490-4_56"},{"key":"e_1_3_2_2_11_1","volume-title":"FluxEV: A Fast and Effective Unsupervised Framework for Time-Series Anomaly Detection. In WSDM '21, The Fourteenth ACM International Conference on Web Search and Data Mining","author":"Li Jia","year":"2021","unstructured":"Jia Li, Shimin Di, Yanyan Shen, and Lei Chen. 2021a. FluxEV: A Fast and Effective Unsupervised Framework for Time-Series Anomaly Detection. In WSDM '21, The Fourteenth ACM International Conference on Web Search and Data Mining, Virtual Event, Israel, March 8--12, 2021, Liane Lewin-Eytan, David Carmel, Elad Yom-Tov, Eugene Agichtein, and Evgeniy Gabrilovich (Eds.). ACM, 824--832."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2022.3191341"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467075"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"crossref","unstructured":"Ajay Mahimkar Zihui Ge Xuan Liu Yusef Shaqalle Yu Xiang Jennifer Yates Shomik Pathak and Rick Reichel. 2022. Aurora: conformity-based configuration recommendation to improve LTE\/5G service. In IMC. ACM 83--97.","DOI":"10.1145\/3517745.3561455"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"crossref","unstructured":"Ajay Mahimkar Zihui Ge Jennifer Yates Chris Hristov Vincent Cordaro Shane Smith Jing Xu and Mark Stockert. 2013a. Robust assessment of changes in cellular networks. In CoNEXT. ACM 175--186.","DOI":"10.1145\/2535372.2535382"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2535372.2535382"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"crossref","unstructured":"Ajay Mahimkar Ashiwan Sivakumar Zihui Ge Shomik Pathak and Karunasish Biswas. 2021. Auric: using data-driven recommendation to automatically generate cellular configuration. In SIGCOMM. ACM 807--820.","DOI":"10.1145\/3452296.3472906"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CySWater.2016.7469060"},{"key":"e_1_3_2_2_19_1","volume-title":"Guoqing Harry Xu, and Ravi Netravali","author":"Padmanabhan Arthi","year":"2023","unstructured":"Arthi Padmanabhan, Neil Agarwal, Anand P. Iyer, Ganesh Ananthanarayanan, Yuanchao Shu, Nikolaos Karianakis, Guoqing Harry Xu, and Ravi Netravali. 2023. Gemel: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edge. In NSDI. USENIX Association, 973--994."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICNP.2017.8117537"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"crossref","unstructured":"Hansheng Ren Bixiong Xu Yujing Wang Chao Yi Congrui Huang Xiaoyu Kou Tony Xing Mao Yang Jie Tong and Qi Zhang. 2019. Time-Series Anomaly Detection Service at Microsoft. In KDD. ACM 3009--3017.","DOI":"10.1145\/3292500.3330680"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"e_1_3_2_2_23_1","volume-title":"Learning structured output representation using deep conditional generative models. Advances in neural information processing systems","author":"Sohn Kihyuk","year":"2015","unstructured":"Kihyuk Sohn, Honglak Lee, and Xinchen Yan. 2015. Learning structured output representation using deep conditional generative models. Advances in neural information processing systems , Vol. 28 (2015)."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330672"},{"key":"e_1_3_2_2_25_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 , Vol. 9, 11 (2008).","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015409"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/DICTA.2016.7797091"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3185996"},{"key":"e_1_3_2_2_29_1","volume-title":"Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy. In International Conference on Learning Representations.","author":"Xu Jiehui","year":"2022","unstructured":"Jiehui Xu, Haixu Wu, Jianmin Wang, and Mingsheng Long. 2022. Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/2716281.2836106"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISSRE59848.2023.00063"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"crossref","unstructured":"Shenglin Zhang Dongwen Li Zhenyu Zhong Jun Zhu Minghan Liang Jiexi Luo Yongqian Sun Ya Su Sibo Xia Zhongyou Hu Yuzhi Zhang Dan Pei Jiyan Sun and Yinlong Liu. 2022a. Robust System Instance Clustering for Large-Scale Web Services. In WWW. ACM 1785--1796.","DOI":"10.1145\/3485447.3511983"},{"key":"e_1_3_2_2_33_1","volume-title":"Robust KPI Anomaly Detection for Large-Scale Software Services with Partial Labels","author":"Zhang Shenglin","unstructured":"Shenglin Zhang, Chenyu Zhao, Yicheng Sui, Ya Su, Yongqian Sun, Yuzhi Zhang, Dan Pei, and Yizhe Wang. 2021. Robust KPI Anomaly Detection for Large-Scale Software Services with Partial Labels. In ISSRE. IEEE, 103--114."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2022.3180785"},{"key":"e_1_3_2_2_35_1","volume-title":"USENIX ATC","author":"Zhang Xu","year":"2019","unstructured":"Xu Zhang, Qingwei Lin, Yong Xu, Si Qin, Hongyu Zhang, Bo Qiao, Yingnong Dang, Xinsheng Yang, Qian Cheng, Murali Chintalapati, Youjiang Wu, Ken Hsieh, Kaixin Sui, Xin Meng, Yaohai Xu, Wenchi Zhang, Furao Shen, and Dongmei Zhang. 2019. Cross-dataset Time Series Anomaly Detection for Cloud Systems. In 2019 USENIX Annual Technical Conference, USENIX ATC 2019, Renton, WA, USA, July 10--12, 2019, , Dahlia Malkhi and Dan Tsafrir (Eds.). USENIX Association, 1063--1076."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.7000"}],"event":{"name":"WWW '24: The ACM Web Conference 2024","location":"Singapore Singapore","acronym":"WWW '24","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2024"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3589334.3645392","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3589334.3645392","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:29:24Z","timestamp":1755822564000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3589334.3645392"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,13]]},"references-count":36,"alternative-id":["10.1145\/3589334.3645392","10.1145\/3589334"],"URL":"https:\/\/doi.org\/10.1145\/3589334.3645392","relation":{},"subject":[],"published":{"date-parts":[[2024,5,13]]},"assertion":[{"value":"2024-05-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}