{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:06:25Z","timestamp":1775912785073,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":26,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T00:00:00Z","timestamp":1660435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Key-Area Research and Development Program of Guangdong Province China","award":["2020B010164003"],"award-info":[{"award-number":["2020B010164003"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,8,14]]},"DOI":"10.1145\/3534678.3539106","type":"proceedings-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:06:41Z","timestamp":1660331201000},"page":"3081-3089","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":26,"title":["Augmenting Log-based Anomaly Detection Models to Reduce False Anomalies with Human Feedback"],"prefix":"10.1145","author":[{"given":"Tong","family":"Jia","sequence":"first","affiliation":[{"name":"Peking University &amp; Advanced Institute of Big Data, Beijing, Beijing, China"}]},{"given":"Ying","family":"Li","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}]},{"given":"Yong","family":"Yang","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}]},{"given":"Gang","family":"Huang","sequence":"additional","affiliation":[{"name":"Peking University &amp; Advanced Institute of Big Data, Beijing, Beijing, China"}]},{"given":"Zhonghai","family":"Wu","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1572272.1572300"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICAC.2004.1301345"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0102"},{"key":"e_1_3_2_2_4_1","volume-title":"Incorporating feedback into tree-based anomaly detection. arXiv preprint arXiv:1708.09441","author":"Das Shubhomoy","year":"2017","unstructured":"Shubhomoy Das, Weng-Keen Wong, Alan Fern, Thomas G Dietterich, and Md Amran Siddiqui. 2017. Incorporating feedback into tree-based anomaly detection. arXiv preprint arXiv:1708.09441 (2017)."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134015"},{"key":"e_1_3_2_2_6_1","volume-title":"Retrieved","author":"Elliot S.","year":"2014","unstructured":"S. Elliot. 2014. DevOps and the cost of downtime: Fortune 1000 best practice metrics quantified. Retrieved December, 2014 from https:\/\/blogs.idc.com"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2012.06.025"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-24797-2"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICWS.2017.12"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISSRE52982.2021.00021"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CLOUD.2017.64"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377813.3381371"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2007.46"},{"key":"e_1_3_2_2_14_1","first-page":"4739","article-title":"LogAnomaly: Unsupervised detection of sequential and quantitative anomalies in unstructured logs","volume":"19","author":"Meng Weibin","year":"2019","unstructured":"Weibin Meng, Ying Liu, Yichen Zhu, Shenglin Zhang, Dan Pei, Yuqing Liu, Yihao Chen, Ruizhi Zhang, Shimin Tao, Pei Sun, et al. 2019. LogAnomaly: Unsupervised detection of sequential and quantitative anomalies in unstructured logs.. In IJCAI, Vol. 19. 4739--4745.","journal-title":"IJCAI"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939712"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-015-5521-0"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/NOMS.2010.5488459"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220083"},{"key":"e_1_3_2_2_19_1","volume-title":"LOGAN: Problem Diagnosis in the Cloud Using Log-Based Reference Models. In 2016 IEEE International Conference on Cloud Engineering (IC2E). 62--67","author":"Tak Byung Chul","year":"2016","unstructured":"Byung Chul Tak, Shu Tao, Lin Yang, Chao Zhu, and Yaoping Ruan. 2016. LOGAN: Problem Diagnosis in the Cloud Using Log-Based Reference Models. In 2016 IEEE International Conference on Cloud Engineering (IC2E). 62--67. https:\/\/doi.org\/10. 1109\/IC2E.2016.12"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CLOUD.2016.0047"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/QRS.2019.00033"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICEBE.2017.52"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2523649.2523670"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSME46990.2020.00069"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/2980024.2872407"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3338906.3338931"}],"event":{"name":"KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Washington DC USA","acronym":"KDD '22","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539106","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539106","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:58Z","timestamp":1750186978000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539106"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":26,"alternative-id":["10.1145\/3534678.3539106","10.1145\/3534678"],"URL":"https:\/\/doi.org\/10.1145\/3534678.3539106","relation":{},"subject":[],"published":{"date-parts":[[2022,8,14]]},"assertion":[{"value":"2022-08-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}