{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T19:32:30Z","timestamp":1776108750541,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":63,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T00:00:00Z","timestamp":1717027200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Wolfgang Gentner Programme of the German Federal Ministry of Education and Research","award":["13E18CHA"],"award-info":[{"award-number":["13E18CHA"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,5,30]]},"DOI":"10.1145\/3650200.3656637","type":"proceedings-article","created":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T14:11:54Z","timestamp":1717423914000},"page":"272-285","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["DeepHYDRA: A Hybrid Deep Learning and DBSCAN-Based Approach to Time-Series Anomaly Detection in Dynamically-Configured Systems"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0426-5837","authenticated-orcid":false,"given":"Franz Kevin","family":"Stehle","sequence":"first","affiliation":[{"name":"Computing Systems Group, Institute of Computer Engineering, Heidelberg University, Germany and ATLAS EP-ADT-DQ, CERN, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6581-9410","authenticated-orcid":false,"given":"Wainer","family":"Vandelli","sequence":"additional","affiliation":[{"name":"ATLAS EP-ADT-DQ, CERN, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3796-3620","authenticated-orcid":false,"given":"Felix","family":"Zahn","sequence":"additional","affiliation":[{"name":"ATLAS EP-ADT-DQ, CERN, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2664-3437","authenticated-orcid":false,"given":"Giuseppe","family":"Avolio","sequence":"additional","affiliation":[{"name":"ATLAS EP-ADT-DQ, CERN, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9562-0680","authenticated-orcid":false,"given":"Holger","family":"Fr\u00f6ning","sequence":"additional","affiliation":[{"name":"Computing Systems Group, Institute of Computer Engineering, Heidelberg University, Germany"}]}],"member":"320","published-online":{"date-parts":[[2024,6,3]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"[n. d.]. Powering CERN. https:\/\/home.cern\/science\/engineering\/powering-cern"},{"key":"e_1_3_2_1_2_1","unstructured":"Facebook Artificial Intelligence Research 2023. fvcore. Facebook Artificial Intelligence Research. https:\/\/github.com\/facebookresearch\/fvcore\/"},{"key":"e_1_3_2_1_3_1","unstructured":"Regionales Rechenzentrum Erlangen RRZE Friedrich-Alexander-Universit\u00e4t 2024. pylikwid. Regionales Rechenzentrum Erlangen RRZE Friedrich-Alexander-Universit\u00e4t. https:\/\/github.com\/RRZE-HPC\/pylikwid"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.2172\/1819812"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","unstructured":"Burak Aksar Efe Sencan Benjamin Schwaller Omar Aaziz Vitus Leung Jim Brandt Brian Kulis Manuel Egele and Ayse Coskun. 2023. Dataset Artifact for Prodigy: Towards Unsupervised Anomaly Detection in Production HPC Systems. https:\/\/doi.org\/10.5281\/zenodo.8079388","DOI":"10.5281\/zenodo.8079388"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3581784.3607076"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-023-00760-1"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3337821.3337907"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1088\/1748-0221\/11\/06\/P06008"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403392"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.3390\/s18020581"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/AICAS.2019.8771527"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12864-019-6413-7"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13040-023-00322-4"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.03.067"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.3390\/s20072028"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.5120\/1977-2651"},{"key":"e_1_3_2_1_18_1","volume-title":"TiSAT: Time Series Anomaly Transformer. arXiv preprint arXiv:2203.05167","author":"Doshi Keval","year":"2022","unstructured":"Keval Doshi, Shatha Abudalou, and Yasin Yilmaz. 2022. TiSAT: Time Series Anomaly Transformer. arXiv preprint arXiv:2203.05167 (2022)."},{"key":"e_1_3_2_1_19_1","unstructured":"Martin Ester Hans-Peter Kriegel J\u00f6rg Sander Xiaowei Xu 1996. A density-based algorithm for discovering clusters in large spatial databases with noise.. In kdd Vol.\u00a096. 226\u2013231."},{"key":"e_1_3_2_1_20_1","volume-title":"Robusttad: Robust time series anomaly detection via decomposition and convolutional neural networks. arXiv preprint arXiv:2002.09545","author":"Gao Jingkun","year":"2020","unstructured":"Jingkun Gao, Xiaomin Song, Qingsong Wen, Pichao Wang, Liang Sun, and Huan Xu. 2020. Robusttad: Robust time series anomaly detection via decomposition and convolutional neural networks. arXiv preprint arXiv:2002.09545 (2020)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1504\/IJNVO.2019.097631"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-31865-1_25"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-04363-9"},{"key":"e_1_3_2_1_24_1","volume-title":"Data clustering: 50 years beyond K-means. Pattern recognition letters 31, 8","author":"Jain K","year":"2010","unstructured":"Anil\u00a0K Jain. 2010. Data clustering: 50 years beyond K-means. Pattern recognition letters 31, 8 (2010), 651\u2013666."},{"key":"e_1_3_2_1_25_1","volume-title":"Springer series in statistics. Principal component analysis 29","author":"Jolliffe T","year":"2002","unstructured":"Ian\u00a0T Jolliffe. 2002. Springer series in statistics. Principal component analysis 29 (2002), 912."},{"key":"e_1_3_2_1_26_1","first-page":"20150202","article-title":"Principal component analysis: a review and recent developments. Philosophical transactions of the royal society A","volume":"374","author":"Jolliffe T","year":"2016","unstructured":"Ian\u00a0T Jolliffe and Jorge Cadima. 2016. Principal component analysis: a review and recent developments. Philosophical transactions of the royal society A: Mathematical, Physical and Engineering Sciences 374, 2065 (2016), 20150202.","journal-title":"Mathematical, Physical and Engineering Sciences"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-62701-4_10"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/1497577.1497578"},{"key":"e_1_3_2_1_29_1","volume-title":"Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1).","author":"Lai Kwei-Herng","year":"2021","unstructured":"Kwei-Herng Lai, Daochen Zha, Junjie Xu, Yue Zhao, Guanchu Wang, and Xia Hu. 2021. Revisiting time series outlier detection: Definitions and benchmarks. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3078553"},{"key":"e_1_3_2_1_31_1","unstructured":"Liam Li Kevin Jamieson Afshin Rostamizadeh Ekaterina Gonina Moritz Hardt Benjamin Recht and Ameet Talwalkar. 2020. A System for Massively Parallel Hyperparameter Tuning. arxiv:1810.05934\u00a0[cs.LG]"},{"key":"e_1_3_2_1_32_1","volume-title":"Tune: A Research Platform for Distributed Model Selection and Training. arXiv preprint arXiv:1807.05118","author":"Liaw Richard","year":"2018","unstructured":"Richard Liaw, Eric Liang, Robert Nishihara, Philipp Moritz, Joseph\u00a0E Gonzalez, and Ion Stoica. 2018. Tune: A Research Platform for Distributed Model Selection and Training. arXiv preprint arXiv:1807.05118 (2018)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","unstructured":"Nguyen\u00a0Van Loi Tran Trung\u00a0Kien Tran\u00a0Vu Hop Le Thanh\u00a0Son and Nguyen\u00a0Van Khuong. 2020. Abnormal Moving Speed Detection Using Combination of Kernel Density Estimator and DBSCAN for Coastal Surveillance Radars. In 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN). 143\u2013147. https:\/\/doi.org\/10.1109\/SPIN48934.2020.9070885","DOI":"10.1109\/SPIN48934.2020.9070885"},{"key":"e_1_3_2_1_34_1","unstructured":"Leland McInnes John Healy and Steve Astels. [n. d.]. hdbscan: Hierarchical density based clustering. ([n. d.])."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2022.12.001"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00357-019-09345-1"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00147"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3388440.3412467"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2017.8258103"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.3390\/s18072110"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1002\/itl2.235"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.5555\/1953048.2078195"},{"key":"e_1_3_2_1_43_1","volume-title":"Evaluation: From Precision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation. Mach. Learn. Technol. 2 (01","author":"Powers David","year":"2008","unstructured":"David Powers. 2008. Evaluation: From Precision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation. Mach. Learn. Technol. 2 (01 2008)."},{"key":"e_1_3_2_1_44_1","volume-title":"Anomaly Detection in RFID Networks Using Bayesian Blocks and DBSCAN. In 2020 SoutheastCon","author":"Prodanoff Zornitza\u00a0Genova","unstructured":"Zornitza\u00a0Genova Prodanoff, Andrew Penkunas, and Patrick Kreidl. 2020. Anomaly Detection in RFID Networks Using Bayesian Blocks and DBSCAN. In 2020 SoutheastCon. IEEE, 1\u20137."},{"key":"e_1_3_2_1_45_1","unstructured":"Ferdinand Rewicki. 2022. py-merlin. https:\/\/gitlab.com\/dlr-dw\/py-merlin\/-\/tree\/v1.0.1"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.3390\/app13031778"},{"key":"e_1_3_2_1_47_1","volume-title":"Deep semi-supervised anomaly detection. arXiv preprint arXiv:1906.02694","author":"Ruff Lukas","year":"2019","unstructured":"Lukas Ruff, Robert\u00a0A Vandermeulen, Nico G\u00f6rnitz, Alexander Binder, Emmanuel M\u00fcller, Klaus-Robert M\u00fcller, and Marius Kloft. 2019. Deep semi-supervised anomaly detection. arXiv preprint arXiv:1906.02694 (2019)."},{"key":"e_1_3_2_1_48_1","unstructured":"Villu Ruusmann. 2024. SkLearn2PMML. https:\/\/github.com\/jpmml\/sklearn2pmml"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/2689746.2689747"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2019.12.111"},{"key":"e_1_3_2_1_51_1","volume-title":"A large-scale study of failures in high-performance computing systems","author":"Schroeder Bianca","year":"2009","unstructured":"Bianca Schroeder and Garth\u00a0A Gibson. 2009. A large-scale study of failures in high-performance computing systems. IEEE transactions on Dependable and Secure Computing 7, 4 (2009), 337\u2013350."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098144"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330672"},{"key":"e_1_3_2_1_55_1","volume-title":"Proceedings of PSTI2010","author":"Treibig J.","unstructured":"J. Treibig, G. Hager, and G. Wellein. 2010. LIKWID: A lightweight performance-oriented tool suite for x86 multicore environments. In Proceedings of PSTI2010, the First International Workshop on Parallel Software Tools and Tool Infrastructures. San Diego CA."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.14778\/3514061.3514067"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pisc.2016.05.010"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.5194\/npg-13-321-2006"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/631"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3112126"},{"key":"e_1_3_2_1_61_1","unstructured":"Tyler Yep. 2020. torchinfo. https:\/\/github.com\/TylerYep\/torchinfo"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"e_1_3_2_1_63_1","volume-title":"International Conference on Learning Representations.","author":"Zong Bo","year":"2018","unstructured":"Bo Zong, Qi Song, Martin\u00a0Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen. 2018. Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In International Conference on Learning Representations."}],"event":{"name":"ICS '24: 2024 International Conference on Supercomputing","location":"Kyoto Japan","acronym":"ICS '24","sponsor":["SIGARCH ACM Special Interest Group on Computer Architecture"]},"container-title":["Proceedings of the 38th ACM International Conference on Supercomputing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3650200.3656637","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3650200.3656637","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T15:24:36Z","timestamp":1755876276000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3650200.3656637"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,30]]},"references-count":63,"alternative-id":["10.1145\/3650200.3656637","10.1145\/3650200"],"URL":"https:\/\/doi.org\/10.1145\/3650200.3656637","relation":{},"subject":[],"published":{"date-parts":[[2024,5,30]]},"assertion":[{"value":"2024-06-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}