{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T19:10:51Z","timestamp":1732043451984,"version":"3.28.0"},"reference-count":85,"publisher":"Association for Computing Machinery (ACM)","issue":"6","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2024,2]]},"abstract":"<jats:p>How to get insights from relational data streams in a timely manner is a hot research topic. Data streams can present unique challenges, such as distribution drifts, outliers, emerging classes, and changing features, which have recently been described as open environment challenges for machine learning. While existing studies have been done on incremental learning for data streams, their evaluations are mostly conducted with synthetic datasets. Thus, a natural question is how those open environment challenges look like and how existing incremental learning algorithms perform on real-world relational data streams. To fill this gap, we develop an Open Environment Benchmark named OEBench to evaluate open environment challenges in real-world relational data streams. Specifically, we investigate 55 real-world relational data streams and establish that open environment scenarios are indeed widespread, which presents significant challenges for stream learning algorithms. Through benchmarks with existing incremental learning algorithms, we find that increased data quantity may not consistently enhance the model accuracy when applied in open environment scenarios, where machine learning models can be significantly compromised by missing values, distribution drifts, or anomalies in real-world data streams. The current techniques are insufficient in effectively mitigating these challenges brought by open environments. More researches are needed to address real-world open environment challenges. All datasets and code are open-sourced in https:\/\/github.com\/Xtra-Computing\/OEBench.<\/jats:p>","DOI":"10.14778\/3648160.3648170","type":"journal-article","created":{"date-parts":[[2024,5,3]],"date-time":"2024-05-03T21:52:53Z","timestamp":1714773173000},"page":"1283-1296","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streams"],"prefix":"10.14778","volume":"17","author":[{"given":"Yiqun","family":"Diao","sequence":"first","affiliation":[{"name":"National University of Singapore"}]},{"given":"Yutong","family":"Yang","sequence":"additional","affiliation":[{"name":"National University of Singapore"}]},{"given":"Qinbin","family":"Li","sequence":"additional","affiliation":[{"name":"University of California, Berkeley"}]},{"given":"Bingsheng","family":"He","sequence":"additional","affiliation":[{"name":"National University of Singapore"}]},{"given":"Mian","family":"Lu","sequence":"additional","affiliation":[{"name":"4Paradigm Inc."}]}],"member":"320","published-online":{"date-parts":[[2024,5,3]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3076264"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_9"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.753"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112832"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i8.16826"},{"key":"e_1_2_1_6_1","volume-title":"Fourth international workshop on knowledge discovery from data streams","volume":"6","author":"Baena-Garc\u0131a Manuel","year":"2006","unstructured":"Manuel Baena-Garc\u0131a, Jos\u00e9 del Campo-\u00c1vila, Ra\u00fal Fidalgo, Albert Bifet, R Gavalda, and Rafael Morales-Bueno. 2006. Early drift detection method. In Fourth international workshop on knowledge discovery from data streams, Vol. 6. 77--86."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.5555\/1368018.1368022"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972771.42"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557041"},{"key":"e_1_2_1_10_1","volume-title":"Proceedings of the first workshop on applications of pattern analysis. PMLR, 44--50","author":"Bifet Albert","year":"2010","unstructured":"Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Philipp Kranen, Hardy Kremer, Timm Jansen, and Thomas Seidl. 2010. Moa: Massive online analysis, a framework for stream classification and clustering. In Proceedings of the first workshop on applications of pattern analysis. PMLR, 44--50."},{"key":"e_1_2_1_11_1","volume-title":"International conference on hybrid artificial intelligence systems. Springer, 155--163","author":"Brzezi\u0144ski Dariusz","year":"2011","unstructured":"Dariusz Brzezi\u0144ski and Jerzy Stefanowski. 2011. Accuracy updated ensemble for data streams with concept drift. In International conference on hybrid artificial intelligence systems. Springer, 155--163."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457321"},{"key":"e_1_2_1_13_1","volume-title":"Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping. Advances in neural information processing systems 13","author":"Caruana Rich","year":"2000","unstructured":"Rich Caruana, Steve Lawrence, and C Giles. 2000. Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping. Advances in neural information processing systems 13 (2000)."},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01252-6_33"},{"key":"e_1_2_1_15_1","volume-title":"Proc. Symposium on the Interface of Statistics, Computing Science, and Applications (Interface).","author":"Dasu Tamraparni","year":"2006","unstructured":"Tamraparni Dasu, Shankar Krishnan, Suresh Venkatasubramanian, and Ke Yi. 2006. An information-theoretic approach to detecting changes in multidimensional data streams. In Proc. Symposium on the Interface of Statistics, Computing Science, and Applications (Interface)."},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00528"},{"key":"e_1_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Yiqun Diao Yutong Yang Qinbin Li Bingsheng He and Mian Lu. 2023. OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streams. arXiv:2308.15059","DOI":"10.14778\/3648160.3648170"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CIDUE.2011.5948491"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-021-00516-9"},{"key":"e_1_2_1_20_1","volume-title":"Towards robust evaluations of continual learning. arXiv preprint arXiv:1805.09733","author":"Farquhar Sebastian","year":"2018","unstructured":"Sebastian Farquhar and Yarin Gal. 2018. Towards robust evaluations of continual learning. arXiv preprint arXiv:1805.09733 (2018)."},{"key":"e_1_2_1_21_1","volume-title":"Twenty-Second International Joint Conference on Artificial Intelligence.","author":"Gama Joao","year":"2011","unstructured":"Joao Gama and Petr Kosina. 2011. Learning decision rules from data streams. In Twenty-Second International Joint Conference on Artificial Intelligence."},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-28645-5_29"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/956750.956813"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557060"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-017-5642-8"},{"key":"e_1_2_1_26_1","volume-title":"Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572","author":"Goodfellow Ian J","year":"2014","unstructured":"Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)."},{"key":"e_1_2_1_27_1","volume-title":"San Mateo","author":"Gray Jim","year":"1993","unstructured":"Jim Gray. 1993. The benchmark handbook for database and transasction systems. Mergan Kaufmann, San Mateo (1993)."},{"key":"e_1_2_1_28_1","volume-title":"International conference on machine learning. PMLR, 2712--2721","author":"Guha Sudipto","year":"2016","unstructured":"Sudipto Guha, Nina Mishra, Gourav Roy, and Okke Schrijvers. 2016. Robust random cut forest based anomaly detection on streams. In International conference on machine learning. PMLR, 2712--2721."},{"key":"e_1_2_1_29_1","unstructured":"Songqiao Han Xiyang Hu Hailiang Huang Mingqi Jiang and Yue Zhao. 2022. ADBench: Anomaly Detection Benchmark. In Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_2_1_30_1","volume-title":"International conference on machine learning. PMLR, 1009--1017","author":"Harel Maayan","year":"2014","unstructured":"Maayan Harel, Shie Mannor, Ran El-Yaniv, and Koby Crammer. 2014. Concept drift detection through resampling. In International conference on machine learning. PMLR, 1009--1017."},{"key":"e_1_2_1_31_1","volume-title":"2019 International Conference on Robotics and Automation (ICRA). IEEE, 9769--9776","author":"Hayes Tyler L","year":"2019","unstructured":"Tyler L Hayes, Nathan D Cahill, and Christopher Kanan. 2019. Memory efficient experience replay for streaming learning. In 2019 International Conference on Robotics and Automation (ICRA). IEEE, 9769--9776."},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13748-011-0008-0"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00092"},{"key":"e_1_2_1_34_1","volume-title":"John Lei, Lynn@Vesta, Marcus2010, and Hussein Abbass.","author":"Howard Addison","year":"2019","unstructured":"Addison Howard, Bernadette Bouchon-Meunier, IEEE CIS, inversion, John Lei, Lynn@Vesta, Marcus2010, and Hussein Abbass. 2019. IEEE-CIS Fraud Detection. https:\/\/kaggle.com\/competitions\/ieee-fraud-detection"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00395"},{"key":"e_1_2_1_36_1","volume-title":"The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset. ICT express 6, 4","author":"Kandel Ibrahem","year":"2020","unstructured":"Ibrahem Kandel and Mauro Castelli. 2020. The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset. ICT express 6, 4 (2020), 312--315."},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1611835114"},{"key":"e_1_2_1_38_1","volume-title":"2018 IEEE International Conference on Big Data (Big Data). IEEE, 2239--2244","author":"Krawczyk Bartosz","year":"2018","unstructured":"Bartosz Krawczyk, Bernhard Pfahringer, and Micha\u0142 Wo\u017aniak. 2018. Combining active learning with concept drift detection for data stream mining. In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2239--2244."},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.10.120"},{"key":"e_1_2_1_40_1","volume-title":"Adversarial machine learning at scale. arXiv preprint arXiv:1611.01236","author":"Kurakin Alexey","year":"2016","unstructured":"Alexey Kurakin, Ian Goodfellow, and Samy Bengio. 2016. Adversarial machine learning at scale. arXiv preprint arXiv:1611.01236 (2016)."},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.JBO.20.12.121305"},{"key":"e_1_2_1_42_1","volume-title":"Learning without forgetting","author":"Li Zhizhong","year":"2017","unstructured":"Zhizhong Li and Derek Hoiem. 2017. Learning without forgetting. IEEE transactions on pattern analysis and machine intelligence 40, 12 (2017), 2935--2947."},{"key":"e_1_2_1_43_1","volume-title":"Ecod: Unsupervised outlier detection using empirical cumulative distribution functions","author":"Li Zheng","year":"2022","unstructured":"Zheng Li, Yue Zhao, Xiyang Hu, Nicola Botta, Cezar Ionescu, and George Chen. 2022. Ecod: Unsupervised outlier detection using empirical cumulative distribution functions. IEEE Transactions on Knowledge and Data Engineering (2022)."},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12530-012-9061-6"},{"key":"e_1_2_1_45_1","volume-title":"Kai Ming Ting, and Zhi-Hua Zhou","author":"Liu Fei Tony","year":"2008","unstructured":"Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. 2008. Isolation forest. In 2008 eighth ieee international conference on data mining. IEEE, 413--422."},{"key":"e_1_2_1_46_1","volume-title":"Conference on Robot Learning. PMLR, 17--26","author":"Lomonaco Vincenzo","year":"2017","unstructured":"Vincenzo Lomonaco and Davide Maltoni. 2017. Core50: a new dataset and benchmark for continuous object recognition. In Conference on Robot Learning. PMLR, 17--26."},{"key":"e_1_2_1_47_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 3600--3610","author":"Lomonaco Vincenzo","year":"2021","unstructured":"Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu, Antonio Carta, Gabriele Graffieti, Tyler L Hayes, Matthias De Lange, Marc Masana, Jary Pomponi, Gido M Van de Ven, et al. 2021. Avalanche: an end-to-end library for continual learning. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 3600--3610."},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105227"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.10.021"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00810"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220107"},{"key":"e_1_2_1_52_1","volume-title":"Class-incremental learning: survey and performance evaluation on image classification","author":"Masana Marc","year":"2022","unstructured":"Marc Masana, Xialei Liu, Bart\u0142omiej Twardowski, Mikel Menta, Andrew D Bagdanov, and Joost van de Weijer. 2022. Class-incremental learning: survey and performance evaluation on image classification. IEEE Transactions on Pattern Analysis and Machine Intelligence (2022)."},{"volume-title":"Data augmentation for improving deep learning in image classification problem. In 2018 international interdisciplinary PhD workshop (IIPhDW)","author":"Miko\u0142ajczyk Agnieszka","key":"e_1_2_1_53_1","unstructured":"Agnieszka Miko\u0142ajczyk and Micha\u0142 Grochowski. 2018. Data augmentation for improving deep learning in image classification problem. In 2018 international interdisciplinary PhD workshop (IIPhDW). IEEE, 117--122."},{"key":"e_1_2_1_54_1","volume-title":"KirillOdintsov, and Martin Kotek.","author":"Montoya Anna","year":"2018","unstructured":"Anna Montoya, inversion, KirillOdintsov, and Martin Kotek. 2018. Home Credit Default Risk. https:\/\/kaggle.com\/competitions\/home-credit-default-risk"},{"key":"e_1_2_1_55_1","volume-title":"Modern hierarchical, agglomerative clustering algorithms. arXiv preprint arXiv:1109.2378","author":"M\u00fcllner Daniel","year":"2011","unstructured":"Daniel M\u00fcllner. 2011. Modern hierarchical, agglomerative clustering algorithms. arXiv preprint arXiv:1109.2378 (2011)."},{"key":"e_1_2_1_56_1","doi-asserted-by":"crossref","first-page":"662","DOI":"10.3390\/math8050662","article-title":"Improving the accuracy of convolutional neural networks by identifying and removing outlier images in datasets using t-SNE","volume":"8","author":"Perez Husein","year":"2020","unstructured":"Husein Perez and Joseph HM Tah. 2020. Improving the accuracy of convolutional neural networks by identifying and removing outlier images in datasets using t-SNE. Mathematics 8, 5 (2020), 662.","journal-title":"Mathematics"},{"volume-title":"McDiarmid drift detection methods for evolving data streams. In 2018 International joint conference on neural networks (IJCNN)","author":"Pesaranghader Ali","key":"e_1_2_1_57_1","unstructured":"Ali Pesaranghader, Herna L Viktor, and Eric Paquet. 2018. McDiarmid drift detection methods for evolving data streams. In 2018 International joint conference on neural networks (IJCNN). IEEE, 1--9."},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-015-5521-0"},{"key":"e_1_2_1_59_1","volume-title":"2019 5th International Conference on Science in Information Technology (ICSITech). IEEE, 83--88","author":"Pujianto Utomo","year":"2019","unstructured":"Utomo Pujianto, Aji Prasetya Wibawa, Muhammad Iqbal Akbar, et al. 2019. K-nearest neighbor (k-NN) based missing data imputation. In 2019 5th International Conference on Science in Information Technology (ICSITech). IEEE, 83--88."},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783359"},{"key":"e_1_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.587"},{"key":"e_1_2_1_62_1","doi-asserted-by":"crossref","unstructured":"Douglas A Reynolds et al. 2009. Gaussian mixture models. Encyclopedia of biometrics 741 659-663 (2009).","DOI":"10.1007\/978-0-387-73003-5_196"},{"key":"e_1_2_1_63_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops. 228--229","author":"Roady Ryne","year":"2020","unstructured":"Ryne Roady, Tyler L Hayes, Hitesh Vaidya, and Christopher Kanan. 2020. Stream-51: Streaming classification and novelty detection from videos. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops. 228--229."},{"key":"e_1_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0057"},{"key":"e_1_2_1_65_1","volume-title":"2020 IEEE international conference on robotics and automation (ICRA). IEEE, 4767--4773","author":"She Qi","year":"2020","unstructured":"Qi She, Fan Feng, Xinyue Hao, Qihan Yang, Chuanlin Lan, Vincenzo Lomonaco, Xuesong Shi, Zhengwei Wang, Yao Guo, Yimin Zhang, et al. 2020. OpenLORIS-Object: A robotic vision dataset and benchmark for lifelong deep learning. In 2020 IEEE international conference on robotics and automation (ICRA). IEEE, 4767--4773."},{"key":"e_1_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2021.11.011"},{"key":"e_1_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00924"},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-020-00698-5"},{"key":"e_1_2_1_69_1","volume-title":"Effects of sample size on accuracy of species distribution models. Ecological modelling 148, 1","author":"Stockwell David RB","year":"2002","unstructured":"David RB Stockwell and A Townsend Peterson. 2002. Effects of sample size on accuracy of species distribution models. Ecological modelling 148, 1 (2002), 1--13."},{"key":"e_1_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1145\/502512.502568"},{"key":"e_1_2_1_71_1","volume-title":"Kai Ming Ting, and Tony Fei Liu","author":"Tan Swee Chuan","year":"2011","unstructured":"Swee Chuan Tan, Kai Ming Ting, and Tony Fei Liu. 2011. Fast anomaly detection for streaming data. In Twenty-second international joint conference on artificial intelligence. Citeseer."},{"key":"e_1_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01623"},{"key":"e_1_2_1_73_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 9, 11 (2008).","journal-title":"Journal of machine learning research"},{"key":"e_1_2_1_74_1","volume-title":"A tutorial on spectral clustering. Statistics and computing 17","author":"Luxburg Ulrike Von","year":"2007","unstructured":"Ulrike Von Luxburg. 2007. A tutorial on spectral clustering. Statistics and computing 17 (2007), 395--416."},{"volume-title":"Concept drift detection for streaming data. In 2015 international joint conference on neural networks (IJCNN)","author":"Wang Heng","key":"e_1_2_1_75_1","unstructured":"Heng Wang and Zubin Abraham. 2015. Concept drift detection for streaming data. In 2015 international joint conference on neural networks (IJCNN). IEEE, 1--9."},{"key":"e_1_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2022.07.065"},{"key":"e_1_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00116"},{"key":"e_1_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00046"},{"key":"e_1_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00303"},{"key":"e_1_2_1_80_1","volume-title":"Mining in anticipation for concept change: Proactive-reactive prediction in data streams. Data mining and knowledge discovery 13","author":"Yang Ying","year":"2006","unstructured":"Ying Yang, Xindong Wu, and Xingquan Zhu. 2006. Mining in anticipation for concept change: Proactive-reactive prediction in data streams. Data mining and knowledge discovery 13 (2006), 261--289."},{"key":"e_1_2_1_81_1","volume-title":"International Conference on Machine Learning. PMLR, 3987--3995","author":"Zenke Friedemann","year":"2017","unstructured":"Friedemann Zenke, Ben Poole, and Surya Ganguli. 2017. Continual learning through synaptic intelligence. In International Conference on Machine Learning. PMLR, 3987--3995."},{"key":"e_1_2_1_82_1","volume-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. 1131--1140","author":"Zhang Junting","year":"2020","unstructured":"Junting Zhang, Jie Zhang, Shalini Ghosh, Dawei Li, Serafettin Tasci, Larry Heck, Heming Zhang, and C-C Jay Kuo. 2020. Class-incremental learning via deep model consolidation. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. 1131--1140."},{"key":"e_1_2_1_83_1","volume-title":"A model or 603 exemplars: Towards memory-efficient class-incremental learning. arXiv preprint arXiv:2205.13218","author":"Zhou Da-Wei","year":"2022","unstructured":"Da-Wei Zhou, Qi-Wei Wang, Han-Jia Ye, and De-Chuan Zhan. 2022. A model or 603 exemplars: Towards memory-efficient class-incremental learning. arXiv preprint arXiv:2205.13218 (2022)."},{"key":"e_1_2_1_84_1","doi-asserted-by":"publisher","DOI":"10.1093\/nsr\/nwac123"},{"key":"e_1_2_1_85_1","volume-title":"Beng Chin Ooi, and Wenqiao Zhang","author":"Zhu Jiaqi","year":"2023","unstructured":"Jiaqi Zhu, Shaofeng Cai, Fang Deng, Beng Chin Ooi, and Wenqiao Zhang. 2023. METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection. arXiv preprint arXiv:2312.16831 (2023)."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3648160.3648170","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T01:01:40Z","timestamp":1731891700000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3648160.3648170"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2]]},"references-count":85,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["10.14778\/3648160.3648170"],"URL":"https:\/\/doi.org\/10.14778\/3648160.3648170","relation":{},"ISSN":["2150-8097"],"issn-type":[{"type":"print","value":"2150-8097"}],"subject":[],"published":{"date-parts":[[2024,2]]},"assertion":[{"value":"2024-05-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}