{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:09:31Z","timestamp":1778080171022,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":72,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T00:00:00Z","timestamp":1654819200000},"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":[[2022,6,10]]},"DOI":"10.1145\/3514221.3526150","type":"proceedings-article","created":{"date-parts":[[2022,6,12]],"date-time":"2022-06-12T02:33:49Z","timestamp":1655001229000},"page":"1286-1300","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["In-Database Machine Learning with CorgiPile: Stochastic Gradient Descent without Full Data Shuffle"],"prefix":"10.1145","author":[{"given":"Lijie","family":"Xu","sequence":"first","affiliation":[{"name":"ETH Z\u00fcrich & Institute of Software, Chinese Academy of Sciences, Z\u00fcrich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuang","family":"Qiu","sequence":"additional","affiliation":[{"name":"University of Chicago, Chicago, IL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Binhang","family":"Yuan","sequence":"additional","affiliation":[{"name":"ETH Z\u00fcrich, Z\u00fcrich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiawei","family":"Jiang","sequence":"additional","affiliation":[{"name":"ETH Z\u00fcrich, Z\u00fcrich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cedric","family":"Renggli","sequence":"additional","affiliation":[{"name":"ETH Z\u00fcrich, Z\u00fcrich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaoduo","family":"Gan","sequence":"additional","affiliation":[{"name":"ETH Z\u00fcrich, Z\u00fcrich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaan","family":"Kara","sequence":"additional","affiliation":[{"name":"ETH Z\u00fcrich, Z\u00fcrich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoliang","family":"Li","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ji","family":"Liu","sequence":"additional","affiliation":[{"name":"Kwai Inc., Seattle, WA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wentao","family":"Wu","sequence":"additional","affiliation":[{"name":"Microsoft Research, Seattle, WA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jieping","family":"Ye","sequence":"additional","affiliation":[{"name":"University of Michigan, Ann Arbor, MI, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ce","family":"Zhang","sequence":"additional","affiliation":[{"name":"ETH Z\u00fcrich, Z\u00fcrich, Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,6,11]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"2008. Epsilon dataset. https:\/\/www.k4all.org\/project\/large-scale-learning-challenge\/."},{"key":"e_1_3_2_2_2_1","unstructured":"2009. CIFAR-10 dataset. http:\/\/www.cs.toronto.edu\/~kriz\/cifar.html."},{"key":"e_1_3_2_2_3_1","unstructured":"2011. LIBSVM Data. https:\/\/www.csie.ntu.edu.tw\/~cjlin\/libsvmtools\/datasets\/."},{"key":"e_1_3_2_2_4_1","unstructured":"2022. Apache MADlib: Big Data Machine Learning in SQL. http:\/\/madlib.apache.org\/."},{"key":"e_1_3_2_2_5_1","unstructured":"2022. Buffer Manager of PostgreSQL. https:\/\/www.interdb.jp\/pg\/pgsql08.html."},{"key":"e_1_3_2_2_6_1","unstructured":"2022. Google BigQuery ML. https:\/\/cloud.google.com\/bigquery-ml\/docs\/introduction."},{"key":"e_1_3_2_2_7_1","unstructured":"2022. I\/O performance test on HDD and SSD with distinct block sizes. https:\/\/github.com\/DS3Lab\/CorgiPile-PostgreSQL\/blob\/corgipile\/IO-perf.pdf."},{"key":"e_1_3_2_2_8_1","unstructured":"2022. Microsoft SQL Server Machine Learning Services. https:\/\/docs.microsoft.com\/en-us\/sql\/machine-learning\/sql-server-machine-learning-services?view=sql-server-ver15."},{"key":"e_1_3_2_2_9_1","unstructured":"2022. Oracle R Enterprise Versions of R Models. https:\/\/docs.oracle.com\/cd\/E11882_01\/doc.112\/e36761\/orelm.htm."},{"key":"e_1_3_2_2_10_1","unstructured":"2022. PostgreSQL. https:\/\/www.postgresql.org\/."},{"key":"e_1_3_2_2_11_1","unstructured":"2022. PostgreSQL TOAST. https:\/\/www.postgresql.org\/docs\/9.5\/storage-toast.html."},{"key":"e_1_3_2_2_12_1","unstructured":"2022. Sliding-Window Shuffle in TensorFlow. https:\/\/www.tensorflow.org\/api_docs\/python\/tf\/data\/Dataset."},{"key":"e_1_3_2_2_13_1","unstructured":"2022. The Greenplum MADlib extension. https:\/\/greenplum.docs.pivotal.io\/6--19\/analytics\/madlib.html."},{"key":"e_1_3_2_2_14_1","unstructured":"2022. The proof of our theorems in the theoretical analysis of CorgiPile. https:\/\/github.com\/DS3Lab\/CorgiPile-PostgreSQL\/blob\/corgipile\/Proof.pdf."},{"key":"e_1_3_2_2_15_1","volume-title":"TensorFlow: A System for Large-Scale Machine Learning. In 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI","author":"Abadi Mart\u00edn","year":"2016","unstructured":"Mart\u00edn Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek Gordon Murray, Benoit Steiner, Paul A. Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng. 2016. TensorFlow: A System for Large-Scale Machine Learning. In 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016. USENIX Association, 265--283."},{"key":"e_1_3_2_2_16_1","volume-title":"Arpaci-Dusseau","author":"Arpaci-Dusseau Remzi H.","year":"2018","unstructured":"Remzi H. Arpaci-Dusseau and Andrea C. Arpaci-Dusseau. 2018. Operating Systems: Three Easy Pieces (1.00 ed.). Arpaci-Dusseau Books."},{"key":"e_1_3_2_2_17_1","volume-title":"Proceedings of the symposium on learning and data science","volume":"8","author":"Bottou L\u00e9on","year":"2009","unstructured":"L\u00e9on Bottou. 2009. Curiously fast convergence of some stochastic gradient descent algorithms. In Proceedings of the symposium on learning and data science, Paris, Vol. 8. 2624--2633."},{"key":"e_1_3_2_2_18_1","volume-title":"Large-Scale Machine Learning with Stochastic Gradient Descent. In 19th International Conference on Computational Statistics, COMPSTAT","author":"Bottou L\u00e9on","year":"2010","unstructured":"L\u00e9on Bottou. 2010. Large-Scale Machine Learning with Stochastic Gradient Descent. In 19th International Conference on Computational Statistics, COMPSTAT 2010. 177--186."},{"key":"e_1_3_2_2_19_1","volume-title":"Neural networks: Tricks of the trade","author":"Bottou L\u00e9on","unstructured":"L\u00e9on Bottou. 2012. Stochastic gradient descent tricks. In Neural networks: Tricks of the trade. Springer, 421--436."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1137\/16M1080173"},{"key":"e_1_3_2_2_21_1","volume-title":"Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD","author":"Cai Zhuhua","year":"2013","unstructured":"Zhuhua Cai, Zografoula Vagena, Luis Leopoldo Perez, Subramanian Arumugam, Peter J. Haas, and Christopher M. Jermaine. 2013. Simulation of database-valued markov chains using SimSQL. In Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2013. ACM, 637--648."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.14778\/3137628.3137633"},{"key":"e_1_3_2_2_23_1","unstructured":"Tianqi Chen Tong He Michael Benesty Vadim Khotilovich Yuan Tang Hyunsu Cho et al. 2015. Xgboost: extreme gradient boosting. R package version 0.4--2 1 4 (2015) 1--4."},{"key":"e_1_3_2_2_24_1","volume-title":"Random reshuffling is not always better. Advances in Neural Information Processing Systems 33","author":"De Sa Christopher M","year":"2020","unstructured":"Christopher M De Sa. 2020. Random reshuffling is not always better. Advances in Neural Information Processing Systems 33 (2020)."},{"key":"e_1_3_2_2_25_1","unstructured":"Jeffrey Dean Greg Corrado Rajat Monga Kai Chen Matthieu Devin Mark Mao Marc'aurelio Ranzato Andrew Senior Paul Tucker Ke Yang et al. 2012. Large scale distributed deep networks. Advances in neural information processing systems 25 (2012) 1223--1231."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3386137"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2213836.2213874"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1137\/120880811"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/69.273032"},{"key":"e_1_3_2_2_30_1","volume-title":"Why random reshuffling beats stochastic gradient descent. Mathematical Programming","author":"G\u00fcrb\u00fczbalaban Mert","year":"2019","unstructured":"Mert G\u00fcrb\u00fczbalaban, Asu Ozdaglar, and Pablo A Parrilo. 2019. Why random reshuffling beats stochastic gradient descent. Mathematical Programming (2019), 1--36."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-019-01440-w"},{"key":"e_1_3_2_2_32_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning, ICML 2019, (Proceedings of Machine Learning Research","volume":"2633","author":"Jeff","unstructured":"Jeff Z. HaoChen and Suvrit Sra. 2019. Random Shuffling Beats SGD after Finite Epochs. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, (Proceedings of Machine Learning Research, Vol. 97). PMLR, 2624--2633."},{"key":"e_1_3_2_2_33_1","volume-title":"Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR","author":"He Kaiming","year":"2016","unstructured":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016. 770--778."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.14778\/2367502.2367510"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3422648.3422659"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.14778\/3450980.3450991"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3380575"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3035933"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.14778\/3297753.3297756"},{"key":"e_1_3_2_2_40_1","volume-title":"Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems 30","author":"Ke Guolin","year":"2017","unstructured":"Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems 30 (2017), 3146--3154."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3196959.3196960"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1181"},{"key":"e_1_3_2_2_43_1","volume-title":"Learning Generalized Linear Models Over Normalized Data. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. ACM","author":"Kumar Arun","unstructured":"Arun Kumar, Jeffrey F. Naughton, and Jignesh M. Patel. 2015. Learning Generalized Linear Models Over Normalized Data. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. ACM, 1969--1984."},{"key":"e_1_3_2_2_44_1","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems. 5336--5346","author":"Lian Xiangru","year":"2017","unstructured":"Xiangru Lian, Ce Zhang, Huan Zhang, Cho-Jui Hsieh, Wei Zhang, and Ji Liu. 2017. Can decentralized algorithms outperform centralized algorithms? a case study for decentralized parallel stochastic gradient descent. In Proceedings of the 31st International Conference on Neural Information Processing Systems. 5336--5346."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1561\/1900000062"},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3405470"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457317"},{"key":"e_1_3_2_2_48_1","volume-title":"Predictive Analysis with SAP","author":"MacGregor John","unstructured":"John MacGregor. 2013. Predictive Analysis with SAP. Bonn: Galileo Press."},{"key":"e_1_3_2_2_49_1","article-title":"MLlib: Machine Learning in Apache Spark","volume":"17","author":"Meng Xiangrui","year":"2016","unstructured":"Xiangrui Meng, Joseph K. Bradley, Burak Yavuz, Evan R. Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, D. B. Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J. Franklin, Reza Zadeh, Matei Zaharia, and Ameet Talwalkar. 2016. MLlib: Machine Learning in Apache Spark. J. Mach. Learn. Res. 17 (2016), 34:1--34:7.","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_2_50_1","volume-title":"Ahmed Khaled Ragab Bayoumi, and Peter Richt\u00e1rik","author":"Mishchenko Konstantin","year":"2020","unstructured":"Konstantin Mishchenko, Ahmed Khaled Ragab Bayoumi, and Peter Richt\u00e1rik. 2020. Random reshuffling: Simple analysis with vast improvements. Advances in Neural Information Processing Systems 33 (2020)."},{"key":"e_1_3_2_2_51_1","unstructured":"Eric Moulines and Francis R Bach. 2011. Non-asymptotic analysis of stochastic approximation algorithms for machine learning. In Advances in Neural Information Processing Systems. 451--459."},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.14778\/3407790.3407816"},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.14778\/3007263.3007312"},{"key":"e_1_3_2_2_54_1","volume-title":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014. 1532--1543","author":"Pennington Jeffrey","unstructured":"Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. Glove: Global Vectors for Word Representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014. 1532--1543."},{"key":"e_1_3_2_2_55_1","volume-title":"Gradient methods for minimizing functionals. Zhurnal Vychislitel'noi Matematiki i Matematicheskoi Fiziki 3, 4","author":"Polyak Boris Teodorovich","year":"1963","unstructured":"Boris Teodorovich Polyak. 1963. Gradient methods for minimizing functionals. Zhurnal Vychislitel'noi Matematiki i Matematicheskoi Fiziki 3, 4 (1963), 643--653."},{"key":"e_1_3_2_2_56_1","volume-title":"International Conference on Machine Learning. PMLR, 7964--7973","author":"Rajput Shashank","year":"2020","unstructured":"Shashank Rajput, Anant Gupta, and Dimitris Papailiopoulos. 2020. Closing the convergence gap of SGD without replacement. In International Conference on Machine Learning. PMLR, 7964--7973."},{"key":"e_1_3_2_2_57_1","doi-asserted-by":"publisher","DOI":"10.14778\/2535573.2488340"},{"key":"e_1_3_2_2_58_1","volume-title":"Conference on Learning Theory. PMLR, 3250--3284","author":"Safran Itay","year":"2020","unstructured":"Itay Safran and Ohad Shamir. 2020. How good is SGD with random shuffling?. In Conference on Learning Theory. PMLR, 3250--3284."},{"key":"e_1_3_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/2882903.2882939"},{"key":"e_1_3_2_2_60_1","unstructured":"Ohad Shamir. 2016. Without-replacement sampling for stochastic gradient methods. In Advances in Neural Information Processing Systems. 46--54."},{"key":"e_1_3_2_2_61_1","volume-title":"Very Deep Convolutional Networks for Large-Scale Image Recognition. In 3rd International Conference on Learning Representations, ICLR","author":"Simonyan Karen","year":"2015","unstructured":"Karen Simonyan and Andrew Zisserman. 2015. Very Deep Convolutional Networks for Large-Scale Image Recognition. In 3rd International Conference on Learning Representations, ICLR 2015."},{"key":"e_1_3_2_2_62_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning, ICML 2018 (Proceedings of Machine Learning Research","volume":"4863","author":"Tang Hanlin","year":"2018","unstructured":"Hanlin Tang, Xiangru Lian, Ming Yan, Ce Zhang, and Ji Liu. 2018. D 2 : Decentralized Training over Decentralized Data. In Proceedings of the 35th International Conference on Machine Learning, ICML 2018 (Proceedings of Machine Learning Research, Vol. 80). PMLR, 4855--4863."},{"key":"e_1_3_2_2_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/2812802"},{"key":"e_1_3_2_2_64_1","volume-title":"Jinliang Wei, Seunghak Lee, Xun Zheng, Pengtao Xie, Abhimanu Kumar, and Yaoliang Yu.","author":"Xing Eric P","year":"2015","unstructured":"Eric P Xing, Qirong Ho, Wei Dai, Jin Kyu Kim, Jinliang Wei, Seunghak Lee, Xun Zheng, Pengtao Xie, Abhimanu Kumar, and Yaoliang Yu. 2015. Petuum: A new platform for distributed machine learning on big data. IEEE transactions on Big Data 1, 2 (2015), 49--67."},{"key":"e_1_3_2_2_65_1","volume-title":"Hovy","author":"Yang Zichao","year":"2016","unstructured":"Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alexander J. Smola, and Eduard H. Hovy. 2016. Hierarchical Attention Networks for Document Classification. In NAACL HLT 2016, The 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2016. 1480--1489."},{"key":"e_1_3_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2018.2878551"},{"key":"e_1_3_2_2_67_1","doi-asserted-by":"publisher","DOI":"10.14778\/3457390.3457399"},{"key":"e_1_3_2_2_68_1","volume-title":"Conference on Learning Theory, COLT 2021 (Proceedings of Machine Learning Research","volume":"4658","author":"Yun Chulhee","year":"2021","unstructured":"Chulhee Yun, Suvrit Sra, and Ali Jadbabaie. 2021. Open Problem: Can Single-Shuffle SGD be Better than Reshuffling SGD and GD?. In Conference on Learning Theory, COLT 2021 (Proceedings of Machine Learning Research, Vol. 134). PMLR, 4653--4658."},{"key":"e_1_3_2_2_69_1","doi-asserted-by":"publisher","DOI":"10.14778\/2732977.2733001"},{"key":"e_1_3_2_2_70_1","volume-title":"Character-level Convolutional Networks for Text Classification. In Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems","author":"Zhang Xiang","year":"2015","unstructured":"Xiang Zhang, Junbo Jake Zhao, and Yann LeCun. 2015. Character-level Convolutional Networks for Text Classification. In Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015. 649--657."},{"key":"e_1_3_2_2_71_1","doi-asserted-by":"publisher","DOI":"10.14778\/3467861.3467867"},{"key":"e_1_3_2_2_72_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2019.00194"}],"event":{"name":"SIGMOD\/PODS '22: International Conference on Management of Data","location":"Philadelphia PA USA","acronym":"SIGMOD\/PODS '22","sponsor":["SIGMOD ACM Special Interest Group on Management of Data"]},"container-title":["Proceedings of the 2022 International Conference on Management of Data"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3514221.3526150","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3514221.3526150","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:10:13Z","timestamp":1750183813000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3514221.3526150"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,10]]},"references-count":72,"alternative-id":["10.1145\/3514221.3526150","10.1145\/3514221"],"URL":"https:\/\/doi.org\/10.1145\/3514221.3526150","relation":{},"subject":[],"published":{"date-parts":[[2022,6,10]]},"assertion":[{"value":"2022-06-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}