{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T11:05:34Z","timestamp":1779447934093,"version":"3.53.1"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["The VLDB Journal"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s00778-026-00973-9","type":"journal-article","created":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T18:32:54Z","timestamp":1775241174000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["LEON+: towards robust ML-aided query optimization"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-3909-4021","authenticated-orcid":false,"given":"Xu","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ximu","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuze","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zibo","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,3]]},"reference":[{"key":"973_CR1","doi-asserted-by":"crossref","unstructured":"Akdere, M., \u00c7etintemel, U., Riondato, M., Upfal, E., Zdonik, S.B.: Learning-based query performance modeling and prediction. In: 2012 IEEE 28th International Conference on Data Engineering, pp. 390\u2013401. IEEE (2012)","DOI":"10.1109\/ICDE.2012.64"},{"key":"973_CR2","doi-asserted-by":"crossref","unstructured":"Basu, D., Lin, Q., Chen, W., Vo, H.T., Yuan, Z., Senellart, P., Bressan, S.: Cost-model oblivious database tuning with reinforcement learning. In: Database and Expert Systems Applications (2015)","DOI":"10.1007\/978-3-319-22849-5_18"},{"key":"973_CR3","doi-asserted-by":"crossref","unstructured":"Basu, D., Lin, Q., Chen, W., Vo, H.T., Yuan, Z., Senellart, P., Bressan, S.: Regularized cost-model oblivious database tuning with reinforcement learning. In: Transactions on Large-Scale Data-and Knowledge-Centered Systems XXVIII, pp. 96\u2013132. Springer (2016)","DOI":"10.1007\/978-3-662-53455-7_5"},{"key":"973_CR4","unstructured":"Behr, H., Markl, V., Kaoudi, Z.: Learn what really matters: A learning-to-rank approach for ml-based query optimization. In: BTW 2023, pp. 535\u2013554. Gesellschaft f\u00fcr Informatik eV (2023)"},{"key":"973_CR5","doi-asserted-by":"crossref","unstructured":"Bharadhwaj, H.: Meta-learning for user cold-start recommendation. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2019)","DOI":"10.1109\/IJCNN.2019.8852100"},{"key":"973_CR6","doi-asserted-by":"crossref","unstructured":"Cao, Z., Qin, T., Liu, T.Y., Tsai, M.F., Li, H.: Learning to rank: from pairwise approach to listwise approach. In: Proceedings of the 24th international conference on Machine learning, pp. 129\u2013136 (2007)","DOI":"10.1145\/1273496.1273513"},{"issue":"9","key":"973_CR7","doi-asserted-by":"publisher","first-page":"2261","DOI":"10.14778\/3598581.3598597","volume":"16","author":"X Chen","year":"2023","unstructured":"Chen, X., Chen, H., Liang, Z., Liu, S., Wang, J., Zeng, K., Su, H., Zheng, K.: Leon: a new framework for ml-aided query optimization. Proceedings of the VLDB Endowment 16(9), 2261\u20132273 (2023)","journal-title":"Proceedings of the VLDB Endowment"},{"key":"973_CR8","doi-asserted-by":"crossref","unstructured":"Ding, B., Das, S., Marcus, R., Wu, W., Chaudhuri, S., Narasayya, V.R.: Ai meets ai: Leveraging query executions to improve index recommendations. In: Proceedings of the 2019 International Conference on Management of Data, pp. 1241\u20131258 (2019)","DOI":"10.1145\/3299869.3324957"},{"key":"973_CR9","first-page":"1094","volume":"33","author":"J Ding","year":"2020","unstructured":"Ding, J., Quan, Y., Yao, Q., Li, Y., Jin, D.: Simplify and robustify negative sampling for implicit collaborative filtering. Adv. Neural. Inf. Process. Syst. 33, 1094\u20131105 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"9","key":"973_CR10","doi-asserted-by":"publisher","first-page":"1044","DOI":"10.14778\/3329772.3329780","volume":"12","author":"A Dutt","year":"2019","unstructured":"Dutt, A., Wang, C., Nazi, A., Kandula, S., Narasayya, V., Chaudhuri, S.: Selectivity estimation for range predicates using lightweight models. Proc. VLDB Endow. 12(9), 1044\u20131057 (2019)","journal-title":"Proc. VLDB Endow."},{"key":"973_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2022.105151","volume":"115","author":"MA Ganaie","year":"2022","unstructured":"Ganaie, M.A., Hu, M., Malik, A.K., Tanveer, M., Suganthan, P.N.: Ensemble deep learning: A review. Eng. Appl. Artif. Intell. 115, 105151 (2022)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"973_CR12","unstructured":"Goodfellow, I.J., Mirza, M., Xiao, D., Courville, A., Bengio, Y.: An empirical investigation of catastrophic forgetting in gradient-based neural networks (2013). arXiv preprint arXiv:1312.6211"},{"issue":"3","key":"973_CR13","first-page":"19","volume":"18","author":"G Graefe","year":"1995","unstructured":"Graefe, G.: The cascades framework for query optimization. IEEE Data Eng. Bull. 18(3), 19\u201329 (1995)","journal-title":"IEEE Data Eng. Bull."},{"key":"973_CR14","doi-asserted-by":"crossref","unstructured":"Graefe, G., McKenna, W.J.: The volcano optimizer generator: Extensibility and efficient search. In: Proceedings of IEEE 9th international conference on data engineering, pp. 209\u2013218. IEEE (1993)","DOI":"10.1109\/ICDE.1993.344061"},{"key":"973_CR15","doi-asserted-by":"crossref","unstructured":"Han, Y., Wu, Z., Wu, P., Zhu, R., Yang, J., Tan, L.W., Zeng, K., Cong, G., Qin, Y., Pfadler, A., et\u00a0al.: Cardinality estimation in dbms: A comprehensive benchmark evaluation (2021). arXiv preprint arXiv:2109.05877","DOI":"10.14778\/3503585.3503586"},{"key":"973_CR16","doi-asserted-by":"crossref","unstructured":"Jindal, A., Qiao, S., Sen, R., Patel, H.: Microlearner: A fine-grained learning optimizer for big data workloads at microsoft. In: 2021 IEEE 37th International Conference on Data Engineering (ICDE), pp. 2423\u20132434. IEEE (2021)","DOI":"10.1109\/ICDE51399.2021.00275"},{"issue":"2","key":"973_CR17","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1109\/MCI.2022.3155327","volume":"17","author":"LV Jospin","year":"2022","unstructured":"Jospin, L.V., Laga, H., Boussaid, F., Buntine, W., Bennamoun, M.: Hands-on bayesian neural networks-a tutorial for deep learning users. IEEE Comput. Intell. Mag. 17(2), 29\u201348 (2022)","journal-title":"IEEE Comput. Intell. Mag."},{"key":"973_CR18","unstructured":"Krishnan, S., Yang, Z., Goldberg, K., Hellerstein, J., Stoica, I.: Learning to optimize join queries with deep reinforcement learning (2018). arXiv preprint arXiv:1808.03196"},{"key":"973_CR19","doi-asserted-by":"publisher","first-page":"2871","DOI":"10.14778\/3611479.3611494","volume":"16","author":"K Lee","year":"2023","unstructured":"Lee, K., Dutt, A., Narasayya, V., Chaudhuri, S.: Analyzing the impact of cardinality estimation on execution plans in microsoft sql server. Proceedings of the VLDB Endowment 16, 2871\u20132883 (2023)","journal-title":"Proceedings of the VLDB Endowment"},{"issue":"3","key":"973_CR20","doi-asserted-by":"publisher","first-page":"204","DOI":"10.14778\/2850583.2850594","volume":"9","author":"V Leis","year":"2015","unstructured":"Leis, V., Gubichev, A., Mirchev, A., Boncz, P., Kemper, A., Neumann, T.: How good are query optimizers, really? Proceedings of the VLDB Endowment 9(3), 204\u2013215 (2015)","journal-title":"Proceedings of the VLDB Endowment"},{"key":"973_CR21","doi-asserted-by":"crossref","unstructured":"Li, G., Zhou, X., Cao, L.: Ai meets database: Ai4db and db4ai. In: Proceedings of the 2021 International Conference on Management of Data, pp. 2859\u20132866 (2021)","DOI":"10.1145\/3448016.3457542"},{"issue":"12","key":"973_CR22","doi-asserted-by":"publisher","first-page":"2118","DOI":"10.14778\/3352063.3352129","volume":"12","author":"G Li","year":"2019","unstructured":"Li, G., Zhou, X., Li, S., Gao, B.: Qtune: A query-aware database tuning system with deep reinforcement learning. Proc. VLDB Endow. 12(12), 2118\u20132130 (2019)","journal-title":"Proc. VLDB Endow."},{"issue":"3","key":"973_CR23","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1109\/TMI.2019.2934577","volume":"39","author":"L Liu","year":"2019","unstructured":"Liu, L., Dou, Q., Chen, H., Qin, J., Heng, P.A.: Multi-task deep model with margin ranking loss for lung nodule analysis. IEEE Trans. Med. Imaging 39(3), 718\u2013728 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"973_CR24","doi-asserted-by":"crossref","unstructured":"Ma, L., Ding, B., Das, S., Swaminathan, A.: Active learning for ml enhanced database systems. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (2020)","DOI":"10.1145\/3318464.3389768"},{"issue":"1","key":"973_CR25","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1145\/3542700.3542703","volume":"51","author":"R Marcus","year":"2022","unstructured":"Marcus, R., Negi, P., Mao, H., Tatbul, N., Alizadeh, M., Kraska, T.: Bao: Making learned query optimization practical. ACM SIGMOD Rec. 51(1), 6\u201313 (2022)","journal-title":"ACM SIGMOD Rec."},{"key":"973_CR26","unstructured":"Marcus, R., Negi, P., Mao, H., Zhang, C., Alizadeh, M., Kraska, T., Papaemmanouil, O., Tatbul, N.: Neo: A learned query optimizer (2019). arXiv preprint arXiv:1904.03711"},{"key":"973_CR27","doi-asserted-by":"crossref","unstructured":"Marcus, R., Papaemmanouil, O.: Deep reinforcement learning for join order enumeration. In: Proceedings of the First International Workshop on Exploiting Artificial Intelligence Techniques for Data Management, pp. 1\u20134 (2018)","DOI":"10.1145\/3211954.3211957"},{"key":"973_CR28","doi-asserted-by":"crossref","unstructured":"Marcus, R., Papaemmanouil, O.: Plan-structured deep neural network models for query performance prediction (2019). arXiv:1902.00132","DOI":"10.14778\/3342263.3342646"},{"key":"973_CR29","doi-asserted-by":"crossref","unstructured":"Markl, V., Lohman, G.M., Raman, V.: Leo: An autonomic query optimizer for db2. IBM Systems Journal (2003)","DOI":"10.1147\/sj.421.0098"},{"issue":"1","key":"973_CR30","doi-asserted-by":"publisher","first-page":"982","DOI":"10.14778\/1687627.1687738","volume":"2","author":"G Moerkotte","year":"2009","unstructured":"Moerkotte, G., Neumann, T., Steidl, G.: Preventing bad plans by bounding the impact of cardinality estimation errors. Proc. VLDB Endow. 2(1), 982\u2013993 (2009)","journal-title":"Proc. VLDB Endow."},{"key":"973_CR31","unstructured":"Mullachery, V., Khera, A., Husain, A.: Bayesian neural networks (2018). arXiv preprint arXiv:1801.07710"},{"key":"973_CR32","doi-asserted-by":"crossref","unstructured":"Negi, P., Interlandi, M., Marcus, R., Alizadeh, M., Kraska, T., Friedman, M., Jindal, A.: Steering query optimizers: A practical take on big data workloads. In: Proceedings of the 2021 International Conference on Management of Data (2021)","DOI":"10.1145\/3448016.3457568"},{"key":"973_CR33","doi-asserted-by":"crossref","unstructured":"Negi, P., Marcus, R., Kipf, A., Mao, H., Tatbul, N., Kraska, T., Alizadeh, M.: Flow-loss: learning cardinality estimates that matter (2021). arXiv preprint arXiv:2101.04964","DOI":"10.14778\/3476249.3476259"},{"key":"973_CR34","unstructured":"Ortiz, J., Balazinska, M., Gehrke, J., Keerthi, S.S.: An empirical analysis of deep learning for cardinality estimation (2019). arXiv preprint arXiv:1905.06425"},{"key":"973_CR35","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Transactions on knowledge and data engineering (2009)"},{"key":"973_CR36","unstructured":"balsa project: https:\/\/github.com\/balsa-project\/balsa (2022)"},{"key":"973_CR37","unstructured":"RyanMarcus: https:\/\/github.com\/learnedsystems\/BaoForPostgreSQL"},{"key":"973_CR38","doi-asserted-by":"crossref","unstructured":"Saxena, G., Rahman, M., Chainani, N., Lin, C., Caragea, G., Chowdhury, F., Marcus, R., Kraska, T., Pandis, I., Narayanaswamy, B.: Auto-wlm: Machine learning enhanced workload management in amazon redshift. In: Companion of the 2023 International Conference on Management of Data, pp. 225\u2013237 (2023)","DOI":"10.1145\/3555041.3589677"},{"key":"973_CR39","unstructured":"Schulman, J., Levine, S., Abbeel, P., Jordan, M., Moritz, P.: Trust region policy optimization. In: International conference on machine learning, pp. 1889\u20131897. PMLR (2015)"},{"key":"973_CR40","doi-asserted-by":"crossref","unstructured":"Selinger, P.G., Astrahan, M.M., Chamberlin, D.D., Lorie, R.A., Price, T.G.: Access path selection in a relational database management system. In: Proceedings of the 1979 ACM SIGMOD international conference on Management of data, pp. 23\u201334 (1979)","DOI":"10.1145\/582095.582099"},{"key":"973_CR41","unstructured":"Sharma, A., Schuhknecht, F.M., Dittrich, J.: The case for automatic database administration using deep reinforcement learning (2018). arXiv preprint arXiv:1801.05643"},{"key":"973_CR42","doi-asserted-by":"crossref","unstructured":"Siddiqui, T., Jindal, A., Qiao, S., Patel, H., Le, W.: Cost models for big data query processing: Learning, retrofitting, and our findings. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (2020)","DOI":"10.1145\/3318464.3380584"},{"key":"973_CR43","doi-asserted-by":"crossref","unstructured":"Sun, J., Li, G.: An end-to-end learning-based cost estimator (2019). arXiv preprint arXiv:1906.02560","DOI":"10.14778\/3368289.3368296"},{"key":"973_CR44","doi-asserted-by":"crossref","unstructured":"Van\u00a0Aken, D., Yang, D., Brillard, S., Fiorino, A., Zhang, B., Bilien, C., Pavlo, A.: An inquiry into machine learning-based automatic configuration tuning services on real-world database management systems. Proceedings of the VLDB Endowment (2021)","DOI":"10.14778\/3450980.3450992"},{"key":"973_CR45","doi-asserted-by":"crossref","unstructured":"Wang, J., Chai, C., Liu, J., Li, G.: Face: a normalizing flow based cardinality estimator. Proceedings of the VLDB Endowment (2021)","DOI":"10.14778\/3485450.3485458"},{"key":"973_CR46","doi-asserted-by":"crossref","unstructured":"Wang, X., Hua, Y., Kodirov, E., Hu, G., Garnier, R., Robertson, N.M.: Ranked list loss for deep metric learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 5207\u20135216 (2019)","DOI":"10.1109\/CVPR.2019.00535"},{"issue":"3","key":"973_CR47","doi-asserted-by":"publisher","first-page":"210","DOI":"10.14778\/3291264.3291267","volume":"12","author":"C Wu","year":"2018","unstructured":"Wu, C., Jindal, A., Amizadeh, S., Patel, H., Le, W., Qiao, S., Rao, S.: Towards a learning optimizer for shared clouds. Proceedings of the VLDB Endowment 12(3), 210\u2013222 (2018)","journal-title":"Proceedings of the VLDB Endowment"},{"key":"973_CR48","doi-asserted-by":"crossref","unstructured":"Wu, Z., Marcus, R., Liu, Z., Negi, P., Nathan, V., Pfeil, P., Saxena, G., Rahman, M., Narayanaswamy, B., Kraska, T.: Stage: Query execution time prediction in amazon redshift. In: Companion of the 2024 International Conference on Management of Data, pp. 280\u2013294 (2024)","DOI":"10.1145\/3626246.3653391"},{"key":"973_CR49","unstructured":"Wu, Z., Shaikhha, A.: Bayescard: A unified bayesian framework for cardinality estimation. arXiv e-prints (2020)"},{"key":"973_CR50","doi-asserted-by":"crossref","unstructured":"Yang, Z., Chiang, W.L., Luan, S., Mittal, G., Luo, M., Stoica, I.: Balsa: Learning a query optimizer without expert demonstrations (2022). arXiv preprint arXiv:2201.01441","DOI":"10.1145\/3514221.3517885"},{"key":"973_CR51","doi-asserted-by":"crossref","unstructured":"Yang, Z., Kamsetty, A., Luan, S., Liang, E., Duan, Y., Chen, X., Stoica, I.: Neurocard: one cardinality estimator for all tables (2020). arXiv preprint arXiv:2006.08109","DOI":"10.14778\/3421424.3421432"},{"key":"973_CR52","doi-asserted-by":"crossref","unstructured":"Yang, Z., Kamsetty, A., Luan, S., Liang, E., Duan, Y., Chen, X., Stoica, I.: NeuroCard: One cardinality estimator for all tables. pp. 61\u201373. VLDB Endowment (2021)","DOI":"10.14778\/3421424.3421432"},{"key":"973_CR53","doi-asserted-by":"crossref","unstructured":"Ying, X.: An overview of overfitting and its solutions. In: Journal of physics: Conference series, vol. 1168, p. 022022. IOP Publishing (2019)","DOI":"10.1088\/1742-6596\/1168\/2\/022022"},{"key":"973_CR54","doi-asserted-by":"crossref","unstructured":"Yu, X., Li, G., Chai, C., Tang, N.: Reinforcement learning with tree-lstm for join order selection. In: 2020 IEEE 36th International Conference on Data Engineering (ICDE) (2020)","DOI":"10.1109\/ICDE48307.2020.00116"},{"key":"973_CR55","doi-asserted-by":"crossref","unstructured":"Zhang, J., Liu, Y., Zhou, K., Li, G., Xiao, Z., Cheng, B., Xing, J., Wang, Y., Cheng, T., Liu, L., et\u00a0al.: An end-to-end automatic cloud database tuning system using deep reinforcement learning. In: Proceedings of the 2019 International Conference on Management of Data (2019)","DOI":"10.1145\/3299869.3300085"},{"key":"973_CR56","doi-asserted-by":"crossref","unstructured":"Zhang, W., Chen, T., Wang, J., Yu, Y.: Optimizing top-n collaborative filtering via dynamic negative item sampling. In: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, pp. 785\u2013788 (2013)","DOI":"10.1145\/2484028.2484126"},{"key":"973_CR57","doi-asserted-by":"crossref","unstructured":"Zhang, W., Interlandi, M., Mineiro, P., Qiao, S., Ghazanfari, N., Lie, K., Friedman, M., Hosn, R., Patel, H., Jindal, A.: Deploying a steered query optimizer in production at microsoft. In: Proceedings of the 2022 International Conference on Management of Data (2022)","DOI":"10.1145\/3514221.3526052"},{"key":"973_CR58","unstructured":"Zhou, X., Chai, C., Li, G., Sun, J.: Database meets artificial intelligence: A survey. IEEE Transactions on Knowledge and Data Engineering (2020)"},{"key":"973_CR59","unstructured":"Zhu, R., Wu, Z., Han, Y., Zeng, K., Pfadler, A., Qian, Z., Zhou, J., Cui, B.: Flat: fast, lightweight and accurate method for cardinality estimation (2020). arXiv preprint arXiv:2011.09022"}],"container-title":["The VLDB Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00778-026-00973-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00778-026-00973-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00778-026-00973-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T10:16:31Z","timestamp":1779444991000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00778-026-00973-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":59,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["973"],"URL":"https:\/\/doi.org\/10.1007\/s00778-026-00973-9","relation":{},"ISSN":["1066-8888","0949-877X"],"issn-type":[{"value":"1066-8888","type":"print"},{"value":"0949-877X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"18 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 November 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 April 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"21"}}