{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T18:13:03Z","timestamp":1790446383042,"version":"4.1.0"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T00:00:00Z","timestamp":1734912000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T00:00:00Z","timestamp":1734912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["834141"],"award-info":[{"award-number":["834141"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["K\u00fcnstl Intell"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s13218-024-00881-6","type":"journal-article","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T11:19:11Z","timestamp":1734952751000},"page":"119-140","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Prescriptive Process Monitoring Under Resource Constraints: A Reinforcement Learning Approach"],"prefix":"10.1007","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7423-9909","authenticated-orcid":false,"given":"Mahmoud","family":"Shoush","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9247-7476","authenticated-orcid":false,"given":"Marlon","family":"Dumas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,23]]},"reference":[{"key":"881_CR1","doi-asserted-by":"publisher","unstructured":"Dasht Bozorgi Z, Teinemaa I, Dumas M, La Rosa M, Polyvyanyy A (2023) Prescriptive process monitoring based on causal effect estimation. Inf Syst 116:102198. https:\/\/doi.org\/10.1016\/j.is.2023.102198, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0306437923000340","DOI":"10.1016\/j.is.2023.102198"},{"key":"881_CR2","doi-asserted-by":"crossref","unstructured":"Shoush M, Dumas M (2021) Prescriptive process monitoring under resource constraints: A causal inference approach. In: ICPM Workshops. Lecture Notes in Business Information Processing, vol.\u00a0433, pp. 180\u2013193. Springer","DOI":"10.1007\/978-3-030-98581-3_14"},{"key":"881_CR3","unstructured":"Weytjens H, Verbeke W, Weerdt JD (2023) Timing process interventions with causal inference and reinforcement learning. arXiv:abs\/2306.04299"},{"key":"881_CR4","doi-asserted-by":"crossref","unstructured":"Metzger A, Kley T, Palm A (2020) Triggering proactive business process adaptations via online reinforcement learning. In: BPM. Lecture Notes in Computer Science, vol. 12168, pp. 273\u2013290. Springer","DOI":"10.1007\/978-3-030-58666-9_16"},{"key":"881_CR5","doi-asserted-by":"publisher","unstructured":"Bozorgi ZD, Dumas M, Rosa ML, Polyvyanyy A, Shoush M, Teinemaa I (2023) Learning when to treat business processes: Prescriptive process monitoring with causal inference and reinforcement learning. CoRR arXiv:abs\/2303.03572. https:\/\/doi.org\/10.48550\/arXiv.2303.03572","DOI":"10.48550\/arXiv.2303.03572"},{"key":"881_CR6","doi-asserted-by":"crossref","unstructured":"Palm A, Metzger A, Pohl K (2020) Online reinforcement learning for self-adaptive information systems. In: CAiSE. Lecture Notes in Computer Science, vol. 12127, pp. 169\u2013184. Springer","DOI":"10.1007\/978-3-030-49435-3_11"},{"key":"881_CR7","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1097","volume":"8","author":"K Kubrak","year":"2022","unstructured":"Kubrak K, Milani F, Nolte A, Dumas M (2022) Prescriptive process monitoring: Quo vadis? PeerJ Comput Sci 8:e1097","journal-title":"PeerJ Comput Sci"},{"key":"881_CR8","unstructured":"Branchi S, Buliga A, Francescomarino CD, Ghidini C, Meneghello F, Ronzani M (2023) Recommending the optimal policy by learning to act from temporal data. arXiv:abs\/2303.09209"},{"key":"881_CR9","doi-asserted-by":"crossref","unstructured":"Weinzierl S, Dunzer S, Zilker S, Matzner M (2020) Prescriptive business process monitoring for recommending next best actions. In: BPM (Forum). Lecture Notes in Business Information Processing, vol.\u00a0392, pp. 193\u2013209. Springer","DOI":"10.1007\/978-3-030-58638-6_12"},{"key":"881_CR10","doi-asserted-by":"crossref","unstructured":"de\u00a0Leoni M, Dees M, Reulink L (2020) Design and evaluation of a process-aware recommender system based on prescriptive analytics. In: ICPM. pp. 9\u201316. IEEE","DOI":"10.1109\/ICPM49681.2020.00013"},{"key":"881_CR11","doi-asserted-by":"crossref","unstructured":"Gr\u00f6ger C, Schwarz H, Mitschang B (2014) Prescriptive analytics for recommendation-based business process optimization. In: BIS. Lecture Notes in Business Information Processing, vol.\u00a0176, pp. 25\u201337. Springer","DOI":"10.1007\/978-3-319-06695-0_3"},{"key":"881_CR12","doi-asserted-by":"crossref","unstructured":"Park G, Song M (2019) Prediction-based resource allocation using LSTM and minimum cost and maximum flow algorithm. In: ICPM. pp. 121\u2013128. IEEE","DOI":"10.1109\/ICPM.2019.00027"},{"key":"881_CR13","doi-asserted-by":"crossref","unstructured":"Sindhgatta R, Ghose AK, Dam HK (2016) Context-aware analysis of past process executions to aid resource allocation decisions. In: CAiSE. Lecture Notes in Computer Science, vol.\u00a09694, pp. 575\u2013589. Springer","DOI":"10.1007\/978-3-319-39696-5_35"},{"key":"881_CR14","doi-asserted-by":"crossref","unstructured":"Abdulhameed NM, Helal IM, Awad A, Ezat E (2018) A resource recommendation approach based on co-working history. Int J Adv Comput Sci Appl 9(7)","DOI":"10.14569\/IJACSA.2018.090734"},{"key":"881_CR15","doi-asserted-by":"crossref","unstructured":"Metzger A, Kley T, Rothweiler A, Pohl K (2023) Automatically reconciling the trade-off between prediction accuracy and earliness in prescriptive business process monitoring. arXiv:abs\/2307.05939","DOI":"10.1016\/j.is.2023.102254"},{"key":"881_CR16","doi-asserted-by":"crossref","unstructured":"Donadello I, Francescomarino CD, Maggi FM, Ricci F, Shikhizada A (2022) Outcome-oriented prescriptive process monitoring based on temporal logic patterns. arXiv:abs\/2211.04880","DOI":"10.1016\/j.engappai.2023.106899"},{"issue":"2","key":"881_CR17","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1007\/s10115-021-01633-w","volume":"64","author":"SA Fahrenkrog-Petersen","year":"2022","unstructured":"Fahrenkrog-Petersen SA, Tax N, Teinemaa I, Dumas M, de Leoni M, Maggi FM, Weidlich M (2022) Fire now, fire later: alarm-based systems for prescriptive process monitoring. Knowl Inf Syst 64(2):559\u2013587","journal-title":"Knowl Inf Syst"},{"key":"881_CR18","doi-asserted-by":"crossref","unstructured":"Shoush M, Dumas M (2022) When to intervene? prescriptive process monitoring under uncertainty and resource constraints. In: BPM (Forum)","DOI":"10.1007\/978-3-031-16171-1_13"},{"key":"881_CR19","doi-asserted-by":"publisher","unstructured":"Shoush M, Dumas M (2022) Intervening with confidence: Conformal prescriptive monitoring of business processes. arXiv:abs\/2212.03710. https:\/\/doi.org\/10.48550\/arXiv.2212.03710","DOI":"10.48550\/arXiv.2212.03710"},{"key":"881_CR20","doi-asserted-by":"crossref","unstructured":"Weytjens H, Weerdt JD (2021) Learning uncertainty with artificial neural networks for improved remaining time prediction of business processes. In: International Conference on Business Process Management. pp. 141\u2013157. Springer","DOI":"10.1007\/978-3-030-85469-0_11"},{"key":"881_CR21","doi-asserted-by":"crossref","unstructured":"Baskharon F, Awad A, Francescomarino CD (2020) Predicting remaining cycle time from ongoing cases: A survival analysis-based approach. In: ICPM Workshops. Lecture Notes in Business Information Processing, vol.\u00a0406, pp. 99\u2013111. Springer","DOI":"10.1007\/978-3-030-72693-5_8"},{"key":"881_CR22","doi-asserted-by":"crossref","unstructured":"Teinemaa I, Dumas M, Rosa ML, Maggi FM (2019) Outcome-oriented predictive process monitoring: Review and benchmark. ACM Trans. Knowl. Discov. Data 13(2):17:1-17:57","DOI":"10.1145\/3301300"},{"key":"881_CR23","doi-asserted-by":"crossref","unstructured":"Tax N, Verenich I, Rosa ML, Dumas M (2017) Predictive business process monitoring with LSTM neural networks. In: CAiSE. Lecture Notes in Computer Science, vol. 10253, pp. 477\u2013492. Springer","DOI":"10.1007\/978-3-319-59536-8_30"},{"key":"881_CR24","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/j.is.2019.01.007","volume":"84","author":"A Senderovich","year":"2019","unstructured":"Senderovich A, Francescomarino CD, Maggi FM (2019) From knowledge-driven to data-driven inter-case feature encoding in predictive process monitoring. Inf Syst 84:255\u2013264","journal-title":"Inf Syst"},{"key":"881_CR25","doi-asserted-by":"crossref","unstructured":"Gustafson JL (2011) Little\u2019s law. In: Encyclopedia of Parallel Computing, pp. 1038\u20131041. Springer","DOI":"10.1007\/978-0-387-09766-4_79"},{"key":"881_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/b97377","volume-title":"Survival analysis: techniques for censored and truncated data","author":"JP Klein","year":"2003","unstructured":"Klein JP, Moeschberger ML et al (2003) Survival analysis: techniques for censored and truncated data, vol 1230. Springer, Germany"},{"key":"881_CR27","unstructured":"Malinin A, Prokhorenkova L, Ustimenko A (2020) Uncertainty in gradient boosting via ensembles. arXiv:2006.10562"},{"key":"881_CR28","unstructured":"Pearl J (2010) Causal inference. In: NIPS Causality: Objectives and Assessment. JMLR Proceedings, vol.\u00a06, pp. 39\u201358. JMLR.org"},{"key":"881_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2023.102198","volume":"116","author":"ZD Bozorgi","year":"2023","unstructured":"Bozorgi ZD, Teinemaa I, Dumas M, Rosa ML, Polyvyanyy A (2023) Prescriptive process monitoring based on causal effect estimation. Inf Syst 116:102198","journal-title":"Inf Syst"},{"issue":"5","key":"881_CR30","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1111\/rssb.12445","volume":"83","author":"L Lei","year":"2021","unstructured":"Lei L, Cand\u00e8s EJ (2021) Conformal inference of counterfactuals and individual treatment effects. J R Stat Soc Ser B: Stat Methodol 83(5):911\u2013938","journal-title":"J R Stat Soc Ser B: Stat Methodol"},{"key":"881_CR31","first-page":"371","volume":"9","author":"G Shafer","year":"2008","unstructured":"Shafer G, Vovk V (2008) A tutorial on conformal prediction. J Mach Learn Res 9:371\u2013421","journal-title":"J Mach Learn Res"},{"key":"881_CR32","unstructured":"Tibshirani RJ, Barber RF, Cand\u00e8s EJ, Ramdas A (2019) Conformal prediction under covariate shift. In: NeurIPS. pp. 2526\u20132536"},{"key":"881_CR33","unstructured":"Zeni G, Fontana M Vantini S (2020) Conformal prediction: a unified review of theory and new challenges. arXiv:abs\/2005.07972"},{"key":"881_CR34","unstructured":"Neal B, Huang C, Raghupathi S (2020) Realcause: Realistic causal inference benchmarking. arXiv:abs\/2011.15007"},{"key":"881_CR35","unstructured":"Prokhorenkova LO, Gusev G, Vorobev A, Dorogush AV, Gulin A (2018) Catboost: unbiased boosting with categorical features. In: NeurIPS (2018)"},{"key":"881_CR36","doi-asserted-by":"crossref","unstructured":"Fox J, Weisberg S (2002) Cox proportional-hazards regression for survival data. An R and S-PLUS companion to applied regression 2002","DOI":"10.32614\/CRAN.package.car"},{"issue":"1\u20132","key":"881_CR37","first-page":"9","volume":"74","author":"V Vovk","year":"2015","unstructured":"Vovk V (2015) Cross-conformal predictors. Ann Math. Artif Intell 74(1\u20132):9\u201328","journal-title":"Artif Intell"},{"key":"881_CR38","unstructured":"Schulman J, Wolski F, Dhariwal P, Radford A, Klimov O (2017) Proximal policy optimization algorithms. arXiv:abs\/1707.06347"}],"container-title":["KI - K\u00fcnstliche Intelligenz"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13218-024-00881-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13218-024-00881-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13218-024-00881-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T03:03:16Z","timestamp":1757127796000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13218-024-00881-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,23]]},"references-count":38,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["881"],"URL":"https:\/\/doi.org\/10.1007\/s13218-024-00881-6","relation":{},"ISSN":["0933-1875","1610-1987"],"issn-type":[{"value":"0933-1875","type":"print"},{"value":"1610-1987","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,23]]},"assertion":[{"value":"1 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}