{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T03:08:57Z","timestamp":1770692937078,"version":"3.49.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,3,14]],"date-time":"2025-03-14T00:00:00Z","timestamp":1741910400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,14]],"date-time":"2025-03-14T00:00:00Z","timestamp":1741910400000},"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":["K\u00fcnstl Intell"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s13218-025-00887-8","type":"journal-article","created":{"date-parts":[[2025,3,14]],"date-time":"2025-03-14T01:16:42Z","timestamp":1741915002000},"page":"141-160","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Predictive Process Monitoring for Airport Operational Support"],"prefix":"10.1007","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5062-5354","authenticated-orcid":false,"given":"Bj\u00f6rn Rafn","family":"Gunnarsson","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seppe","family":"vanden Broucke","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thibault","family":"Verhoeven","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jochen","family":"De Weerdt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,14]]},"reference":[{"key":"887_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jairtraman.2020.101766","volume":"83","author":"GC Bezerra","year":"2020","unstructured":"Bezerra GC, Gomes CF (2020) Antecedents and consequences of passenger satisfaction with the airport. J Air Transp Manag 83:101766","journal-title":"J Air Transp Manag"},{"key":"887_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jairtraman.2022.102298","volume":"105","author":"L Li","year":"2022","unstructured":"Li L, Mao Y, Wang Y, Ma Z (2022) How has airport service quality changed in the context of covid-19: a data-driven crowdsourcing approach based on sentiment analysis. J Air Transp Manag 105:102298","journal-title":"J Air Transp Manag"},{"key":"887_CR3","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.jretconser.2018.10.006","volume":"47","author":"C Prentice","year":"2019","unstructured":"Prentice C, Kadan M (2019) The role of airport service quality in airport and destination choice. J Retail Consum Serv 47:40\u201348","journal-title":"J Retail Consum Serv"},{"issue":"2","key":"887_CR4","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/S0167-9236(01)00139-7","volume":"33","author":"JP Shim","year":"2002","unstructured":"Shim JP, Warkentin M, Courtney JF, Power DJ, Sharda R, Carlsson C (2002) Past, present, and future of decision support technology. Decis Support Syst 33(2):111\u2013126","journal-title":"Decis Support Syst"},{"key":"887_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jengtecman.2022.101697","volume":"65","author":"R Ahmed","year":"2022","unstructured":"Ahmed R, Shaheen S, Philbin SP (2022) The role of big data analytics and decision-making in achieving project success. J Eng Technol Manag 65:101697","journal-title":"J Eng Technol Manag"},{"key":"887_CR6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-49851-4","volume-title":"Process mining: data science in action","author":"W van Der Aalst","year":"2016","unstructured":"van Der Aalst W (2016) Process mining: data science in action. Springer, Berlin"},{"issue":"6","key":"887_CR7","doi-asserted-by":"publisher","first-page":"962","DOI":"10.1109\/TSC.2017.2772256","volume":"11","author":"AE M\u00e1rquez-Chamorro","year":"2017","unstructured":"M\u00e1rquez-Chamorro AE, Resinas M, Ruiz-Cort\u00e9s A (2017) Predictive monitoring of business processes: a survey. IEEE Trans Serv Comput 11(6):962\u2013977","journal-title":"IEEE Trans Serv Comput"},{"key":"887_CR8","doi-asserted-by":"crossref","unstructured":"Khosravi A, Nahavandi S, Creighton D (2009) Estimating performance indexes of a baggage handling system using metamodels. In: 2009 IEEE International Conference on Industrial Technology, pp. 1\u20136. IEEE","DOI":"10.1109\/ICIT.2009.4939626"},{"key":"887_CR9","doi-asserted-by":"crossref","unstructured":"Ahmed T, Pedersen TB, Calders T, Lu H (2016) Online risk prediction for indoor moving objects. In: 2016 17th IEEE International Conference on Mobile Data Management (MDM), 1, pp. 102\u2013111. IEEE","DOI":"10.1109\/MDM.2016.27"},{"key":"887_CR10","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.jairtraman.2016.08.017","volume":"58","author":"G Kim","year":"2017","unstructured":"Kim G, Kim J, Chae J (2017) Balancing the baggage handling performance of a check-in area shared by multiple airlines. J Air Transp Manag 58:31\u201349","journal-title":"J Air Transp Manag"},{"key":"887_CR11","doi-asserted-by":"crossref","unstructured":"Nahavandi S, Gunn B, Johnstone M, Creighton D (2018) Modelling and simulation of large and complex systems for airport baggage handling security. In: Science and Information Conference, pp. 1055\u20131067. Springer","DOI":"10.1007\/978-3-030-01174-1_80"},{"key":"887_CR12","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/j.trpro.2018.12.008","volume":"35","author":"C Malandri","year":"2018","unstructured":"Malandri C, Briccoli M, Mantecchini L, Paganelli F (2018) A discrete event simulation model for inbound baggage handling. Transp Res Procedia 35:295\u2013304","journal-title":"Transp Res Procedia"},{"key":"887_CR13","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.trc.2018.01.009","volume":"88","author":"J Skorupski","year":"2018","unstructured":"Skorupski J, Uchro\u0144ski P, \u0141ach A (2018) A method of hold baggage security screening system throughput analysis with an application for a medium-sized airport. Transp Res Part C Emerg Technol 88:52\u201373","journal-title":"Transp Res Part C Emerg Technol"},{"key":"887_CR14","doi-asserted-by":"crossref","unstructured":"van\u00a0der Aalst WM (2011) Intra-and inter-organizational process mining: Discovering processes within and between organizations. In: IFIP Working Conference on The Practice of Enterprise Modeling, pp 1\u201311. Springer","DOI":"10.1007\/978-3-642-24849-8_1"},{"key":"887_CR15","doi-asserted-by":"crossref","unstructured":"Di\u00a0Francescomarino C, Ghidini C, Maggi FM, Milani F (2018) Predictive process monitoring methods: Which one suits me best? In: International Conference on Business Process Management, pp. 462\u2013479. Springer","DOI":"10.1007\/978-3-319-98648-7_27"},{"key":"887_CR16","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1007\/978-3-031-08848-3_10","volume-title":"Process mining handbook. Lecture notes in business information processing","author":"C Di Francescomarino","year":"2022","unstructured":"Di Francescomarino C, Ghidini C (2022) Predictive process monitoring. In: van der Aalst WMP, Carmona J (eds) Process mining handbook. Lecture notes in business information processing, vol 448. Springer, Cham, pp 320\u2013346"},{"key":"887_CR17","first-page":"739","volume":"16","author":"E Rama-Maneiro","year":"2021","unstructured":"Rama-Maneiro E, Vidal J, Lama M (2021) Deep learning for predictive business process monitoring: review and benchmark. IEEE Trans Serv Comput 16:739\u2013756","journal-title":"IEEE Trans Serv Comput"},{"issue":"2","key":"887_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3301300","volume":"13","author":"I Teinemaa","year":"2019","unstructured":"Teinemaa I, Dumas M, Rosa ML, Maggi FM (2019) Outcome-oriented predictive process monitoring: review and benchmark. ACM Trans Knowl Discov Data (TKDD) 13(2):1\u201357","journal-title":"ACM Trans Knowl Discov Data (TKDD)"},{"issue":"6","key":"887_CR19","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1109\/TSC.2016.2645153","volume":"12","author":"C Di Francescomarino","year":"2016","unstructured":"Di Francescomarino C, Dumas M, Maggi FM, Teinemaa I (2016) Clustering-based predictive process monitoring. IEEE Trans Serv Comput 12(6):896\u2013909","journal-title":"IEEE Trans Serv Comput"},{"issue":"2","key":"887_CR20","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1016\/j.is.2010.09.001","volume":"36","author":"WM van der Aalst","year":"2011","unstructured":"van der Aalst WM, Schonenberg MH, Song M (2011) Time prediction based on process mining. Inf Syst 36(2):450\u2013475","journal-title":"Inf Syst"},{"key":"887_CR21","doi-asserted-by":"crossref","unstructured":"Tax N, Verenich I, La\u00a0Rosa M, Dumas M (2017) Predictive business process monitoring with lstm neural networks. In: International Conference on Advanced Information Systems Engineering, pp. 477\u2013492. Springer","DOI":"10.1007\/978-3-319-59536-8_30"},{"key":"887_CR22","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.dss.2017.04.003","volume":"100","author":"J Evermann","year":"2017","unstructured":"Evermann J, Rehse JR, Fettke P (2017) Predicting process behaviour using deep learning. Decis Support Syst 100:129\u2013140","journal-title":"Decis Support Syst"},{"key":"887_CR23","doi-asserted-by":"crossref","unstructured":"Camargo M, Dumas M, Gonz\u00e1lez-Rojas O (2019) Learning accurate lstm models of business processes. In: International Conference on Business Process Management, pp. 286\u2013302. Springer","DOI":"10.1007\/978-3-030-26619-6_19"},{"key":"887_CR24","doi-asserted-by":"crossref","unstructured":"Denisov V, Fahland D, van\u00a0der Aalst WM (2019) Predictive performance monitoring of material handling systems using the performance spectrum. In: 2019 International Conference on Process Mining (ICPM), pp. 137\u2013144. IEEE","DOI":"10.1109\/ICPM.2019.00029"},{"key":"887_CR25","doi-asserted-by":"crossref","unstructured":"Denisov V, Fahland D, van\u00a0der Aalst WM (2018) Unbiased, fine-grained description of processes performance from event data. In: International Conference on Business Process Management, pp. 139\u2013157. Springer","DOI":"10.1007\/978-3-319-98648-7_9"},{"key":"887_CR26","doi-asserted-by":"crossref","unstructured":"Denisov V, Fahland D, van\u00a0der Aalst WM (2020) Repairing event logs with missing events to support performance analysis of systems with shared resources. In: International Conference on Applications and Theory of Petri Nets and Concurrency, pp. 239\u2013259. Springer","DOI":"10.1007\/978-3-030-51831-8_12"},{"key":"887_CR27","doi-asserted-by":"publisher","first-page":"2330","DOI":"10.1109\/TSC.2023.3245726","volume":"16","author":"BR Gunnarsson","year":"2023","unstructured":"Gunnarsson BR, van den Broucke S, De Weerdt J (2023) A direct data aware LSTM neural network architecture for complete remaining trace and runtime prediction. IEEE Trans Serv Comput 16:2330\u20132342","journal-title":"IEEE Trans Serv Comput"},{"issue":"1","key":"887_CR28","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1145\/375360.375365","volume":"33","author":"G Navarro","year":"2001","unstructured":"Navarro G (2001) A guided tour to approximate string matching. ACM Comput Surv (CSUR) 33(1):31\u201388","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"9","key":"887_CR29","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1007\/s00607-018-0593-x","volume":"100","author":"M Polato","year":"2018","unstructured":"Polato M, Sperduti A, Burattin A, de Leoni M (2018) Time and activity sequence prediction of business process instances. Computing 100(9):1005\u20131031","journal-title":"Computing"},{"issue":"1","key":"887_CR30","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"key":"887_CR31","doi-asserted-by":"crossref","unstructured":"Wang Y, Huang M, Zhu X, Zhao L (2016) Attention-based lstm for aspect-level sentiment classification. In: Proceedings of the 2016 conference on empirical methods in natural language processing, pp. 606\u2013615","DOI":"10.18653\/v1\/D16-1058"},{"key":"887_CR32","doi-asserted-by":"crossref","unstructured":"Adams JN, Park G, Levich S, Schuster D, van\u00a0der Aalst WM (2022) A framework for extracting and encoding features from object-centric event data. In: International Conference on Service-Oriented Computing, pp. 36\u201353. Springer","DOI":"10.1007\/978-3-031-20984-0_3"},{"key":"887_CR33","doi-asserted-by":"crossref","unstructured":"Adams JN, van Der\u00a0Aalst WM (2021) Precision and fitness in object-centric process mining. In: 2021 3rd International Conference on Process Mining (ICPM), pp. 128\u2013135. IEEE","DOI":"10.1109\/ICPM53251.2021.9576886"},{"key":"887_CR34","doi-asserted-by":"crossref","unstructured":"Maisenbacher M, Weidlich M (2017) Handling concept drift in predictive process monitoring. In: 2017 IEEE International Conference on Services Computing (SCC), pp. 1\u20138. IEEE","DOI":"10.1109\/SCC.2017.10"},{"issue":"5","key":"887_CR35","doi-asserted-by":"publisher","first-page":"1385","DOI":"10.1007\/s10115-022-01666-9","volume":"64","author":"W Rizzi","year":"2022","unstructured":"Rizzi W, Di Francescomarino C, Ghidini C, Maggi FM (2022) How do i update my model? On the resilience of predictive process monitoring models to change. Knowl Inf Syst 64(5):1385\u20131416","journal-title":"Knowl Inf Syst"}],"container-title":["KI - K\u00fcnstliche Intelligenz"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13218-025-00887-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13218-025-00887-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13218-025-00887-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T07:39:53Z","timestamp":1757144393000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13218-025-00887-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,14]]},"references-count":35,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["887"],"URL":"https:\/\/doi.org\/10.1007\/s13218-025-00887-8","relation":{},"ISSN":["0933-1875","1610-1987"],"issn-type":[{"value":"0933-1875","type":"print"},{"value":"1610-1987","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,14]]},"assertion":[{"value":"1 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}