{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T11:55:06Z","timestamp":1775562906024,"version":"3.50.1"},"reference-count":15,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:00:00Z","timestamp":1772582400000},"content-version":"vor","delay-in-days":62,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Procedia Computer Science"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1016\/j.procs.2026.03.064","type":"journal-article","created":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T12:39:40Z","timestamp":1774355980000},"page":"898-905","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Predicting Order Activity Sequence Using Contextual Process Mining"],"prefix":"10.1016","volume":"278","author":[{"given":"Diogo A.","family":"Barbeiro","sequence":"first","affiliation":[]},{"given":"Ricardo F.G.","family":"Martinho","sequence":"additional","affiliation":[]},{"given":"Carlos J.R.","family":"Ferreira","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.procs.2026.03.064_bib1","doi-asserted-by":"crossref","unstructured":"Tax, Niek, Ilya Verenich, Marcello La Rosa, and Marlon Dumas. (2017) \u201cPredictive business process monitoring with LSTM neural networks.\u201d In Lecture Notes in Computer Science, Vol. 10253, pp. 477\u2013492. Springer. doi: 10.1007\/978-3-319-59536-830.","DOI":"10.1007\/978-3-319-59536-8_30"},{"key":"10.1016\/j.procs.2026.03.064_bib2","doi-asserted-by":"crossref","unstructured":"Marquez-Chamorro, Alfonso Eduardo, Manuel Resinas, and Antonio Ruiz-Cortes. (2018) \u201cPredictive Monitoring of Business Processes: A Survey.\u201d IEEE Transactions on Services Computing 11(6). doi: 10.1109\/TSC.2017.2772256.","DOI":"10.1109\/TSC.2017.2772256"},{"key":"10.1016\/j.procs.2026.03.064_bib3","doi-asserted-by":"crossref","unstructured":"Petropoulos, Fotios, Daniele Apiletti, Vassilios Assimakopoulos, Mohamed Zied Babai, Devon K. Barrow, Souhaib Ben Taieb, Christoph Bergmeir, Ricardo J. Bessa, Jakub Bijak, John E. Boylan, and others. (2022) \u201cForecasting: theory and practice.\u201d International Journal of Forecasting. Elsevier. doi: 10.1016\/j.ijforecast.2021.11.001.","DOI":"10.1016\/j.ijforecast.2021.11.001"},{"key":"10.1016\/j.procs.2026.03.064_bib4","doi-asserted-by":"crossref","unstructured":"Gunnarsson, Bjorn Rafn, Seppe K. L. M. Vanden Broucke, and Jochen De Weerdt. (2019) \u201cPredictive Process Monitoring in Operational\u00a8 Logistics: A Case Study in Aviation.\u201d Unpublished manuscript.","DOI":"10.1007\/978-3-030-37453-2_21"},{"key":"10.1016\/j.procs.2026.03.064_bib5","unstructured":"Berti, Alessandro, Sebastiaan J. van Zelst, and Wil M. P. van der Aalst. (2020) \u201cPM4Py: A Process Mining Library for Python.\u201d In Business Process Management Workshops, pp. 169\u2013179. Springer. doi: 10.1007\/978-3-030-66498-513."},{"key":"10.1016\/j.procs.2026.03.064_bib6","doi-asserted-by":"crossref","unstructured":"Badakhshan, Peyman, Bastian Wurm, Thomas Grisold, Jerome Geyer-Klingeberg, Jan Mendling, and Jan vom Brocke. (2022) \u201cCreating Business Value with Process Mining.\u201d The Journal of Strategic Information Systems 31: 101745. Elsevier. doi: 10.1016\/j.jsis.2022.101745.","DOI":"10.1016\/j.jsis.2022.101745"},{"key":"10.1016\/j.procs.2026.03.064_bib7","doi-asserted-by":"crossref","unstructured":"Berti, Alessandro, Sebastiaan van Zelst, and Daniel Schuster. (2023) \u201cPM4Py: A process mining library for Python.\u201d Software Impacts 17: 100556. Elsevier B.V. doi: 10.1016\/j.simpa.2023.100556.","DOI":"10.1016\/j.simpa.2023.100556"},{"key":"10.1016\/j.procs.2026.03.064_bib8","doi-asserted-by":"crossref","unstructured":"Di Francescomarino, Chiara, Marlon Dumas, Alessandro Federici, Fabrizio Maria Maggi, and Ivana Teinemaa. (2017) \u201cPredictive process monitoring methods: Which one suits me best?\u201d Business Process Management Journal. Emerald Publishing Limited.","DOI":"10.1007\/978-3-319-98648-7_27"},{"key":"10.1016\/j.procs.2026.03.064_bib9","series-title":"\u201cOutcome-oriented predictive process monitoring: Review and benchmark.\u201d ACM Transactions on Knowledge Discovery from Data (TKDD) 13 (2): 1\u201357","author":"Teinemaa","year":"2019"},{"key":"10.1016\/j.procs.2026.03.064_bib10","doi-asserted-by":"crossref","unstructured":"Elkhawaga, Ghada, Mervat Abuelkheir, and Manfred Reichert. (2022) \u201cExplainability of Predictive Process Monitoring Results: Can You See My Data Issues?\u201d arXiv preprint arXiv:2202.08041.","DOI":"10.3390\/app12168192"},{"key":"10.1016\/j.procs.2026.03.064_bib11","series-title":"\u201cContextual Factors for Predictive Monitoring of Order Fulfillment Times.\u201d In\u00a8 Lecture Notes in Business Information Processing, Vol. 308","author":"Intayoad","year":"2018"},{"key":"10.1016\/j.procs.2026.03.064_bib12","first-page":"1303","article-title":"\u201cContext-Aware Predictive Process Monitoring in Smart Manufacturing.\u201d In\u00a8","author":"Becker","year":"2018","journal-title":"Proceedings of the 51st Hawaii International Conference on System Sciences"},{"key":"10.1016\/j.procs.2026.03.064_bib13","series-title":"\u201cContext-Aware Predictive Process Monitoring Under Dynamic Workload Conditions.\u201d Information Systems, 118: 103199","author":"Al-Jebrni","year":"2023"},{"key":"10.1016\/j.procs.2026.03.064_bib14","doi-asserted-by":"crossref","unstructured":"Hevner, Alan R., Salvatore T. March, Jinsoo Park, and Sudha Ram. (2004) \u201cDesign Science in Information Systems Research.\u201d MIS Quarterly 28 (1): 75\u2013105. JSTOR. doi: 10.2307\/25148625.","DOI":"10.2307\/25148625"},{"key":"10.1016\/j.procs.2026.03.064_bib15","unstructured":"Campos, and [Co-authors, if applicable]. (2025) \u201cPredictive Process Monitoring for Logistics: Context-Aware Timestamp and Activity Prediction.\u201d In Proceedings of the DCE 2025 Conference. To appear."}],"container-title":["Procedia Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1877050926006563?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1877050926006563?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T11:09:07Z","timestamp":1775560147000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1877050926006563"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":15,"alternative-id":["S1877050926006563"],"URL":"https:\/\/doi.org\/10.1016\/j.procs.2026.03.064","relation":{},"ISSN":["1877-0509"],"issn-type":[{"value":"1877-0509","type":"print"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Predicting Order Activity Sequence Using Contextual Process Mining","name":"articletitle","label":"Article Title"},{"value":"Procedia Computer Science","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.procs.2026.03.064","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}]}}