{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T16:10:10Z","timestamp":1791907810553,"version":"4.3.4"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,12,30]],"date-time":"2021-12-30T00:00:00Z","timestamp":1640822400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,12,30]],"date-time":"2021-12-30T00:00:00Z","timestamp":1640822400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100005189","name":"Estonian Research Competency Council","doi-asserted-by":"publisher","award":["IUT20-55"],"award-info":[{"award-number":["IUT20-55"]}],"id":[{"id":"10.13039\/501100005189","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010663","name":"H2020 European Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2022,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Predictive process monitoring is a family of techniques to analyze events produced during the execution of a business process in order to predict the future state or the final outcome of running process instances. Existing techniques in this field are able to predict, at each step of a process instance, the likelihood that it will lead to an undesired outcome. These techniques, however, focus on generating predictions and do not prescribe when and how process workers should intervene to decrease the cost of undesired outcomes. This paper proposes a framework for prescriptive process monitoring, which extends predictive monitoring with the ability to generate alarms that trigger interventions to prevent an undesired outcome or mitigate its effect. The framework incorporates a parameterized cost model to assess the cost\u2013benefit trade-off of generating alarms. We show how to optimize the generation of alarms given an event log of past process executions and a set of cost model parameters. The proposed approaches are empirically evaluated using a range of real-life event logs. The experimental results show that the net cost of undesired outcomes can be minimized by changing the threshold for generating alarms, as the process instance progresses. Moreover, introducing delays for triggering alarms, instead of triggering them as soon as the probability of an undesired outcome exceeds a threshold, leads to lower net costs.<\/jats:p>","DOI":"10.1007\/s10115-021-01633-w","type":"journal-article","created":{"date-parts":[[2021,12,30]],"date-time":"2021-12-30T03:02:13Z","timestamp":1640833333000},"page":"559-587","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Fire now, fire later: alarm-based systems for prescriptive process monitoring"],"prefix":"10.1007","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1863-8390","authenticated-orcid":false,"given":"Stephan A.","family":"Fahrenkrog-Petersen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7239-5206","authenticated-orcid":false,"given":"Niek","family":"Tax","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8045-8238","authenticated-orcid":false,"given":"Irene","family":"Teinemaa","sequence":"additional","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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8447-5374","authenticated-orcid":false,"given":"Massimiliano de","family":"Leoni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9089-6896","authenticated-orcid":false,"given":"Fabrizio Maria","family":"Maggi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3325-7227","authenticated-orcid":false,"given":"Matthias","family":"Weidlich","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,12,30]]},"reference":[{"key":"1633_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-49851-4","volume-title":"Process mining\u2014data science in action","author":"WMP van der Aalst","year":"2016","unstructured":"van der Aalst WMP (2016) Process mining\u2014data science in action, 2nd edn. Springer, Berlin","edition":"2"},{"key":"1633_CR2","first-page":"113","volume":"1","author":"EL Allwein","year":"2000","unstructured":"Allwein EL, Schapire RE, Singer Y (2000) Reducing multiclass to binary: a unifying approach for margin classifiers. J Mach Learn Res 1:113\u2013141","journal-title":"J Mach Learn Res"},{"key":"1633_CR3","unstructured":"Bergstra JS, Bardenet R, Bengio Y, K\u00e9gl B (2011) Algorithms for hyper-parameter optimization. In: Proc. of NIPS, pp 2546\u20132554"},{"key":"1633_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.dss.2014.10.006","volume":"69","author":"R Conforti","year":"2015","unstructured":"Conforti R, de Leoni M, La Rosa M, van der Aalst WM, ter Hofstede AH (2015) A recommendation system for predicting risks across multiple business process instances. Decis Support Syst 69:1\u201319","journal-title":"Decis Support Syst"},{"key":"1633_CR5","doi-asserted-by":"crossref","unstructured":"Dachraoui A, Bondu A, Cornu\u00e9jols A (2015) Early classification of time series as a non myopic sequential decision making problem. In: ECML PKDD, pp. 433\u2013447. Springer","DOI":"10.1007\/978-3-319-23528-8_27"},{"key":"1633_CR6","doi-asserted-by":"crossref","unstructured":"Dees M, de Leoni M, Mannhardt F (2017 ) Enhancing process models to improve business performance: A methodology and case studies. In: Proc. of CoopIS, pp 232\u2013251. Springer","DOI":"10.1007\/978-3-319-69462-7_15"},{"key":"1633_CR7","first-page":"909","volume":"12","author":"C Di Francescomarino","year":"2017","unstructured":"Di Francescomarino C, Dumas M, Maggi FM, Teinemaa I (2017) Clustering-based predictive process monitoring. IEEE Trans Serv Comput 12:909","journal-title":"IEEE Trans Serv Comput"},{"key":"1633_CR8","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1346","volume-title":"Extraction, correlation, and abstraction of event data for process mining","author":"K Diba","year":"2020","unstructured":"Diba K, Batoulis K, Weidlich M, Weske M (2020) Extraction, correlation, and abstraction of event data for process mining. Rev Data Min Knowl Discov, Wiley Interdiscip. https:\/\/doi.org\/10.1002\/widm.1346"},{"key":"1633_CR9","doi-asserted-by":"crossref","unstructured":"Domingos P(1999) Metacost: a general method for making classifiers cost-sensitive. In: Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, pp 155\u2013164. ACM","DOI":"10.1145\/312129.312220"},{"key":"1633_CR10","unstructured":"Elkan C (2001) The foundations of cost-sensitive learning. In: Proc. of IJCAI, pp 973\u2013978. Morgan Kaufmann"},{"key":"1633_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.is.2017.11.001","volume":"73","author":"B Fazzinga","year":"2018","unstructured":"Fazzinga B, Flesca S, Furfaro F, Masciari E, Pontieri L (2018) Efficiently interpreting traces of low level events in business process logs. Inf Syst 73:1\u201324","journal-title":"Inf Syst"},{"key":"1633_CR12","doi-asserted-by":"publisher","first-page":"113,386","DOI":"10.1016\/j.eswa.2020.113386","volume":"153","author":"B Fazzinga","year":"2020","unstructured":"Fazzinga B, Folino F, Furfaro F, Pontieri L (2020) An ensemble-based approach to the security-oriented classification of low-level log traces. Expert Syst Appl 153:113,386","journal-title":"Expert Syst Appl"},{"issue":"1","key":"1633_CR13","first-page":"3133","volume":"15","author":"M Fern\u00e1ndez-Delgado","year":"2014","unstructured":"Fern\u00e1ndez-Delgado M, Cernadas E, Barro S, Amorim D (2014) Do we need hundreds of classifiers to solve real world classification problems. JMLR 15(1):3133\u20133181","journal-title":"JMLR"},{"key":"1633_CR14","doi-asserted-by":"crossref","unstructured":"Gr\u00f6ger C, Schwarz H, Mitschang B (2014) Prescriptive analytics for recommendation-based business process optimization. In: Proc. of BIS, pp 25\u201337. Springer","DOI":"10.1007\/978-3-319-06695-0_3"},{"issue":"4","key":"1633_CR15","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1007\/s12599-015-0412-2","volume":"58","author":"J Krumeich","year":"2016","unstructured":"Krumeich J, Werth D, Loos P (2016) Prescriptive control of business processes. Bus Inf Syst Eng 58(4):261\u2013280","journal-title":"Bus Inf Syst Eng"},{"key":"1633_CR16","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.is.2015.07.003","volume":"56","author":"M de Leoni","year":"2016","unstructured":"de Leoni M, van der Aalst WMP, Dees M (2016) A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs. Inf Syst 56:235\u2013257","journal-title":"Inf Syst"},{"key":"1633_CR17","doi-asserted-by":"crossref","unstructured":"Maggi FM, Di Francescomarino C, Dumas M, Ghidini C (2014) Predictive monitoring of business processes. In: Proc. of CAiSE, pp 457\u2013472. Springer","DOI":"10.1007\/978-3-319-07881-6_31"},{"key":"1633_CR18","doi-asserted-by":"crossref","unstructured":"Metzger A, F\u00f6cker F (2017) Predictive business process monitoring considering reliability estimates. In: Proc. of CAiSE, pp 445\u2013460. Springer","DOI":"10.1007\/978-3-319-59536-8_28"},{"issue":"2","key":"1633_CR19","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1109\/TSMC.2014.2347265","volume":"45","author":"A Metzger","year":"2015","unstructured":"Metzger A, Leitner P, Ivanovic D, Schmieders E, Franklin R, Carro M, Dustdar S, Pohl K (2015) Comparing and combining predictive business process monitoring techniques. IEEE Trans Syst Man Cybern Syst 45(2):276\u2013290","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"1633_CR20","doi-asserted-by":"crossref","unstructured":"Metzger A, Neubauer A, Bohn P, Pohl K (2019) Proactive process adaptation using deep learning ensembles. In: Advanced information systems engineering - 31st international conference, CAiSE 2019, Rome, Italy, June 3\u20137, 2019, Proceedings, pp 547\u2013562","DOI":"10.1007\/978-3-030-21290-2_34"},{"key":"1633_CR21","first-page":"1","volume":"99","author":"U Mori","year":"2017","unstructured":"Mori U, Mendiburu A, Dasgupta S, Lozano JA (2017) Early classification of time series by simultaneously optimizing the accuracy and earliness. IEEE Trans Neural Netw Learn Syst 99:1\u201310","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"1","key":"1633_CR22","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/s10618-016-0462-1","volume":"31","author":"U Mori","year":"2017","unstructured":"Mori U, Mendiburu A, Keogh E, Lozano JA (2017) Reliable early classification of time series based on discriminating the classes over time. Data Min Knowl Disc 31(1):233\u2013263","journal-title":"Data Min Knowl Disc"},{"key":"1633_CR23","doi-asserted-by":"crossref","unstructured":"Nguyen A, Chatterjee S, Weinzierl S, Schwinn L, Matzner M, Eskofier B.M (2020)Time matters: time-aware lstms for predictive business process monitoring. In: Leemans SJJ, Leopold H (eds) Process Mining Workshops - ICPM 2020 international workshops, Padua, Italy, October 5\u20138, 2020, revised selected papers, Lecture Notes in Business Information Processing, vol. 406, pp. 112\u2013123. Springer","DOI":"10.1007\/978-3-030-72693-5_9"},{"key":"1633_CR24","doi-asserted-by":"crossref","unstructured":"Olson RS, La Cava W, Mustahsan Z, Varik A, Moore JH (2017) Data-driven advice for applying machine learning to bioinformatics problems. In Proc. of biocomputing, pp 192\u2013203. World Scientific","DOI":"10.1142\/9789813235533_0018"},{"issue":"1","key":"1633_CR25","first-page":"3561","volume":"14","author":"N Parrish","year":"2013","unstructured":"Parrish N, Anderson HS, Gupta MR, Hsiao DY (2013) Classifying with confidence from incomplete information. J Mach Learn Res 14(1):3561\u20133589","journal-title":"J Mach Learn Res"},{"key":"1633_CR26","doi-asserted-by":"crossref","unstructured":"Pednault E, Abe N, Zadrozny B (2002) Sequential cost-sensitive decision making with reinforcement learning. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp 259\u2013268. ACM","DOI":"10.1145\/775047.775086"},{"issue":"3","key":"1633_CR27","first-page":"61","volume":"10","author":"J Platt","year":"1999","unstructured":"Platt J et al (1999) Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Adv Large Margin Classif 10(3):61\u201374","journal-title":"Adv Large Margin Classif"},{"key":"1633_CR28","unstructured":"Sheng VS, Ling CX (2006) Thresholding for making classifiers cost-sensitive. In: AAAI, pp 476\u2013481"},{"key":"1633_CR29","doi-asserted-by":"crossref","unstructured":"Tavenard R, Malinowski S (2016) Cost-aware early classification of time series. In: ECML PKDD, pp 632\u2013647. Springer","DOI":"10.1007\/978-3-319-46128-1_40"},{"issue":"2","key":"1633_CR30","doi-asserted-by":"publisher","first-page":"17:1","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 13(2):17:1-17:57","journal-title":"ACM Trans Knowl Discov Data"},{"key":"1633_CR31","doi-asserted-by":"crossref","unstructured":"Teinemaa I, Tax N, de Leoni M, Dumas M, Maggi FM (2018) Alarm-based prescriptive process monitoring. In: Proceedings of the business process management forum (BPM Forum), pp 91\u2013107. Springer","DOI":"10.1007\/978-3-319-98651-7_6"},{"issue":"3","key":"1633_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/jdwm.2007070101","volume":"3","author":"G Tsoumakas","year":"2007","unstructured":"Tsoumakas G, Katakis I (2007) Multi-label classification: An overview. Int J Data Warehous Min 3(3):1\u201313","journal-title":"Int J Data Warehous Min"},{"key":"1633_CR33","doi-asserted-by":"crossref","unstructured":"Weinzierl S, Dunzer S, Zilker S, Matzner M (2020) Prescriptive business process monitoring for recommending next best actions. In: Fahland D, Ghidini C, Becker J, Dumas M (eds) Business process management forum - BPM forum 2020, Seville, Spain, September 13-18, 2020, Proceedings, lecture notes in business information processing, vol 392, pp 193\u2013209. Springer","DOI":"10.1007\/978-3-030-58638-6_12"},{"issue":"1","key":"1633_CR34","first-page":"105","volume":"31","author":"Z Xing","year":"2012","unstructured":"Xing Z, Pei J, Philip SY (2012) Early classification on time series. KAIS 31(1):105\u2013127","journal-title":"KAIS"},{"key":"1633_CR35","doi-asserted-by":"crossref","unstructured":"Zadrozny B, Elkan C (2001) Learning and making decisions when costs and probabilities are both unknown. In: Proc. of KDD, pp 204\u2013213. ACM","DOI":"10.1145\/502512.502540"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-021-01633-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-021-01633-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-021-01633-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T05:07:18Z","timestamp":1645420038000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-021-01633-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,30]]},"references-count":35,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,2]]}},"alternative-id":["1633"],"URL":"https:\/\/doi.org\/10.1007\/s10115-021-01633-w","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,30]]},"assertion":[{"value":"14 October 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 December 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}