{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:26:22Z","timestamp":1754155582069,"version":"3.41.2"},"reference-count":27,"publisher":"Emerald","issue":"7","license":[{"start":{"date-parts":[[2013,9,2]],"date-time":"2013-09-02T00:00:00Z","timestamp":1378080000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,9,2]]},"abstract":"<jats:sec>\n               <jats:title content-type=\"abstract-heading\">Purpose<\/jats:title>\n               <jats:p> \u2013 Process mining provides a new means to improve processes in a variety of application domains. The purpose of this paper is to abstract a process model and then use the discovered models from process mining to make useful optimization via predictions. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Design\/methodology\/approach<\/jats:title>\n               <jats:p> \u2013 The paper divides the process model into a combination of \u201cpair-adjacent activities\u201d and \u201cpair-adjacent persons\u201d in the event logs. First, two new handover process models based on adjacency matrix are proposed. Second, by adding the stage, frequency, and time for every activity or person into the matrix, another two new handover prediction process models based on stage adjacency matrix are further proposed. Third, compute the conditional probability from every stage to next stage through the frequency. Finally, use real data to analyze and demonstrate the practicality and effectiveness of the proposed handover optimization process. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Findings<\/jats:title>\n               <jats:p> \u2013 The process model can be extended with information to predict what will actually happen, how possible to reach the next activity, who will do this activity, and the corresponding probability if there are several people executing the same activity, etc. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title>\n               <jats:p> \u2013 The contribution of this paper is to predict what will actually happen, how possible it is to reach the following activities or persons in the next stage, how soon to reach the following activities or persons by calculating all the possible interval time via different traces, who will do this activity, and the corresponding probability if there are several people executing the same activity, etc.<\/jats:p>\n            <\/jats:sec>","DOI":"10.1108\/k-11-2012-0107","type":"journal-article","created":{"date-parts":[[2013,11,25]],"date-time":"2013-11-25T21:14:49Z","timestamp":1385414089000},"page":"1101-1127","source":"Crossref","is-referenced-by-count":0,"title":["Handover optimization in business processes via prediction"],"prefix":"10.1108","volume":"42","author":[{"given":"Jian","family":"Liu","sequence":"first","affiliation":[]},{"given":"Peng","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Sifeng","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yizhong","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Wensheng","family":"Yang","sequence":"additional","affiliation":[]}],"member":"140","reference":[{"key":"key2022020520355263700_b2","doi-asserted-by":"crossref","unstructured":"Agrawal, R.\n               , \n                  Gunopulos, D.\n                and \n                  Leymann, F.\n                (1998), \u201cMining process models from workflow logs\u201d, Sixth International Conference on Extending Database Technology, Proceedings on LNCS, Vol. 1377, Springer, Berlin, pp. 467-483.","DOI":"10.1007\/BFb0101003"},{"key":"key2022020520355263700_b1","doi-asserted-by":"crossref","unstructured":"Alves de Mdeoros, A.K.\n               , \n                  Weihters, A.J.M.M.\n                and \n                  Van der Aalst, W.M.P.\n                (2007), \u201cGenetic process mining: an experimental evaluation\u201d, Data Mining and Knowledge Discovery, Vol. 14 No. 2, pp. 245-304.","DOI":"10.1007\/s10618-006-0061-7"},{"key":"key2022020520355263700_b3","doi-asserted-by":"crossref","unstructured":"Bezerra, F.\n                and \n                  Wainer, J.\n                (2013), \u201cAlgorithms for anomaly detection of traces in logs of process aware information systems\u201d, Information Systems, Vol. 38 No. 1, pp. 33-44.","DOI":"10.1016\/j.is.2012.04.004"},{"key":"key2022020520355263700_b4","unstructured":"Buijs, J.C.A.M.\n                (2010), Mapping Data Sources to XES in Generic Way, Technische Universiteit Eindhoven University of Technology, Eindhoven."},{"key":"key2022020520355263700_b5","doi-asserted-by":"crossref","unstructured":"Cook, J.E.\n                and \n                  Wolf, A.L.\n                (1998), \u201cDiscovering models of software processes from event-based data\u201d, ACM Transactions on Software Engineering and Methodology, Vol. 7 No. 3, pp. 215-249.","DOI":"10.1145\/287000.287001"},{"key":"key2022020520355263700_b7","doi-asserted-by":"crossref","unstructured":"Datta, A.\n                (1998), \u201cAutomating the discovery of As-Is business process models: probabilistic and algorithmic approaches\u201d, Information Systems Research, Vol. 9 No. 3, pp. 275-301.","DOI":"10.1287\/isre.9.3.275"},{"key":"key2022020520355263700_b6","doi-asserted-by":"crossref","unstructured":"Dustdar, S.\n               , \n                  Hoffmann, T.\n                and \n                  Van der Aalst, W.M.P.\n                (2005), \u201cMining of ad-hoc business process with TeamLog\u201d, Data & Knowledge Engineering, Vol. 55 No. 2, pp. 129-158.","DOI":"10.1016\/j.datak.2005.02.002"},{"key":"key2022020520355263700_b8","doi-asserted-by":"crossref","unstructured":"Gunther, C.W.\n                and \n                  Van der Aalst, W.M.P.\n                (2007), \u201cFinding structure in unstructured processes: the case for process mining\u201d, Proceedings of the 7th International Conference on Application of Concurrency to System Design (ACSD 2007), Bratislava, Slovak Republic, 10-13 July, IEEE, Piscataway, NJ, pp. 3-12.","DOI":"10.1109\/ACSD.2007.50"},{"key":"key2022020520355263700_b10","doi-asserted-by":"crossref","unstructured":"Huang, Z.\n                and \n                  Kumar, A.\n                (2009), \u201cNew quality metrics for evaluating process models\u201d, Proceedings of the 4th WBPM, Stockholm, AAAI Press, Menlo Park, CA, pp. 52-57.","DOI":"10.1007\/978-3-642-00328-8_16"},{"key":"key2022020520355263700_b9","unstructured":"Huang, Z.\n                and \n                  Kumar, A.\n                (2011), \u201cA study of quality and accuracy trade-offs in process mining\u201d, INFORMS Journal on Computing, Vol. 10 No. 3, pp. 1-18."},{"key":"key2022020520355263700_b11","doi-asserted-by":"crossref","unstructured":"Klein, M.\n                and \n                  Bernstein, A.\n                (2004), \u201cTowards high-precision service retrieval\u201d, IEEE Internet Computing, Vol. 8 No. 1, pp. 30-36.","DOI":"10.1109\/MIC.2004.1260701"},{"key":"key2022020520355263700_b13","unstructured":"PROM\n                (2009), The Process Mining Group, Mathematics and Computer Science Department, Eindhoven University of Technology, available at: www.processmining.org\/prom\/downloads."},{"key":"key2022020520355263700_b14","doi-asserted-by":"crossref","unstructured":"Rozinat, A.\n                and \n                  Van der Aalst, W.M.P.\n                (2008), \u201cConformance checking of processes based on monitoring real behavior\u201d, Information Systems, Vol. 33 No. 1, 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                 Kumar, V.\n                (2006), Introduction to Data Mining, Addison Wesley, Reading, MA."},{"key":"key2022020520355263700_b22","doi-asserted-by":"crossref","unstructured":"Van der Aalst, W.M.P.\n                (2007), \u201cExploring the CSCW spectrum using process mining\u201d, Advanced Engineering Informatics, Vol. 21 No. 4, pp. 191-199.","DOI":"10.1016\/j.aei.2006.05.002"},{"key":"key2022020520355263700_b18","doi-asserted-by":"crossref","unstructured":"Van der Aalst, W.M.P.\n                (2011), Process Mining: Discovery, Conformance and Enhancement of Business Processes, Springer, Berlin.","DOI":"10.1007\/978-3-642-19345-3"},{"key":"key2022020520355263700_b19","doi-asserted-by":"crossref","unstructured":"Van der Aalst, W.M.P.\n               , \n                  Schonenberg, M.H.\n                and \n                  Song, M.\n                (2011), \u201cTime prediction based on process mining\u201d, Information Systems, Vol. 36 No. 2, pp. 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