{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T19:05:18Z","timestamp":1780340718198,"version":"3.54.1"},"reference-count":35,"publisher":"Emerald","issue":"6","license":[{"start":{"date-parts":[[2015,11,2]],"date-time":"2015-11-02T00:00:00Z","timestamp":1446422400000},"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":[[2015,11,2]]},"abstract":"<jats:sec>\n               <jats:title content-type=\"abstract-heading\">Purpose<\/jats:title>\n               <jats:p> \u2013 Process mining is a research area used to discover, monitor and improve real business processes by extracting knowledge from event logs available in process-aware information systems. The purpose of this paper is to evaluate the application of artificial neural networks (ANNs) and support vector machines (SVMs) in data mining tasks in the process mining context. The goal was to understand how these computational intelligence techniques are currently being applied in process mining. <\/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 authors conducted a systematic literature review with three research questions formulated to evaluate the use of ANNs and SVMs in process mining. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Findings<\/jats:title>\n               <jats:p> \u2013 The authors identified 11 papers as primary studies according to the criteria established in the review protocol. Most of them deal with process mining enhancement, mainly using ANNs. Regarding the data mining task, the authors identified three types of tasks used: categorical prediction (or classification); numeric prediction, considering the \u201cregression\u201d type, and clustering analysis. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title>\n               <jats:p> \u2013 Although there is scientific interest in process mining, little attention has been specifically given to ANNs and SVM. This scenario does not reflect the general context of data mining, where these two techniques are widely used. This low use may be possibly due to a relative lack of knowledge about their potential for this type of problem, which the authors seek to reverse with the completion of this study.<\/jats:p>\n            <\/jats:sec>","DOI":"10.1108\/bpmj-02-2015-0017","type":"journal-article","created":{"date-parts":[[2015,10,19]],"date-time":"2015-10-19T05:27:36Z","timestamp":1445232456000},"page":"1391-1415","source":"Crossref","is-referenced-by-count":21,"title":["Process mining through artificial neural networks and support vector machines"],"prefix":"10.1108","volume":"21","author":[{"given":"Ana Roc\u00edo C\u00e1rdenas","family":"Maita","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucas Corr\u00eaa","family":"Martins","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlos  Ram\u00f3n","family":"L\u00f3pez Paz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarajane Marques","family":"Peres","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcelo","family":"Fantinato","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"140","reference":[{"key":"key2020122001374987400_b1","unstructured":"Abony\u00ed, J.\n               , \n                  Feil, B.\n                and \n                  Abraham, A.\n                (2005), \u201cComputational intelligence in data mining\u201d, \n                  Informatica\n               , Vol. 29 No. 1, pp. 3-12."},{"key":"key2020122001374987400_b2","doi-asserted-by":"crossref","unstructured":"Alves, V.\n               , \n                  Niu, N.\n               , \n                  Alves, C.\n                and \n                  Valenca, G.\n                (2010), 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547-533.","DOI":"10.1016\/j.dss.2009.05.016"},{"key":"key2020122001374987400_b7","doi-asserted-by":"crossref","unstructured":"Cristianini, N.\n                and \n                  Shawe-Taylor, J.\n                (2000), \n                  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods\n               , Cambridge University Press, Cambridge.","DOI":"10.1017\/CBO9780511801389"},{"key":"key2020122001374987400_b8","doi-asserted-by":"crossref","unstructured":"Dyba, T.\n                and \n                  Dings\u00f8yr, T.\n                (2008), \u201cStrength of evidence in systematic reviews in software engineering\u201d, in \n                  Rombach, H.D.\n               , \n                  Elbaum, S.G.\n                and \n                  M\u00fcnch, J.\n                (Eds), Proceedings of the 2nd International Symposium on Empirical Software Engineering and Measurement, ESEM 2008, ACM Press, Kaiserslautern, pp. 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S.M.\n                and \n                  Shin, B.\n                (2002), \u201cData mining: new arsenal for strategic decision-making\u201d, in \n                  Becker, S.\n                (Ed.), \n                  Data Warehousing and Web Engineering\n               , IRM Press, Hershey, PA, pp. 103-112.","DOI":"10.4018\/978-1-931777-02-5.ch005"},{"key":"key2020122001374987400_b22","doi-asserted-by":"crossref","unstructured":"Perry, D.E.\n               , \n                  Porter, A.A.\n                and \n                  Votta, L.G.\n                (2000), \u201cEmpirical studies of software engineering: a roadmap\u201d, \n                  Proceedings of the 22nd International Conference on Software Engineering, Future of Software Engineering Track, ICSE 2000, ACM Press, Limerick, June 4-11\n               , pp. 345-355.","DOI":"10.1145\/336512.336586"},{"key":"key2020122001374987400_b25","doi-asserted-by":"crossref","unstructured":"Stahl, F.\n                and 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