{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:46:48Z","timestamp":1754156808766,"version":"3.41.2"},"reference-count":30,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2015,7,6]],"date-time":"2015-07-06T00:00:00Z","timestamp":1436140800000},"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":[[2015,7,6]]},"abstract":"<jats:sec>\n               <jats:title content-type=\"abstract-heading\">Purpose<\/jats:title>\n               <jats:p> \u2013 Business process has become the core assets of many organizations and it becomes increasing common for most medium to large organizations to have collections of hundreds or even thousands of business process models. The purpose of this paper is to explore an alternative dimension to process mining in which the objective is to extract process constraints (or business rules) as opposed to business process models. It also focusses on an alternative data set \u2013 process models as opposed to process instances (i.e. event logs). <\/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 present a new method of knowledge discovery to find business activity sequential patterns embedded in process model repositories. The extracted sequential patterns are considered as business rules. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Findings<\/jats:title>\n               <jats:p> \u2013 The authors find significant knowledge hidden in business processes model repositories. The hidden knowledge is considered as business rules. The business rules extracted from process models are significant and valid sequential correlations among business activities belonging to a particular organization. Such business rules represent business constraints that have been encoded in business process models. Experimental results have indicated the effectiveness and accuracy of the approach in extracting business rules from repositories of business process models. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Social implications<\/jats:title>\n               <jats:p> \u2013 This research will assist organizations to extract business rules from their existing business process models. The discovered business rules are very important for any organization, where rules can be used to help organizations better achieve goals, remove obstacles to market growth, reduce costly mistakes, improve communication, comply with legal requirements, and increase customer loyalty. <\/jats:p>\n            <\/jats:sec>\n            <jats:sec>\n               <jats:title content-type=\"abstract-heading\">Originality\/value<\/jats:title>\n               <jats:p> \u2013 There has very been little work in mining business process models as opposed to an increasing number of very large collections of business process models. This work has filled this gap with the focus on extracting business rules.<\/jats:p>\n            <\/jats:sec>","DOI":"10.1108\/bpmj-01-2014-0004","type":"journal-article","created":{"date-parts":[[2015,7,1]],"date-time":"2015-07-01T08:02:07Z","timestamp":1435737727000},"page":"820-836","source":"Crossref","is-referenced-by-count":9,"title":["Mining business rules from business process model repositories"],"prefix":"10.1108","volume":"21","author":[{"given":"Jantima","family":"Polpinij","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aditya","family":"Ghose","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hoa Khanh","family":"Dam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"key2020122301352092300_b1","unstructured":"Aalst, W.M.P.V.D.\n                (2011), \n                  Process Mining: 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