{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:02:35Z","timestamp":1773792155236,"version":"3.50.1"},"reference-count":28,"publisher":"IGI Global","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,4,1]]},"abstract":"<p>Business processes compliance monitoring checks whether running business processes comply with involved compliance rules. Business processes in modern enterprise are rarely supported by a single and centralized workflow system, but instead implemented over different applications (e.g., CRM, ERP, WfMS, and legacy systems). The running data (i.e., event) about process executions are scattered across these applications. Under such circumstance, understanding the compliance of running processes entails the compliance monitoring enabling to correlate events from different applications and even different process instances. This paper introduces a framework named as bpCMon for business process compliance monitoring. bpCMon consists of an expressive compliance rule language ECL and a rule system ERS. ECL is a pattern-based formal language for specifying compliance rules of multiple process perspectives, and also allows for describing event-correlation conditions. ERS, generated from compliance rules in ECL, in turn plays as a compliance monitor enabling to correlate events efficiently by means of an indexing structure created from event-correlation conditions. The applicability of bpCMon is demonstrated by experiments on real-world data sets, and the efficiency of bpCMon is illustrated by comparing with related approaches. Overall, bpCMon enables business process compliance monitoring to meet real-world requirements.<\/p>","DOI":"10.4018\/ijwsr.2017040105","type":"journal-article","created":{"date-parts":[[2017,4,18]],"date-time":"2017-04-18T14:43:23Z","timestamp":1492526603000},"page":"81-103","source":"Crossref","is-referenced-by-count":5,"title":["bpCMon"],"prefix":"10.4018","volume":"14","author":[{"given":"Ping","family":"Gong","sequence":"first","affiliation":[{"name":"Department of Computer Science, Fujian Normal University, Fuzhou, China"}]},{"given":"David","family":"Knuplesch","sequence":"additional","affiliation":[{"name":"Institute of Databases and Information Systems, Ulm University, Ulm, Germany"}]},{"given":"Zaiwen","family":"Feng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China"}]},{"given":"Jianmin","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Fujian Normal University, Fuzhou, China"}]}],"member":"2432","reference":[{"key":"IJWSR.2017040105-0","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvlc.2010.11.002"},{"key":"IJWSR.2017040105-1","doi-asserted-by":"publisher","DOI":"10.1145\/2699444"},{"key":"IJWSR.2017040105-2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40787-1_3"},{"key":"IJWSR.2017040105-3","doi-asserted-by":"crossref","unstructured":"Chen, F., Meredith, P. 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