Process Mining: Discovery, Conformance and Enhancement of Business Processes (Google eBook)
More and more information about business processes is recorded by information systems in the form of so-called "event logs". Despite the omnipresence of such data, most organizations diagnose problems based on fiction rather than facts. Process mining is an emerging discipline based on process model-driven approaches and data mining. It not only allows organizations to fully benefit from the information stored in their systems, but it can also be used to check the conformance of processes, detect bottlenecks, and predict execution problems. Wil van der Aalst delivers the first book o.
activity instances analysis analyze approach arcs association rule learning attributes behavior BPMN business process business process maps C-net cess Chap chapter Check ticket classifier clustering complete conformance checking Consider constraint construct control-flow corresponding data mining data set decision tree decision tree learning defined described deviations discovered model event data event log event log contains example executed F1 score Figure fitness footprint frequent function Hence heuristic mining hidden Markov models information systems Lasagna processes life-cycle maps Moreover multi-set MXML nodes notation operational support order line organizational output bindings overfitting partial trace Pentaho perspective Petri net Petri nets plug-ins possible predict problems process mining techniques process model ProM refer register request remaining flow replay representational bias response variable Sect shown in Fig shows simulation social network Spaghetti processes timestamps tion tokens transition system typically users WF-net workflow YAWL