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dc.contributor.authorHinkelmann, Knut
dc.contributor.authorWitschel, Hans Friedrich
dc.contributor.authorNguyen, Tuan Q.
dc.date.accessioned2015-10-14T16:26:43Z
dc.date.available2015-10-14T16:26:43Z
dc.date.issued2012
dc.identifier.urihttp://hdl.handle.net/11654/10774
dc.description.abstractThis work presents a new approach to handling knowledge-intensive business processes in an adaptive, flexible and accurate way. We propose to support processes by executing a process skeleton, consisting of the most important recurring activities of the process, through a workflow engine. This skeleton should be kept simple. The corresponding workflow is complemented by two features: firstly, a task management tool through which workflow tasks are delivered and that give human executors flexibility and freedom to adapt tasks by adding subtasks and resources as required by the context. And secondly, a component that learns business rules from the log files of this task management and that will predict subtasks and resources on the basis of knowledge from previous executions. We present supervised and unsupervised approaches for rule learning and evaluate both on a real business process with 61 instances. Results are promising, showing that meaningful rules can be learned even from this comparatively small data set.
dc.language.isoen
dc.relation.ispartofBUSTECH 2012 - The Second International Conference on Business Intelligence and Technology
dc.accessRightsAnonymous
dc.subjectData Mining
dc.subjectprocess mining
dc.subject.ddc330 - Wirtschaftde
dc.titleLearning Business Rules for Adaptive Process Models
dc.type04 - Beitrag Sammelband oder Konferenzschrift
dc.spatialNice
dc.audienceScience
fhnw.publicationStatePublished
fhnw.ReviewTypeAnonymous ex ante peer review of a complete publication
fhnw.InventedHereYes
fhnw.PublishedSwitzerlandNo
fhnw.IsStudentsWorkno


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