Learning Business Rules for Adaptive Process Models
Type
04 - Beitrag Sammelband oder Konferenzschrift
Primary target group
Science
Created while belonging to FHNW?
Yes
Zusammenfassung
This 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.