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2015
Conference Paper
Titel
Model-driven business process analytics
Abstract
Business process analytics helps companies to optimize their processes by identifying shortcomings and improvement potentials. However, existing approaches require either a homogenous process execution environment or expert knowledge, both of which many companies lack. In previous work, we introduced aPro, a model-driven architecture enabling process monitoring even in heterogenous environments. Utilizing the model-driven approach, this work presents a new out-of-the-box approach for business process analytics, allowing the business user to analyze the process within the familiar context of the model without needing to know details of the implementation or data mining techniques. We evaluate this approach using both a simulated and a real-life process.