Acquiring models from data
There is a growing body of evidence that certain classes of models (enterprise architecture models, goal models as well as process and service models, for instance) are amenable to automated acquisition from readily available enterprise data. These techniques seek to mine useful “first-cut” models from the available data, which can be subsequently edited and refined by analysts, thereby easing the model acquisition bottleneck (there are other benefits, including the ability to improve model quality, and the use of models as dashboard artefacts). Data-driven methods can also indirectly contribute to the development of more resilient models. This tutorial will provide a timely exposition to these techniques whose importance is likely to grow significantly in the near future.
Lecture at NEMO2021
Date/Time: Tuesday, July 27, 2021 at 13:00