  Much time in process mining projects is spent on finding and understanding
data sources and extracting the event data needed. As a result, only a fraction
of time is spent actually applying techniques to discover, control and predict
the business process. Moreover, current process mining techniques assume a
single case notion. However, in reallife processes often different case notions
are intertwined. For example, events of the same order handling process may
refer to customers, orders, order lines, deliveries, and payments. Therefore,
we propose to use Multiple Viewpoint (MVP) models that relate events through
objects and that relate activities through classes. The required event data are
much closer to existing relational databases. MVP models provide a holistic
view on the process, but also allow for the extraction of classical event logs
using different viewpoints. This way existing process mining techniques can be
used for each viewpoint without the need for new data extractions and
transformations. We provide a toolchain allowing for the discovery of MVP
models (annotated with performance and frequency information) from relational
databases. Moreover, we demonstrate that classical process mining techniques
can be applied to any selected viewpoint.
