  Process mining starts from event data. The ordering of events is vital for
the discovery of process models. However, the timestamps of events may be
unreliable or imprecise. To further complicate matters, also causally unrelated
events may be ordered in time. The fact that one event is followed by another
does not imply that the former causes the latter. This paper explores the
relationship between time and order. Moreover, it describes an approach to
preprocess event data having timestamp-related problems. This approach avoids
using accidental or unreliable orders and timestamps, creates partial orders to
capture uncertainty, and allows for exploiting domain knowledge to (re)order
events. Optionally, the approach also generates interleavings to be able to use
existing process mining techniques that cannot handle partially ordered event
data. The approach has been implemented using ProM and can be applied to any
event log.
