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18 1 Introduction

time is remarkably long, process mining can be used to identify the problem and
discover possible causes. If the event log contains case-related information, this can
be used to further analyze the decision points in the process. For instance, through
decision point analysis it may be learned that requests for compensation of more
than € 800 tend to be rejected.
Using process mining, the different perspectives can be cross-correlated to find
surprising insights. Examples of such findings could be: “requests examined by Sean
tend to be rejected more frequently”, “requests for which the ticket is checked after
examination tend to take much longer”, “requests of less than € 500 tend to be
completed without any additional iterations”. Moreover, these perspectives can also
be linked to conformance questions. For example, it may be shown that Pete is
involved in relatively many incorrectly handled requests. These examples show that
privacy issues need to be considered when analyzing event logs with information
about individuals (see Sect. 8.3.3).

1.5 Play-in, Play-out, and Replay

One of the key elements of process mining is the emphasis on establishing a strong
relation between a process model and “reality” captured in the form of an event log.
Inspired by the terminology used by David Harel in the context of Live Sequence
Charts [53], we use the terms Play-in, Play-out, and Replay to reflect on this relation.
Figure 1.8 illustrates these three notions.
Play-out refers to the classical use of process models. Given a Petri net, it is
possible to generate behavior. The traces in Table 1.2 could have been obtained by
repeatedly “playing the token game” using the Petri net of Fig. 1.5. Play-out can
be used both for the analysis and the enactment of business processes. A workflow
engine can be seen as a “Play-out engine” that controls cases by only allowing the
“moves” allowed according to the model. Hence, Play-out can be used to enact oper-
ational processes using some executable model. Simulation tools also use a Play-out
engine to conduct experiments. The main idea of simulation is to repeatedly run a
model and thus collect statistics and confidence intervals. Note that a simulation
engine is similar to a workflow engine. The main difference is that the simulation
engine interacts with a modeled environment whereas the workflow engine interacts
with the real environment (workers, customers, etc.). Also classical verification ap-
proaches using exhaustive state-space analysis—often referred to as model checking
[20]—can be seen as Play-out methods.
Play-in is the opposite of Play-out, i.e., example behavior is taken as input and
the goal is to construct a model. Play-in is often referred to as inference.The
α-algorithm and other process discovery approaches are examples of Play-in tech-
niques. Note that the Petri net of Fig. 1.5 can be derived automatically given an
event log like the one in Table 1.2. Most data mining techniques use Play-in, i.e.,
a model is learned on the basis of examples. However, traditionally, data mining
has not been concerned with process models. Typical examples of models are de-
cision trees (“people that drink more than five glasses of alcohol and smoke more