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10 1 Introduction

The first type of process mining is discovery. A discovery technique takes an
event log and produces a model without using any a-priori information. An example
is the α-algorithm [103] that will be described in Chap. 5. This algorithm takes
an event log and produces a Petri net explaining the behavior recorded in the log.
For example, given sufficient example executions of the process shown in Fig. 1.1,
the α-algorithm is able to automatically construct the Petri net without using any
additional knowledge. If the event log contains information about resources, one can
also discover resource-related models, e.g., a social network showing how people
work together in an organization.
The second type of process mining is conformance. Here, an existing process
model is compared with an event log of the same process. Conformance check-
ing can be used to check if reality, as recorded in the log, conforms to the model
and vice versa. For instance, there may be a process model indicating that purchase
orders of more than one million Euro require two checks. Analysis of the event
log will show whether this rule is followed or not. Another example is the check-
ing of the so-called “four-eyes” principle stating that particular activities should
not be executed by one and the same person. By scanning the event log using a
model specifying these requirements, one can discover potential cases of fraud.
Hence, conformance checking may be used to detect, locate and explain devia-
tions, and to measure the severity of these deviations. An example is the confor-
mance checking algorithm described in [80]. Given the model shown in Fig. 1.1
and a corresponding event log, this algorithm can quantify and diagnose devia-
tions.
The third type of process mining is enhancement. Here, the idea is to extend
or improve an existing process model using information about the actual process
recorded in some event log. Whereas conformance checking measures the alignment
between model and reality, this third type of process mining aims at changing or
extending the a-priori model. One type of enhancement is repair, i.e., modifying the
model to better reflect reality. For example, if two activities are modeled sequentially
but in reality can happen in any order, then the model may be corrected to reflect
this. Another type of enhancement is extension, i.e., adding a new perspective to
the process model by cross-correlating it with the log. An example is the extension
of a process model with performance data. For instance, by using timestamps in
the event log of the “request for compensation” process, one can extend Fig. 1.1
to show bottlenecks, service levels, throughput times, and frequencies. Similarly,
Fig. 1.1 can be extended with information about resources, decision rules, quality
metrics, etc.
As indicated earlier, process models such as depicted in Figs. 1.1 and 1.2 show
only the control-flow. However, when extending process models, additional perspec-
tives are added. Moreover, discovery and conformance techniques are not limited to
control-flow. For example, one can discover a social network and check the validity
of some organizational model using an event log. Hence, orthogonal to the three
types of mining (discovery, conformance, and enhancement), different perspectives
can be identified.