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1.3 Process Mining 9

Fig. 1.4 Positioning of the three main types of process mining: discovery, conformance,and en-
hancement

However, most information systems store such information in unstructured form,
e.g., event data is scattered over many tables or needs to be tapped off from sub-
systems exchanging messages. In such cases, event data exist but some efforts are
needed to extract them. Data extraction is an integral part of any process mining
effort.
Let us assume that it is possible to sequentially record events such that each
event refers to an activity (i.e., a well-defined step in the process) and is related to
a particular case (i.e., a process instance). Consider, for example, the handling of
requests for compensation modeled in Fig. 1.1. The cases are individual requests
and per case a trace of events can be recorded. An example of a possible trace
is register request, examine casually, check ticket, decide, reinitiate request, check
ticket, examine thoroughly, decide, pay compensation . Here activity names are used
to identify events. However, there are two decide events that occurred at different
times (the fourth and eighth event of the trace), produced different results, and may
have been conducted by different people. Obviously, it is important to distinguish
these two decisions. Therefore, most event logs store additional information about
events. In fact, whenever possible, process mining techniques use extra information
such as the resource (i.e., person or device) executing or initiating the activity, the
timestamp of the event, or data elements recorded with the event (e.g., the size of an
order).
Event logs can be used to conduct three types of process mining as shown in
Fig. 1.4.