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1.4 Analyzing an Example Log 11

In the remainder, we consider the following perspectives.

• The control-flow perspective focuses on the control-flow, i.e., the ordering
of activities. The goal of mining this perspective is to find a good character-
ization of all possible paths, e.g., expressed in terms of a Petri net or some
other notation (e.g., EPCs, BPMN, and UML ADs).
• The organizational perspective focuses on information about resources hid-
den in the log, i.e., which actors (e.g., people, systems, roles, and depart-
ments) are involved and how are they related. The goal is to either structure
the organization by classifying people in terms of roles and organizational
units or to show the social network.
• The case perspective focuses on properties of cases. Obviously, a case can
be characterized by its path in the process or by the originators working
on it. However, cases can also be characterized by the values of the corre-
sponding data elements. For example, if a case represents a replenishment
order, it may be interesting to know the supplier or the number of products
ordered.
• The time perspective is concerned with the timing and frequency of events.
When events bear timestamps it is possible to discover bottlenecks, mea-
sure service levels, monitor the utilization of resources, and predict the re-
maining processing time of running cases.

Note that the different perspectives are partially overlapping and non-exhaustive.
Nevertheless, they provide a good characterization of the aspects that process min-
ing aims to analyze.
In most examples given thus far it is assumed that process mining is done off-line,
i.e., processes are analyzed afterward to see how they can be improved or better un-
derstood. However, more and more process mining techniques can also be used in
an online setting. We refer to this as operational support. An example is the detec-
tion of nonconformance at the moment the deviation actually takes place. Another
example is time prediction for running cases, i.e., given a partially executed case
the remaining processing time is estimated based on historic information of similar
cases. This illustrates that the “process mining spectrum” is broad and not limited
to process discovery. In fact, today’s process mining techniques are indeed able to
support the whole BPM life-cycle shown in Fig. 1.3. Process mining is not only
relevant for the design and diagnosis/requirements phases, but also for the enact-
ment/monitoring and adjustment phases.

1.4 Analyzing an Example Log

After providing an overview of process mining and positioning it in the broader
BPM discipline, we use the event log shown in Table 1.1 to clarify some of the foun-
dational concepts. The table shows just a fragment of a possible log corresponding