Figure 3. the process model can be extended using event attributes (such as timestamps, resource information, and case data); the model also shows frequencies, as in, say, 1,537 times a decision was made and 930 cases were rejected.

Resource information, in the event log can be used for social network analysis, role discovery, and performance analysis.

Sue
Mike
Timestamps, in the event log
can be used to analyze waiting
times between activities.

Mary
Pete
Attributes, in the event log can be
used for decision-point analysis.

Norman

b

566

566

check = “OK” and report = “Approved”

g

1391

1537

971

c

971

461

461

1391

a
e 1537

930

930

start

1391

1537

end

h

1537

d

1537

146

146

146

f

 

timestamps, and case data; for example, an event referring to activity “ register request” and case “992564” may also have attributes describing the person registering the request (such as “John”), time of the event (such as “30- 11-2011: 14. 55”), age of the customer (such as “ 45”), and claimed amount (such as “650 euro”). After aligning model and log the event log can be replayed on the model; while replaying, one can analyze these additional attributes; Figure 3 shows, for example, it is possible to analyze wait times between activities. Measuring the time difference between causally related events and computing basic statistics (such as averages, variances, and confidence intervals) makes it possible to identify the main bottlenecks.

Information about resources can
help discover roles, or groups of peo-
ple executing related activities fre-
quently, through standard clustering
techniques. Social networks can be
constructed based on the flow of work,
and resource performance (such as
the relation between workload and
service times) can be analyzed. Stan-
dard classification techniques can be
used to analyze the decision points in
the process model; for example, activ-
ity e (“decide”) has three possible out-
comes: “pay,” “reject,” and “redo.”
Using data known about the case
prior to the decision, a decision tree
can be constructed explaining the ob-
served behavior.

Practical Value

Here, I focus on the practical value of process mining. As mentioned earlier, process mining is driven by the continuing exponential growth of event-data volume; for example, according to McKinsey Global Institute in 2010

enterprises stored more than seven exabytes of new data on disk drives, while consumers stored more than six exabytes of new data on such devices as PCs and notebooks. 5

The remainder of the article explores how process mining provides value, referring to case studies that used our open-source software package ProM ( http://www.processmining. org) 1 created and maintained by the process-mining group at Eindhoven University of Technology, though other research groups have contributed, including the University of Padua, Universitat Politècnica de Catalunya, University of Calabria, Humboldt-Universität zu Berlin, Queensland University of Technology, Technical University of Lisbon, Vienna University of Economics and Business, Ulsan National Institute of Science and Technology, K.U. Leuven, Tsinghua University, and University of Innsbruck. Besides ProM, approximately 10 commercial software vendors worldwide develop and distribute process-mining software, often embedded in larger tools