The increasing importance of corporate governance, risk, compliance
management, and legislation (such as
the Sarbanes-Oxley Act and the Basel
II Accord) highlight the practical relevance of conformance checking. Process mining can help auditors check
whether processes execute within
certain boundaries set by managers,
governments, and other stakeholders. 3 Violations discovered through
process mining might indicate fraud,
malpractice, risks, or inefficiency;
for example, in the municipality for
which the WOZ appeal process was
analyzed, ProM revealed misconfigu-rations of its eiStream workflow-man-agement system. Municipal employees frequently bypassed the system
because system administrators could
manually change the status of cases
(such as to skip activities or roll back
the process). 7
Show variability. Handmade process models tend to provide an idealized view of the business process being
modeled. However, such “PowerPoint
reality” often has little in common
with real processes, which have much
more variability. To improve conformance and performance, process analysts should not naively abstract away
Process mining often involves spaghetti-like models; the one in Figure 6
was discovered based on an event log
containing 24,331 events referring to
376 different activities describing the
diagnosis and treatment of 627 gynecological oncology patients in the AMC
Hospital in Amsterdam. The spaghetti-like structures are not caused by the
discovery algorithm but by the variability of the process.
Although stakeholders should see
reality in all its detail (see Figure 6),
spaghetti-like models can be simplified. As with electronic maps, it is possible to seamlessly zoom in and out. 1
Zooming out, insignificant things are
either left out or dynamically clustered into aggregate shapes, in the
same way streets and suburbs amalgamate into cities in Google Maps.
The significance level of an activity or
connection may be based on frequency, costs, or time.
Improve reliability. Process min-
ing can also help improve the reli-
ability of systems and processes; for
example, since 2007, we have used
process mining to analyze the event
logs of X-ray machines from Phil-
ips Healthcare1 that record massive
amounts of events describing actual
use. Regulations in different coun-
tries require proof systems were test-
ed under realistic circumstances; for
this reason, process discovery was
used to construct realistic test pro-
files. Philips Healthcare also used
process mining for fault diagnosis to
identify potential failures within its
X-ray systems. By learning from ear-
lier system failure, fault diagnosis
was able to find the root cause for new
emergent problems. For example,
we used ProM to analyze the circum-
stances under which particular com-
ponents are replaced, resulting in a
set of “signatures,” or historical fault
patterns; when a malfunctioning X-
ray machine exhibits a particular sig-
nature behavior, the service engineer
knows what component to replace.
Process-mining techniques enable
organizations to X-ray their busi-
ness processes, diagnose problems,
and identify promising solutions for
treatment. Process discovery often
provides surprising insight that can
be used to redesign processes or im-
prove management, and conformance
checking can be used to identify
where processes deviate. This is rele-
vant where organizations are required
to emphasize corporate governance,
risk, and compliance. Process-mining
techniques are a means to more rigor-
ously check compliance while improv-
I thank the members of the IEEE Task
Force on Process Mining and all who
contributed to the Process Mining
Manifesto9 and the ProM framework.
1. aalst, W. van der. Process Mining: Discovery,
Conformance and Enhancement of Business
Processes. springer-verlag, berlin, 2011.
2. aalst, W. van der. using process mining to bridge the
gap between bI and bpm. IEEE Computer 44, 12
(Dec. 2011), 77–80.
3. aalst, W. van der, hee, k. van, Werf, j.m. van der, and
verdonk, m. auditing 2.0: using process mining to
support tomorrow’s auditor. IEEE Computer 43, 3
(mar. 2010), 90–93.
4. hilbert, m. and lopez, p. the world’s technological
capacity to store, communicate, and compute
information. Science 332, 6025 (feb. 2011), 60–65.
5. manyika, j., Chui, m., brown, b., bughin, j., Dobbs, r.,
roxburgh, C., and byers, a. Big Data: The Next Frontier
for Innovation, Competition, and Productivity. report
by mckinsey global Institute, june 2011; http://www.
6. mendling, j., neumann, g., and aalst, W. van der.
understanding the occurrence of errors in process
models based on metrics. In Proceedings of the OTM
Conference on Cooperative information Systems
(vilamoura, algarve, portugal, nov. 25–30), f. Curbera,
f. leymann, and m. Weske, eds. lecture notes in
Computer science series, vol. 4803. springer-verlag,
berlin, 2007, 113–130.
7. rozinat, a. and aalst, W. van der. Conformance checking
of processes based on monitoring real behavior.
Information Systems 33, 1 (mar. 2008), 64–95.
8. rozinat, a., de jong, I., günther, C., and aalst, W.
van der. process mining applied to the test process
of wafer scanners in asml. IEEE Transactions on
Systems, Man and Cybernetics, Part C 39, 4 (july
9. task force on process mining. Process Mining
Manifesto. In Proceedings of Business Process
Management Workshops, f. Daniel, k. barkaoui, and s.
Dustdar, eds. lecture notes in business Information
processing series 99. springer-verlag, berlin, 2012,
Wil van der Aalst (w.m.p. email@example.com) is a professor
in the Department of mathematics & Computer science
of the technische universiteit eindhoven, the netherlands,
where he is chair of the architecture of Information