Fuzzy mining - adaptive process simplification based on multi-
perspective metrics
Citation for published version (APA):
Günther, C. W., & Aalst, van der, W. M. P. (2007). Fuzzy mining - adaptive process simplification based on
multi-perspective metrics. In G. Alonso, P. Dadam, & M. Rosemann (Eds.),
Proceedings of the 5th International
Conference on Business Process Management (BPM 2007) 24-28 September 2007, Brisbane, Australia
(pp.
328-343). (Lecture Notes in Computer Science; Vol. 4714). Springer. https://doi.org/10.1007/978-3-540-75183-
0_24
DOI:
10.1007/978-3-540-75183-0_24
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Published: 01/01/2007
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Fuzzy Mining – Adaptive Process Simplification
Based on Multi-perspective Metrics
Christian W. G¨unther and Wil M.P. van der Aalst
Eindhoven University of Technology
P.O. Box 513, NL-5600 MB, Eindhoven, The Netherlands
{c.w.gunther, w.m.p.v.d.aalst}@tue.nl
Abstract. Process Mining is a technique for extracting process models from ex-
ecution logs. This is particularly useful in situations where people have an ide-
alized view of reality. Real-life processes turn out to be less structured than peo-
ple tend to believe. Unfortunately, traditional process mining approaches have
problems dealing with unstructured processes. The discovered models are often
“spaghetti-like”, showing all details without distinguishing what is important and
what is not. This paper proposes a new process mining approach to overcome this
problem. The approach is configurable and allows for different faithfully simpli-
fied views of a particular process. To do this, the concept of a roadmap is used as
a metaphor. Just like different roadmaps provide suitable abstractions of reality,
process models should provide meaningful abstractions of operational processes
encountered in domains ranging from healthcare and logistics to web services
and public administration.
1 Introduction
Business processes, whether defined and prescribed or implicit and ad-hoc, drive and
support most of the functions and services in enterprises and administrative bodies of
today’s world. For describing such processes, modeling them as graphs has proven to
be a useful and intuitive tool. While modeling is well-established in process design, it
is complicated to do for monitoring and documentation purposes. However, especially
for monitoring, process models are valuable artifacts, because they allow us to commu-
nicate complex knowledge in intuitive, compact, and high-level form.
Process mining is a line of research which attempts to extract such abstract, compact
representations of processes from their logs, i.e. execution histories [1,2,3,5,6,7,10,14].
The α-algorithm, for example, can create a Petri net process model from an execution
log [2]. In the last years, a number of process mining approaches have been developed,
which address the various perspectives of a process (e.g., control flow, social network),
and use various techniques to generalize from the log (e.g., genetic algorithms, theory
of regions [12,4]). Applied to explicitly designed, well-structured, and rigidly enforced
processes, these techniques are able to deliver an impressive set of information, yet their
purpose is somewhat limited to verifying the compliant execution. However, most pro-
cesses in real life have not been purposefully designed and optimized, but have evolved
over time or are not even explicitly defined. In such situations, the application of pro-
cess mining is far more interesting, as it is not limited to re-discovering what we already
know, but it can be used to unveil previously hidden knowledge.
G. Alonso, P. Dadam, and M. Rosemann (Eds.): BPM 2007, LNCS 4714, pp. 328–343, 2007.
c
Springer-Verlag Berlin Heidelberg 2007
Fuzzy Mining – Adaptive Process Simplification Based on Multi-perspective Metrics 329
Over the last couple of years we obtained much experience in applying the tried-
and-tested set of mining algorithms to real-life processes. Existing algorithms tend to
perform well on structured processes, but often fail to provide insightful models for less
structured processes. The phrase “spaghetti models” is often used to refer to the results
of such efforts. The problem is not that existing techniques produce incorrect results.
In fact, some of the more robust process mining techniques guarantee that the resulting
model is “correct” in the sense that reality fits into the model. The problem is that the
resulting model shows all details without providing a suitable abstraction. This is com-
parable to looking at the map of a country where all cities and towns are represented by
identical nodes and all roads are depicted in the same manner. The resulting map is cor-
rect, but not very suitable. Therefore, the concept of a roadmap is used as a metaphor to
visualize the resulting models. Based on an analysis of the log, the importance of activ-
ities and relations among activities are taken into account. Activities and their relations
can be clustered or removed depending on their role in the process. Moreover, certain
aspects can be emphasized graphically just like a roadmap emphasizes highways and
large cities over dirt roads and small towns. As will be demonstrated in this paper, the
roadmap metaphor allows for meaningful process models.
In this paper we analyze the problems traditional mining algorithms have with
less-structured processes (Section 2), and use the metaphor of maps to derive a novel,
more appropriate approach from these lessons (Section 3). We abandon the idea of
performing process mining confined to one perspective only, and propose a multi-
perspective set of log-based process metrics (Section 4). Based on these, we have de-
veloped a flexible approach for Fuzzy Mining, i.e. adaptively simplifying mined process
models (Section 5).
2 Less-Structured Processes – The Infamous Spaghetti Affair
The fundamental idea of process mining is both simple and persuasive: There is a pro-
cess which is unknown to us, but we can follow the traces of its behavior, i.e. we have
access to enactment logs. Feeding those into a process mining technique will yield an
aggregate description of that observed behavior, e.g. in form of a process model.
In the beginning of process mining research, mostly artificially generated logs were
used to develop and verify mining algorithms. Then, also logs from real-life work-
flow management systems, e.g. Staffware, could be successfully mined with these tech-
niques. Early mining algorithms had high requirements towards the qualities of log files,
e.g. they were supposed to be complete and limited to events of interest. Yet, most of
the resulting problems could be easily remedied with more data, filtering the log and
tuning the algorithm to better cope with problematic data.
While these successes were certainly convincing, most real-life processes are not
executed within rigid, inflexible workflow management systems and the like, which en-
force correct, predictive behavior. It is the inherent inflexibility of these systems which
drove the majority of process owners (i.e., organizations having the need to support
processes) to choose more flexible or ad-hoc solutions. Concepts like Adaptive Work-
flow or Case Handling either allow users to change the process at runtime, or define
processes in a somewhat more “loose” manner which does not strictly define a specific
330 C.W. G¨unther and W.M.P. van der Aalst
path of execution. Yet the most popular solutions for supporting processes do not en-
force any defined behavior at all, but merely offer functionality like sharing data and
passing messages between users and resources. Examples for these systems are ERP
(Enterprise Resource Planning) and CSCW (Computer-Supported Cooperative Work)
systems, custom-built solutions, or plain E-Mail.
It is obvious that executing a process within such less restrictive environments will
lead to more diverse and less-structured behavior. This abundance of observed behav-
ior, however, unveiled a fundamental weakness in most of the early process mining
algorithms. When these are used to mine logs from less-structured processes, the result
is usually just as unstructured and hard to understand. These “spaghetti” process mod-
els do not provide any meaningful abstraction from the event logs themselves, and are
therefore useless to process analysts. It is important to note that these “spaghetti” mod-
els are not incorrect. The problem is that the processes themselves are really “spaghetti-
like”, i.e., the model is an accurate reflection of reality.
DSYE
(complete)
69
OSYW
(complete)
181
OSIX
(complete)
20
0.5
4
0.987
125
OSZY
(complete)
28
0.8
10
AHCW
(complete)
1
0.5
1
UNHL
(complete)
91
0.979
74
VPPQ
(complete)
184
0.667
11
UNEL
(complete)
155
0.977
86
UNLE
(complete)
41
0.545
18
0.881
36
UNEN
(complete)
44
NEOI
(complete)
15
0.75
9
DNEZ
(complete)
6
0.833
6
ONZY
(complete)
394
0.992
244
ONVL
(complete)
155
0.756
70
ONZO
(complete)
173
0.956
78
0.955
28
DSYN
(complete)
57
0.921
37
DSYV
(complete)
52
0.939
49
DSPY
(complete)
35
0.8
18
DSLQ
(complete)
30
0.909
16
0.667
10
0.917
13
OSYN
(complete)
106
0.5
7
0.957
52
OSDL
(complete)
80
0.821
28
OSAT
(complete)
8
0.75
5
0.929
19
DSSV
(complete)
234
0.966
50
OSAI
(complete)
8
0.8
5
OSHY
(complete)
224
0.989
153
0.878
50
0.983
88
DSSN
(complete)
304
0.854
129
OSVZ
(complete)
18
0.5
4
0.8
43
0.9
28
ONVY
(complete)
160
DNLP
(complete)
264
HQQL
(complete)
1153
0.984
165
OSHB
(complete)
231
0.944
65
OSZO
(complete)
61
0.8
28
DSSA
(complete)
238
0.98
147
0.969
66
VPPN
(complete)
2
0.5
1
DSVM
(complete)
29
0.833
12
0.756
42
0.984
128
POZI
(complete)
263
0.8
20
OSOI
(complete)
291
0.981
110
POLA
(complete)
1255
0.923
169
ONCZ
(complete)
11
0.5
2
0.5
5
0.923
89
0.998
871
OSWL
(complete)
103
0.877
54
AISW
(complete)
430
0.667
34
ONPI
(complete)
264
0.819
104
LELY
(complete)
126
0.667
12
0.8
36
SPWV
(complete)
114
0.929
28
ONYZ
(complete)
7
0.5
6
OSPL
(complete)
4
0.5
1
0.667
20
0.5
7
0.974
71
ONAZ
(complete)
171
0.909
41
ONHL
(complete)
264
0.974
108
0.975
42
ONYN
(complete)
142
0.942
88
ONIZ
(complete)
23
0.75
8
LEVN
(complete)
5
0.5
2
VPNY
(complete)
10
0.5
1
VPNH
(complete)
160
0.854
53
0.983
75
ONLW
(complete)
100
0.978
46
UNEA
(complete)
239
0.941
36
VPNE
(complete)
15
0.667
2
0.957
52
0.75
24
0.909
14
0.985
94
NEOW
(complete)
60
0.923
34
NEPL
(complete)
47
0.885
26
0.909
19
0.667
24
0.941
37
XISH
(complete)
866
0.833
59
OSEL
(complete)
1
0.5
1
0.8
46
0.997
729
AIVM
(complete)
130
0.667
44
OSAX
(complete)
6
0.833
5
0.997
370
0.8
40
HDEI
(complete)
40
0.5
3
TUII
(complete)
90
0.667
9
VPPK
(complete)
350
0.904
80
0.986
154
0.9
38
0.909
72
0.964
25
0.984
101
VPHA
(complete)
102
0.9
43
0.981
173
0.978
44
0.909
37
ONOI
(complete)
703
0.667
54
0.966
56
ONHI
(complete)
78
0.947
60
ONHB
(complete)
178
0.952
79
ONHY
(complete)
74
0.983
64
ONNY
(complete)
191
0.938
64
0.988
108
0.75
77
0.75
60
0.998
522
VPHN
(complete)
279
0.867
140
0.985
136
0.985
151
PONA
(complete)
65
0.667
11
0.938
38
0.98
91
ONIY
(complete)
117
0.921
70
0.981
70
0.97
43
HQWL
(complete)
221
0.99
165
HQXZ
(complete)
83
0.759
35
0.968
47
0.982
84
LEJE
(complete)
50
0.923
41
LEOO
(complete)
85
0.914
37
0.75
3
0.917
35
0.786
43
AIHT
(complete)
87
0.909
37
0.977
111
HDWE
(complete)
43
0.833
6
TUGP
(complete)
73
0.688
19
DSCW
(complete)
20
0.667
4
0.944
95
0.957
47
AINO
(complete)
270
0.872
47
0.857
50
0.992
205
0.75
14
0.97
30
HQLE
(complete)
6010
HQQY
(complete)
185
0.962
83
HQLY
(complete)
80
0.902
62
HQLK
(complete)
1
0.5
1
0.896
79
0.998
682
0.667
10
0.845
96
1
5791
0.667
27
KZEY
(complete)
43
0.667
18
HQXY
(complete)
12
0.667
4
LEOR
(complete)
4
0.5
3
TUJI
(complete)
296
0.987
171
POZW
(complete)
103
0.933
75
0.8
43
0.933
15
0.933
27
0.667
18
TUMI
(complete)
409
0.995
310
0.923
10
HDPT
(complete)
145
0.833
10
BKMI
(complete)
195
0.923
60
TUMK
(complete)
40
0.571
16
TURK
(complete)
42
0.825
38
0.9
20
TUSC
(complete)
166
0.833
17
0.967
36
SPWA
(complete)
129
0.824
72
0.962
45
SPWN
(complete)
120
0.9
74
0.957
45
SPWI
(complete)
99
0.963
72
0.914
46
0.909
27
TUZV
(complete)
308
0.986
119
TUZC
(complete)
390
0.929
148
TUJV
(complete)
92
0.889
28
0.929
14
0.987
101
TUZI
(complete)
395
0.944
252
TUWV
(complete)
5
0.5
1
TUTP
(complete)
24
0.667
4
0.944
2
0.98
119
TUOK
(complete)
44
0.975
42
TUPK
(complete)
66
0.954
65
TUZK
(complete)
207
0.969
156
0.875
43
TUPC
(complete)
18
0.75
8
BKYA
(complete)
153
0.97
55
BKYI
(complete)
160
0.892
93
KZAL
(complete)
2
0.5
2
0.952
37
BKYV
(complete)
144
0.884
109
TUSI
(complete)
306
0.667
10
LEBR
(complete)
1
0.5
1
0.933
21
BLYI
(complete)
128
0.797
120
0.968
56
0.978
50
0.96
80
0.957
84
0.938
49
BCCC
(complete)
82
0.667
18
TUEW
(complete)
154
0.688
35
TUIV
(complete)
1
0.5
1
0.97
82
0.8
16
HDWP
(complete)
11
0.5
1
TUSK
(complete)
88
0.667
13
0.923
10
0.941
25
0.5
7
0.962
51
TUJC
(complete)
121
0.857
22
0.9
37
0.955
32
TUIC
(complete)
24
0.667
7
TUJP
(complete)
203
0.964
99
0.923
86
TUGV
(complete)
39
0.8
13
0.923
56
0.986
100
TUJK
(complete)
131
0.852
124
TUMC
(complete)
192
0.8
49
LEBS
(complete)
197
0.833
38
TUAB
(complete)
12
0.5
2
TUYK
(complete)
1
0.5
1
TUKW
(complete)
73
0.833
12
0.955
45
0.5
1
0.667
7
0.955
28
0.75
12
0.981
100
HDPR
(complete)
35
0.594
31
0.8
8
BCCV
(complete)
55
0.75
22
TUGC
(complete)
79
0.667
20
0.989
134
0.857
19
0.875
21
0.964
36
0.8
18
0.955
41
0.75
27
0.967
49
TUGI
(complete)
152
0.986
119
TUGK
(complete)
40
0.857
21
HLPO
(complete)
4
0.5
3
BCCI
(complete)
177
0.989
139
BCCK
(complete)
63
0.923
30
0.993
231
0.917
54
0.667
7
0.5
1
DSLN
(complete)
30
0.917
13
DSLL
(complete)
36
0.759
28
DSZN
(complete)
25
0.958
25
DSLX
(complete)
48
0.87
20
0.889
26
0.947
20
DSZV
(complete)
22
0.957
22
DSLV
(complete)
26
0.952
20
0.95
15
HDOP
(complete)
7
0.5
6
0.667
3
0.8
30
BKMA
(complete)
130
0.8
29
0.944
69
0.875
53
0.99
129
0.923
44
0.96
80
BKMV
(complete)
143
0.912
66
0.75
17
0.923
14
0.917
45
0.7
14
0.955
17
0.923
17
0.929
18
BCCW
(complete)
4
0.4
4
0.75
19
0.5
3
0
.
875
8
0.833
10
0.941
17
0.5
2
0.667
3
LECL
(complete)
211
0.75
23
0.991
159
0.75
37
0.99
135
TUIP
(complete)
39
0.75
28
0.667
20
0.8
2
0.98
66
TUIK
(complete)
24
0.842
21
0.889
43
0.917
50
BLOM
(complete)
22
0.522
20
BLAM
(complete)
63
0.833
11
TUQK
(complete)
1
0.5
1
0.889
18
0.857
26
0.833
26
0.977
76
BLBO
(complete)
10
0.5
2
0.75
15
0.929
19
TUWP
(complete)
39
0.944
19
0.5
7
0.923
20
0.545
17
0.667
12
0.667
7
0.667
12
05
OSHW
(complete)
4
0.5
1
0.5
1
0.5
1
NEBI
(complete)
33
0.917
13
0.947
16
NEOO
(complete)
16
0.562
14
0.9
11
OSLL
(complete)
61
0.941
35
OSHD
(complete)
17
0.667
6
0.667
8
0.8
10
0.5
1
0.875
17
0.5
2
0.4
4
0.75
3
0.75
4
0.929
14
0.5
3
0.75
2
TUAV
(complete)
5
0.5
7
0.25
4
TUAI
(complete)
1
0.5
1
TUAK
(complete)
2
0.5
1
0.5
2
0.5
1
0.75
2
0.5
2
05
0.833
5
0.929
17
0.5
1
0.5
1
Fig.1. Excerpt of a typical “Spaghetti” process model (ca. 20% of complete model)
An example of such a “spaghetti” model is given in Figure 1. It is noteworthy that this
figure shows only a small excerpt (ca. 20%) of a highly unstructured process model. It
has been mined from machine test logs using the Heuristics Miner, one of the traditional
process mining techniques which is most resilient towards noise in logs [14]. Although
this result is rather useful, certainly in comparison with other early process mining
techniques, it is plain to see that deriving helpful information from it is not easy.
Event classes found in the log are interpreted as activity nodes in the process model.
Their sheer amount makes it difficult to focus on the interesting parts of the process.
The abundance of arcs in the model, which constitute the actual “spaghetti”, introduce
an even greater challenge for interpretation. Separating cause from effect, or the general
direction in which the process is executed, is not possible because virtually every node
is transitively connected to any other node in both directions. This mirrors the crux of
flexibility in process execution – when people are free to execute anything in any given
order they will usually make use of such feature, which renders monitoring business
activities an essentially infeasible task.
Fuzzy Mining – Adaptive Process Simplification Based on Multi-perspective Metrics 331
We argue that the fault for these problems lies neither with less-structured pro-
cesses, nor with process mining itself. Rather, it is the result of a number of, mostly
implicit, assumptions which process mining has historically made, both with respect
to the event logs under consideration, and regarding the processes which have gener-
ated them. While being perfectly sound in structured, controlled environments, these
assumptions do not hold in less-structured, real-life environments, and thus ultimately
make traditional process mining fail there.
Assumption 1: All logs are reliable and trustworthy. Any event type found in the
log is assumed to have a corresponding logical activity in the process. However,
activities in real-life processes may raise a random number of seemingly unrelated
events. Activities may also go unrecorded, while other events do not correspond to
any activity at all.
The assumption that logs are well-formed and homogeneous is also often not
true. For example, a process found in the log is assumed to correspondto one logical
entity. In less-structured environments, however, there are often a number of “tacit”
process types which are executed, and thus logged, under the same name.
Also, the idea that all events are raised on the same level of abstraction, and
are thus equally important, is not true in real-life settings. Events on different lev-
els are “flattened” into the same event log, while there is also a high amount of
informational events (e.g., debug messages from the system) which need to be
disregarded.
Assumption 2: There exists an exact process which is reflected in the logs. This as-
sumption implies that there is the one perfect solution out there, which needs to
be found. Consequently, the mining result should model the process completely,
accurately,andprecisely. However, as stated before, spaghetti models are not nec-
essarily incorrect – the models look like spaghetti, because they precisely describe
every detail of the less-structured behavior found in the log. A more high-level
solution, which is able to abstract from details, would thus be preferable.
Traditional mining algorithms have also been confined to a single perspective
(e.g., control flow, data), as such isolated view is supposed to yield higher pre-
cision. However, perspectives are interacting in less-structured processes, e.g. the
data flow may complement the control flow, and thus also needs to be taken into
account.
In general, the assumption of a perfect solution is not well-suited for real-
life application. Reality often differs significantly from theory, in ways that had
not been anticipated. Consequently, useful tools for practical application must be
explorative, i.e. support the analyst to tweak results and thus capitalize on their
knowledge.
We have conducted process mining case studies in organizations like Philips Med-
ical Systems, UWV, Rijkswaterstaat, the Catharina Hospital Eindhoven and the AMC
hospital Amsterdam, and the Dutch municipalities of Alkmaar and Heusden. Our ex-
periences in these case studies have shown the above assumptions to be violated in all
ways imaginable. Therefore, to make process mining a useful tool in practical, less-
structured settings, these assumptions need to be discarded. The next section introduces
the main concept of our mining approach, which takes these lessons into account.
