Francesco Folino
, Gianluigi Greco
, Antonella Guzzo
and Luigi Pontieri
ICAR-CNR, via P. Bucci 41C, I87036 Rende, Italy
Dept. of Mathematics, UNICAL, Via P. Bucci 30B, I87036 Rende, Italy
DEIS, UNICAL, Via P. Bucci 41C, I87036 Rende, Italy
Business Process Intelligence, Process Mining, Decision Trees.
Process Mining techniques exploit the information stored in the executions log of a process in order to extract
some high-level process model, which can be used for both analysis and design tasks. Most of these techniques
focus on “structural” (control-flow oriented) aspects of the process, in that they only consider what elementary
activities were executed and in which ordering. In this way, any other “non-structural” information, usually
kept in real log systems (e.g., activity executors, parameter values, and time-stamps), is completely disre-
garded, yet being a potential source of knowledge. In this paper, we overcome this limitation by proposing
a novel approach for discovering process models, where the behavior of a process is characterized from both
structural and non-structural viewpoints. In a nutshell, different variants of the process (classes) are recognized
through a structural clustering approach, and represented with a collection of specific workflow models. Rel-
evant correlations between these classes and non-structural properties are made explicit through a rule-based
classification model, which can be exploited for both explanation and prediction purposes. Results on real-
life application scenario evidence that the discovered models are often very accurate and capture important
knowledge on the process behavior.
Process mining techniques have been getting increas-
ing attention in the Business Process Management
community because of their ability of characterizing
and analyzing process behaviors, which turns out to
be particularly useful in the (re)-design of complex
systems. In fact, these techniques are aimed at ex-
tracting a model for a process, based on the data gath-
ered during its past enactments and stored in suitable
log files by workflow (or similar kinds of transac-
tional) systems. In particular, traditional and consol-
idated approaches —see, e.g., (van der Aalst et al.,
2003) for a survey on this topic— are focused on
“structural” aspects of the process, i.e., they try to
single out mutual dependencies among the activities
involved in the process, in terms of relationships of
precedence and/or in terms of various routing con-
structs (such as parallelism, synchronization, exclu-
sive choice, and loops).
Only very recently, process mining techniques have
been proposed to deal with important “non-structural”
aspects of the process, such as activity executors, pa-
rameter values, and performance data. For instance,
decision trees have been used in (Ly et al., 2005)
to express staff assignment rules, which correlate
agent profiles with the execution of process activities;
while, a decision point analysis has been discussed
in (Rozinat and van der Aalst, 2006), where decision
trees are used to determine, at each split point of the
model, the activities that are most likely to be exe-
cuted next. Actually, despite the improvements w.r.t.
classical approaches, these techniques still exploit a
few simplifying assumptions that limit their applica-
bility in some application contexts. In particular, the
various approaches share the basic idea of using non-
structural data to characterize which activities are to
be executed, by completely disregarding the specific
coordination mechanisms of their enactment, i.e., how
the flow of execution is influenced by certain data val-
As an example, in a sales order process where
different sub-processes are enacted depending on
whether the customer is a novel one or has, instead,
some fidelity card, current approaches will hardly dis-
cover that two different usage scenarios occur and that
they can moreover be discriminated by some specific
data fields associated with the customer.
The aim of this paper is precisely to enhance cur-
rent techniques with the capability of characterizing
Folino F., Greco G., Guzzo A. and Pontieri L. (2008).
In Proceedings of the Tenth International Conference on Enterprise Information Systems - AIDSS, pages 70-77
DOI: 10.5220/0001705700700077
how activity executors, parameter values and perfor-
mance data affect routing constructs and, more gener-
ally, how they determine the sub-processes to be exe-
cuted. To this end, we shall investigate on the mining
of a multi-perspective process model, where structural
aspects and non-structural ones are formally related
with each other. In fact, the model consists of:
a (structure-centric) behavioral schema, where the
various sub-processes occurring in the process be-
ing analyzed are explicitly and independently de-
scribed; and,
a (data-dependent) classification model, assessing
in terms of decision rules over process instance
attributes when the various sub-processes have to
be enacted.
From a technical point of view, on the one hand the
behavioral schema will be discovered by resorting to
the structural clustering approach presented in (Greco
et al., 2006), where log instances are partitioned into
a number of clusters according to the sequence of
tasks performed, and where each cluster is eventu-
ally equipped with a specific workflow schema. On
the other hand, the classification model will be built
by means of a rule-based classifier, using the clusters
previously found as class labels for all their associated
The whole approach has been implemented as a
plug-in for the ProM framework (van Dongen et al.,
2005), a powerful platform for the analysis of process
logs, and has been tested over a challenging real-life
application scenario, regarding the trading of contain-
ers in an Italian harbor. Results of experimentation
evidence that very accurate models can be found with
our technique, which moreover provides useful hints
for the tuning of the involved logistic operations.
Organization. The remainder of the paper is orga-
nized as follows. After introducing some notation
and basic concepts, Section 2 discusses the proposed
approach in a detailed way, and reports some basic
implementation issues about the integration within
ProM. Then, the real-life case study and results of ex-
perimental activity are discussed in Section 3. Finally,
a few concluding remarks are reported in Section 4.
Process logs contain a wide range of information
about process executions. Following a standard ap-
proach in the literature, we next shall adopt a simple
representation of process logs, where each trace stor-
ing a single enactment of the process (named, process
instance or case) is just viewed as a sequence of task
identifiers, while additional data are represented via
attributes associated with traces.
Let T be a set of task labels, and A = {A
be a set of (names of) process attributes, taking values
from the domains D
, respectively. Then, a
trace (over T and A) is a sequence s = [s
] of
task labels in T , associated with a tuple data(s) of
values from D
, i.e., data(s) D
× ...× D
A process log (or simply log) over T and A is a set of
traces over T and A.
By inspecting and analyzing a process log, our ap-
proach aims at extracting a high-level representation
of the (possibly unknown) underlying process, which
we call multi-perspective process model. Formally, a
multi-perspective process model for a log L is quadru-
ple M = hC
i such that:
= {C
} is a partition of the traces in L,
0 and
= L;
= {W
} is a set of workflow
schemas, one for each cluster in C
, where all
tasks are named with labels from T ;
λ is bijective function mapping the clusters C
to their corresponding workflow schemas in W
i.e., λ : C
, where λ(C
) is the schema
modeling cluster C
: D
× ... × D
is q-ary classification
function, which discriminates among the clusters
in C
based on the values of As attributes.
In words, W
is a modular description of the be-
havior registered in the log, as far as concerns struc-
tural aspects only, where each execution cluster is rep-
resented with a separate workflow schema, describing
the typical way tasks are executed in that cluster. Con-
versely, δ
is a classification function that correlates
these structural clusters with (non-structural) trace at-
tributes, by mapping any n-ple of values for the at-
tributes A
, ..., A
into a single cluster of C
For notation convenience, in the rest of the pa-
per the above model will be seen as two sub-models,
which represent the process in different and yet com-
plementary ways: a structural model hC
which focuses on the different execution flows across
the process tasks, and a data-aware classification
model, which is essentially encoded in the function
in the form of a decision tree (Buntine, 1992). In
fact, yet being strictly connected between each other,
both models are relevant in themselves, for they take
care of separate aspects of the process.
2.1 Algorithmic Issues
Input: A log L over tasks T and attributes A.
Output: A multi-perspective process model for L.
Method: Perform the following steps:
1 C
2 δ
3 C
4 for each C
5 W
6 W
:= W
)} {W
7 λ(C
) := {W
8 end
9 return hC
Figure 1: Algorithm
As discussed in the introduction, the problem of dis-
covering process models based on structural informa-
tion has been widely investigated in the literature. In
Figure 1 an algorithm (named
) is
reported that addresses instead the more general prob-
lem of discovering a high-level process representa-
tion, by computing a multi-perspective process model
for a given log L.
To this aim, the recursive clustering scheme
recently proposed in (Greco et al., 2006)
is first exploited as a sub-routine (function
) to recognize behav-
iorally homogenous groups of traces. Actually, log
traces are partitioned by applying the well-known
k-means method (Mitchell, 1997) to a vectorial
representation of the traces, obtained by using a
special kind of sequential patterns over the tasks
—further details can be found in (Greco et al., 2006).
Afterwards, the algorithm takes advantage of all
available data attributes by invocating the func-
, which derives a classifica-
tion function (see Step 2), expressing the mapping
from data attributes to the clusters discovered previ-
ously. Note that, by using the clusters produced by
as class labels for all their
associated traces, we may learn a classification model
over the attributes in A. Indeed, a wide variety of
models and algorithms might be exploited to this end.
Among them, we selected the popular formalism of
decision trees, mainly for the following two reasons.
First, decision trees can easily be understood and do
not require any prior assumptions on data distribution;
and, second, a number of algorithms are available that
compute them in an efficient way, which cope quite
well with noise and overfitting as well as with miss-
ing values.
The remainder of the
rithm is meant to refine the structural model com-
puted in the first phase, by making it more consis-
tent with the classification function δ just discovered.
Indeed, all the log traces are first remapped to the
clusters, in Step 3, by using the model in a predic-
tive way. A new workflow model is then discovered
for each cluster, in Steps 4-8, which should model
more precisely the structure of the traces that the
cluster has been made contain—note that the func-
basically stands for any standard pro-
cess mining algorithm in the literature, such as those
discussed in the survey (van der Aalst et al., 2003).
Eventually, the algorithm concludes by returning a
multi-perspective process model integrating the dif-
ferent kinds of knowledge discovered.
2.2 Implementation Issues
The above approach has been implemented into a pro-
totype system, which has been integrated as a plug-
in within the ProM framework (van Dongen et al.,
2005), a powerful platform for the analysis of process
logs, quite popular in the Process Mining community.
The logical architecture of the system is sketched
Figure 2, where solid arrow lines stand for informa-
tion exchange. Note that the whole mining process is
driven by the module Process Mining Handler, while
the other modules roughly replicate the computation
scheme in Figure 1. By Log Repository we denote
a collection of existing process logs, represented in
MXML (van Dongen and van der Aalst, 2005), a for-
mat shared by many process mining tools (including,
in particular, the ProM framework).
The Structural Mining module, together with the
ones connected with it, is responsible for the con-
struction of structural models. In particular, the dis-
covery of different trace clusters is carried out by the
module Structural Clustering, which exploits some
functionalities of the plug-in DWS implementing the
clustering scheme in (Greco et al., 2006). The mod-
ule WFDiscovery is used, instead, to derive a work-
flow schema for each discovered cluster, by exploit-
ing the ProM implementation of the Heuristic miner
algorithm (Weijters and van der Aalst, 2003).
Discovered clusters and schemas are stored in the
Trace Clusters repository and in the Workflow Repos-
itory, respectively, for inspecting and further analysis.
The Training Set Builder module mainly labels
each trace with the name of the cluster it has been
assigned to, as it is registered in the Trace Clusters
repository, in order to provide the Classifier induction
module with a training set for learning a classification
model. The module Classifier applies such a model
ICEIS 2008 - International Conference on Enterprise Information Systems
Fetched Traces
Training Set
Figure 2: System architecture.
to reassign the original log traces to the clusters. Both
these latter modules, coordinated by the module Clas-
sification Mining, have been implemented by using
the Weka library (Frank et al., 2005) and, in particular,
the J48 implementation of the well known algorithm
C4.5 (Quinlan, 1993). In this regard, we notice that
the Training Set Builder also acts as a “translator” by
encoding the labelled log traces into a tabular form,
according to the ARFF format used in Weka. Inci-
dentally, this format requires that proper types (such
as nominal, numeric, string) are specified for all at-
tributes. Since this information misses in the origi-
nal (MXML) log files, a semi-automatic procedure is
used to assign such a type to each attribute.
Two modules help the user in evaluating the qual-
ity of the discovered models: the Classifier Evaluator,
which computes standard performance indexes for the
classification models, and the Conformance Evalua-
tor module which measures what an extent workflow
models represents the behaviors actually registered in
the log traces they have been derived from. To this
end, some metrics have been implemented, which will
be briefly described in Section 3.2.
Figure 3 shows two screenshots of the plug-in.
Specifically, Figure 3(a) reports the input panel,
which allows for setting all the parameters needed in
our approach, whereas Figure 3(b) illustrates the out-
come of a mining session. For the latter, note, in par-
ticular, the decision tree (on the left) and one of the
workflow schemas (on the right) found by the tool.
This section is devoted to discuss the application of
the proposed approach within a real-life scenario,
concerning an Italian maritime container terminal, as
a way to give some evidence for the validity of our
proposal, as well as to better explain its behavior
through a practical example. In Section 3.1, we first
illustrate the application scenario, by discussing the
kind of data involved in it. Then, in Section 3.2, we
introduce the setting adopted for evaluating the qual-
ity of discovered models, while in Section 3.3, we
eventually discuss results of experiments carried out
on the scenario.
3.1 Application Scenario
A series of logistic activities are registered for each
of the containers that pass through the harbor, which
actually amount to nearly 4 millions per year. Mas-
sive volumes of data are hence generated continually,
which can profitably be exploited to analyze and im-
prove the enactment of logistics processes. For the
sake of simplicity, in the remainder of the paper we
only consider the handling of containers which both
arrive and depart by sea, and focus on the different
kinds of moves they undergo over the “yard”, i.e.,
the main area used in the harbor for storage purposes.
This area is logically partitioned into a finite number
of bi-dimensional slots, which are the units of storage
space used for containers, and are organized in a fixed
number of sectors.
The life cycle of any container is roughly sum-
(a) Parameter setting
(b) Result view
Figure 3: Screenshots of the plug-in within ProM.
marized as follows. The container is unloaded from
the ship and temporarily placed near to the dock, un-
til it is carried to some suitable yard slot for being
stocked. Symmetrically, at boarding time, the con-
tainer is first placed in a yard area close to the dock,
and then loaded on the cargo. Different kinds of vehi-
cles can be used for moving a container, including,
e.g., cranes, straddle-carriers (a vehicle capable of
picking and carrying a container, by possibly lifting
it up), and multi-trailers (a sort of train-like vehicle
that can transport many containers).
This basic life cycle may be extended with ad-
ditional transfers—classified as “house-keeping”—
which are meant to make the container approach its
final embark point or to leave room for other contain-
ers. More precisely, the following basic operations
are registered for any container c:
, when c is moved from a yard position to an-
other by a straddle carrier;
, when c is moved from a yard position to an-
other by a multi-trailer;
, when a multi-trailer moves to get c;
, when c is charged on a multi-trailer;
, when c is discharged off a multi-trailer;
, when c is moved upward or downward, pos-
sibly to switch its position with another container.
, when a dock crane embarks c on a ship.
Critical performance aspects in this scenario are
the latency time elapsed when serving a ship (where,
typically, a number of containers are both discharged
off and charged on), and the overall costs of moving
the containers around the yard. Both these measures
are heavily affected by the number of extra, “house-
keeping”, moves applied to the containers. Reducing
such operations is a major goal of the allocation poli-
cies, established to decide where to place a newly ar-
rived container, based on different features of it, such
as, e.g., the kind of conveyed goods, its origin and
next destination, as well as some future events (e.g.
which ships are going to take the container and when
this is going to happen). Unfortunately, this informa-
tion can partly be missing or even incorrect, while di-
verse types of delays or malfunctioning are likely to
occur. Therefore, some ex-post analysis of yard op-
eration logs via data mining and process mining tech-
nique is expected to provide precious feedback to the
planning of logistic processes, and to help in decision
making tasks.
3.2 Evaluation Setting
In order to provide a quantitative evaluation of the
multi-perspective process models mined with our ap-
proach, two aspects have been measured in the ex-
perimentation: (i) the predictive power of the classifi-
cation model in discriminating the structural clusters
found, and (ii) the level of conformance of each work-
flow schema w.r.t. the clusters that it models.
As for point (i), a number of well-known measures
are available in the literature to evaluate a classifica-
tion model. Here, we simply resort to the Accuracy
measure, which roughly expresses the percentage of
correct predictions that the classifier would make over
all possible traces of the process. In particular, we es-
timate this measure via the popular cross-validation
method (with ten folders) (Mitchell, 1997). In addi-
tion, standard Precision and Recall measures will be
used to provide a “local” evaluation of the classifier,
w.r.t. each single cluster; and, for each cluster, the
F measure will be reported as well, which is defined
as F
= ((β
+ 1)P
× R
+ β
)—note that for
β = 1, it coincides with the harmonic mean of the pre-
cision and recall values.
As for point (ii), the conformance of a workflow
model W w.r.t. a set L of traces can be evaluated
through the following metrics, all defined in (Rozi-
ICEIS 2008 - International Conference on Enterprise Information Systems
Table 1: Summary of results obtained against real log data.
Test Clusters Structural model Data-aware classification model
Fitness BehAppr StrAppr Accuracy Tree Size Top-4 Discriminant Attributes
Test A
2 0.8725 0.9024 1 96.01% 69 PrevHarbor, NavLine OUT, ContHeight, ShipType IN
Test A-bis
4 0.9324 0.9351 1 94.78% 77 PrevHarbor, NavLine OUT, ContHeight, ShipType IN
Test B
5 0.8558 0.9140 1 91.64% 105 ShipSize IN, NavLine OUT, PrevHarbor, ContHeight
nat and van der Aalst, 2008) and ranging over the real
interval [0,1]:
Fitness, which essentially evaluates the ability of
W to parse all the traces L, by indicating how
much the events in L comply with W .
Advanced Behavioral Appropriateness (denoted
by BehAppr, for short), which estimates the
level of flexibility allowed in W (i.e., alterna-
tive/parallel behavior) really used to produce L.
Advanced Structural Appropriateness (or
StrAppr, for short), which assesses the capability
of W to describe L in a maximally concise way.
These measures have been defined for a workflow
schema and do not apply directly to the multi-schema
structural model discovered by our approach. In order
to show a single overall score for such a model, we
simply average the values computed by each of these
measures against all of its workflow schemas. More
precisely, the conformance values of these schemas
are added up in a weighted way, where the weight of
each schema is the fraction of original log traces that
constitute the cluster it was mined from.
3.3 Experimental Results
For all the experiments described next, we selected
only a subset of containers that completed their en-
tire life cycle in the hub along the first two months of
year 2006, and which were exchanged with four given
ports around the Mediterranean sea—this yielded
about 50Mb log data concerning 5336 containers. In
order to apply our analysis approach, the transit of any
container through the hub has been regarded as a sin-
gle enactment case of a (unknown) logistic process,
for which a suitable model is to be discovered.
We next discuss three tests, which were carried out
on these data according to different perspectives:
Test A (“operation-centric”), where we focus on
the sequence of basic logistic operations applied
to the containers. In more detail, for each con-
tainer a distinct log trace is built that records the
sequence of basic operations (i.e.,
) it underwent.
Test A-bis, where we still consider the sequence
of operations performed on each container, while
distinguishing different cost-dependent variants
for each of them. In fact, each operation is an-
notated with a suffix denoting the cost spent for
performing it.
Test B (“position-centric”), where we focus on
the flow of containers across the yard. Specifi-
cally, original data are transformed into a set of
log trace, each of them encoding the sequence of
yard sectors occupied by a single container during
its stay.
In all cases, two dummy activities (denoted by
, respectively) are used to univocally
mark the beginning and the end of each log trace.
Further, various data attributes have been considered
for each container (i.e., for each process instance), in-
cluding, e.g., its origin and final destination ports, its
previous and next calls, diverse characteristics of the
ship that unloaded it, its physical features (e.g., size,
weight), and a series of categorical attributes concern-
ing its contents (e.g., the presence of dangerous or
perishable goods).
Table 1 summarizes a few relevant figures for these
tests, which concern both the clustering structure and
the two models associated with it. More specifically,
for each test, we report the number of clusters found
and the different conformance measures for the struc-
tural model (i.e., the set of workflow schemas), as
well as the accuracy and size of the decision tree.
Moreover, to give some intuition on the semantics
value of this latter model, we report four of the most
discriminant attributes, actually appearing in its top
. In general, we note that surprisingly high ef-
fectiveness results have been achieved, as concerns
both the structural model and the classification model.
Notably, such a precision in modelling both structural
and non-structural aspects of the logged events does
not come with a verbose (and possibly overfitting)
representation. Indeed, for all the tests, the number
of clusters and the size of the tree are quite restrained,
while the workflow models collectively attain a max-
imal score with the StrAppr metric.
In order to give more insight on the behavior of the
approach, we next illustrate some detailed results for
two of these tests (namely Test A and Test B).
Test A. In this case, the approach has discovered
Decision trees are not shown in detail for both space
and privacy reasons—many attribute express, indeed, sensi-
ble information about the hub society and its partners.
(a) Cluster 0 (b) Cluster 1
Figure 4: Test A - the two workflow schemas discovered.
Table 2: Test A - details on the discovered clusters (sizes
and classification metrics).
Cluster Size P R F (β = 1)
4736 97.28% 98.25% 97.76%
600 84.99% 78.33% 81.53%
two distinct usage scenarios, whose structural aspects
are described by the workflow schemas in Figure 4.
These schemas substantially differ for the presence of
operations performed with multi-trailer vehicles: the
schema of Figure 4.(a) does not feature any of these
operations, which are instead contained in the other
schema. Notably, the former schema captures the vast
majority of handling cases (4736 containers of the
original 5336 ones). This reflects a major aim of yard
allocation strategies: to keep each container as near
as possible to its positions of disembarkation/embark,
by performing short transfers via straddle-carriers.
Interestingly enough, these two markedly differ-
ent structural models appear to strongly depend—
an astonishing 96% accuracy score is achieved (cf.
Table 1)—on some features that go beyond the
mere occurrence of yard operations. Among these
features, the following container attributes stand
out: the provenance port (
) of a con-
tainer, the navigation line that is going to take
it away (
NavLine OUT
), the height of a container
), and the kind of ship that delivered it
to the hub (
ShipType IN
). A finer grain analysis per-
formed with the help of Table 2 (where individual pre-
(a) Cluster 2
(b) Cluster 3
Figure 5: Test B - two of the workflow schemas found.
Table 3: Test B - details on the discovered clusters (sizes
and classification metrics).
Cluster Size P R F (β = 1)
3664 93,76% 98,39% 96, 02%
188 66,67% 53,19% 59,17%
346 90,81% 74,28% 81,72%
1070 87,25% 80,56% 83, 77%
68 94,29% 97,06% 95, 65%
cision/recall measures for the two clusters are shown),
confirms that the model guarantees a high rate of cor-
rect predictions for either cluster.
Test B. The “position-centric” approach is addressed
to analyze the different ways of displacing the con-
tainers around the yard, yet comparing the usage of
different storage areas. In principle, due to the high
number of sectors and moving patterns that come to
play in such analysis perspective, any flat represen-
tation of container flows, just consisting of a single
workflow schema, risks being either inaccurate or dif-
ficult to interpret. Conversely, by separating different
behavioral classes our approach ensures a modular
representation, which can better support explorative
analyses. In fact, the five clusters found in this test
have been equipped with clear and compact workflow
schemas, which yet guarantee a high level of confor-
ICEIS 2008 - International Conference on Enterprise Information Systems
mance (see Table 1). As an instance, in Figure 5, we
report two of these schemas, which differ both in the
usage of sectors and in some of the paths followed by
the containers across these sectors.
Good quality results are achieved again both for the
structural model and for the decision tree. In actual
fact, by comparing these results with those obtained
in the other two tests, we notice some slight decrease
in the accuracy and a larger tree size, mainly due to
the higher level of complexity that distinguish the
position-centric analysis from the operation-centric
one. Incidentally, Table 3 reveals that such worsening
is mainly to blame on the inability of the decision tree
to recognize well the second cluster, which is, in fact,
slightly confused with the third one —further details
are omitted here for lack of space. Almost the same
attributes as in Test A have been employed to discrimi-
nate the clusters, except for the usage of
ShipSize IN
(i.e., the size category of the ship that delivered the
container) in place of
ShipType IN
In this paper we have proposed a novel process
mining approach which allows to discover multi-
perspective process models, i.e., process models
where structural and non-structural aspects are for-
mally related with each other. In a nutshell, the ap-
proach recognizes a number of homogeneous execu-
tion clusters (by way of a clustering method), while
providing each of them with a specific workflow
model. These classes of behavior are then correlated
with non-structural data (such as activity executors,
parameter values and performance metrics) by means
of a classification model, which can be used to both
explain and predict them.
The whole approach has been implemented, inte-
grated as a plug-in in the ProM framework, and val-
idated on a real test case. Results of experimenta-
tion evidenced that the discovered classification mod-
els provide a valuable help for interpreting and dis-
criminating different ways of executing the process,
because of their ability of making explicit the link be-
tween these variants and other process properties.
As future work, we will investigate the extension
of the proposed approach with outlier detection tech-
niques, in order to provide it with the capability of
spotting anomalous executions, which may mislead
the learning of process models, and could profitably
be analyzed in a separate way. Moreover, we are ex-
ploring the integration of multi-perspective models in
an existing process management platform, as a basic
means for providing prediction and simulation fea-
tures, supporting both the design of new processes
and the enactment of future cases.
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