=Paper=
{{Paper
|id=Vol-1847/paper10
|storemode=property
|title=Refining Discovered Petri Nets by Sequencing Repetitive Components
|pdfUrl=https://ceur-ws.org/Vol-1847/paper10.pdf
|volume=Vol-1847
|authors=Ernesto López-Mellado,Tonatiuh Flores-Tapia
|dblpUrl=https://dblp.org/rec/conf/apn/Lopez-MelladoF17
}}
==Refining Discovered Petri Nets by Sequencing Repetitive Components==
https://ceur-ws.org/Vol-1847/paper10.pdf
Refining Discovered Petri Nets by Sequencing
Repetitive Components
Ernesto López-Mellado, Tonatiuh Tapia-Flores
CINVESTAV Unidad Guadalajara.
Av. del Bosque 1145. Col. El Bajío 45015 Zapopan, México
{elopez, ttapia}@gdl.cinvestav.mx,
Abstract. The problem of refining a Petri net (PN) discovered from a single se-
quence S of events T generated by discrete event processes is addressed. The re-
finement aims to reduce a possible exceeding language in the discovered model. A
technique that extends a t-invariant based discovery method is presented. Given a
discovered PN and its set of minimal t-invariants Y, the technique analyses the
execution of the t-components in S and determines a sequencing pattern SY that
schedules the execution of t-components in the initial PN. Then, SY is used to dis-
cover a PN’ that uses transitions in T and new places; PN’ schedules representa-
tive transitions of each t-invariant in SY. The refined model is obtained by merg-
ing the representative transitions of both PN and PN’ if the pattern is discovered.
A first result coping with a subclass of safe PN in which each t-component has at
least a transition not shared with any other component is reported.
Keywords: Discrete event process discovery; Petri nets refinement; Repetitive
components.
1 Introduction
Automated modelling of discrete event processes from external behaviour is nowa-
days studied by numerous research groups along the two last decades. The main mo-
tivation is to discover the current behaviour of unknown or ill known systems, be-
cause the documentation is not updated or missing. The aim is to obtain models that
express clearly causal and concurrency relationships between the events generated by
the process. Petri nets have been mostly used to represent such models.
Variations on this problem have been named differently in the research community.
Earlier methods were called language learning techniques, which aimed to build finite
automata, or grammars of languages from samples of accepted words [1, 2]. In dis-
crete manufacturing systems the problem is referred as process identification, in
which the purpose is to build finite automata or Petri nets from sequences of input-
output signals. In this field several approaches and methods have been proposed: the
incremental approach [3, 4], and the integer programming based approach [5, 6].
Input-output identification of automated production process is addressed in [7]; an
interpreted PN is obtained from a long single observation of input-output vectors.
Overviews of these methods are presented in [8] and [9].
In the field of workflow management systems (WMS) the problem is named process
mining: discovery. Most of the proposals obtain Petri nets from a set of event traces
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representing the system behaviour as process cases. A complete review of recent
works can be found in [10].
The work presented herein concerns to identification of discrete event processes, in
which the available data is a set of sequences of input-output vectors, which represent
the exchanged signals between a controller and a plant from the controller point of
view. The events, the number of places, and the number of transitions are not known a
priori. The proposed method yields a Petri net model including input functions and
outputs. The method is based on a two-phase strategy presented in a recent previous
work [7], in which the observable components of the labelled PN are first obtained,
and then the non-observable part is inferred.
This paper focuses on the second phase in which a PN is obtained from a sequence S
of events (transitions) from a given alphabet T. More precisely, a method based on the
computation of the t-invariants [11] is extended; the proposed technique refines the
obtained PN by determining a sequencing pattern, given also as a PN’, that schedules
the execution of t-components in the initial PN.
The paper contains a) an overview of the t-invariant based discovery method and b)
the repetitive component scheduling technique.
2 Context and background
2.1 Input-Output identification
The present paper follows the approach presented in [7, 11] for dealing with input-
output identification. The method processes off-line the I/O-sequence w captured
during the process operation and builds an interpreted PN (IPN) model that repro-
duces w.
Example 1. Consider a process handling 3 inputs (s, x, y) and 3 outputs (A, B, C),
from which the following I/O sequence is captured:
s ⎡0⎤ ⎡1 ⎤ ⎡0⎤ ⎡0⎤ ⎡0⎤ ⎡0⎤ ⎡0⎤ ⎡1 ⎤ ⎡0⎤ ⎡0⎤ ⎡0⎤ ⎡0⎤ ⎡0⎤ ⎡1 ⎤ ⎡0⎤ ⎡0⎤ ⎡0⎤ ⎡0⎤ ⎡0⎤ ⎡1 ⎤ ⎡0⎤ ⎡0⎤ ⎡0⎤ ⎡0⎤
x ⎢⎢0⎥⎥ ⎢⎢0⎥⎥ ⎢⎢0⎥⎥ ⎢⎢0⎥⎥ ⎢⎢1 ⎥⎥ ⎢⎢0⎥⎥ ⎢⎢0⎥⎥ ⎢⎢0⎥⎥ ⎢⎢0⎥⎥ ⎢⎢0⎥⎥ ⎢⎢0⎥⎥ ⎢⎢0⎥⎥ ⎢⎢0⎥⎥ ⎢⎢0⎥⎥ ⎢⎢0⎥⎥ ⎢⎢0⎥⎥ ⎢⎢1 ⎥⎥ ⎢⎢0⎥⎥ ⎢⎢0⎥⎥ ⎢⎢0⎥⎥ ⎢⎢0⎥⎥ ⎢⎢0⎥⎥ ⎢⎢0⎥⎥ ⎢⎢0⎥⎥
y ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢1 ⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢1 ⎥ ⎢0⎥
w = ⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥
A ⎢0⎥ ⎢1 ⎥ ⎢1 ⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢1 ⎥ ⎢1 ⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢1 ⎥ ⎢1 ⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢1 ⎥ ⎢1 ⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥
B ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢1 ⎥ ⎢1 ⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢1 ⎥ ⎢1 ⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥ ⎢0⎥
⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥⎢ ⎥
C ⎢⎣0⎥⎦ ⎢⎣0⎦⎥ ⎢⎣0⎥⎦ ⎢⎣0⎥⎦ ⎢⎣0⎥⎦ ⎢⎣0⎥⎦ ⎢⎣0⎥⎦ ⎣⎢0⎥⎦ ⎢⎣0⎥⎦ ⎢⎣0⎥⎦ ⎢⎣1 ⎥⎦ ⎢⎣1 ⎥⎦ ⎢⎣0⎦⎥ ⎢⎣0⎥⎦ ⎢⎣0⎥⎦ ⎢⎣0⎥⎦ ⎢⎣0⎥⎦ ⎢⎣0⎥⎦ ⎣⎢0⎥⎦ ⎢⎣0⎥⎦ ⎢⎣0⎥⎦ ⎢⎣0⎥⎦ ⎢⎣1 ⎥⎦ ⎢⎣1 ⎥⎦
The construction of an interpreted Petri net (IPN) model from w is performed in
two stages. The first stage processes the sequence w and obtains the observable part of
the model consisting of components using observable places and transitions labelled
with output symbols and expressions of input symbols respectively (Fig. 1). This
determines the set of transitions T. A transition sequence S that corresponds to the
observed event sequence is also delivered. In the example S1=t1 t2 t3 t4 t1 t2 t5 t6 t1 t2 t3
t4 t1 t2 t5 t6 t1 is obtained. A simpler technique for this stage is proposed later in [12].
The second stage builds a PN model that is able to reproduce S with a reduced ex-
cessive behaviour; furthermore, the number of nodes is small since only the necessary
places to represent the discovered structural constrains are added. The resulting PN
corresponds to the process’ internal behaviour represented by non observable places.
In the example the obtained PN showed in figure 2 reproduces S1 (thus w). Merging
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this model with the observable model and eliminating implicit places (p11, p22, p33)
yields the final IPN model shown in figure 3.
A technique that allows determining more complex behaviours, in particular im-
plicit dependencies, is proposed in [11]; besides discovering the non observable PN
the technique delivers the t-invariant supports. In this example, the supports are
={t1, t2, t3, t4} and ={t1, t2, t5, t6}; the t-components ||Yi|| can be clearly
distinguished in this example.
In the present paper we are extending the method in [11] for obtaining a more pre-
cise non observable IPN with respect to the observed sequence S.
In Example 1, notice that the execution of S exhibit always the occurrence of t-
components in alternate way: SY=τ1τ2τ1τ2τ1τ2..., where τi is a sub-sequence of S in-
cluding transitions in . However, in the model sub-sequences of repeated com-
ponents (τ1τ1τ1 ...τ2τ2) may occur, which is a kind of exceeding behaviour.
A possible refining of the PN N of figure 3 to reduce this behaviour can be done by
adding a component that ensures the occurrence of the t-components. Being ρ(τ1)=t3
and ρ(τ1)=t5 the representative transitions of each component, it is easy to determine
that a circuit N’ including such transitions and new places can be added to the PN to
ensure the order of occurrence, as shown in figure 4. Next section describes this meta-
analysis technique.
p4 p4 p4
t1 t1 s_1 t1
t1 s_1
p 11 p1 A p11
p1 A
t2 t2 ε t2
t2 ε
p5 p5 p5
t3 x_1 t5 y_1 t3 t5 t3 x_1 t5 y_1 t3 t5
p6
p2 B p3 C P22 P33 p2 B p3 C p22 p33
p7
ε t6 ε t4 t6 t4 ε t6 ε t4 t6
t4
Fig. 1. IPN fragments Fig. 2. Non-observable PN Fig. 3. Complete IPN Fig. 4. Scheduled t-components
2.2 Petri nets definitions and notation
This section presents the basic concepts and notations of ordinary PN.
Definition 1. An ordinary Petri Net structure G is a bipartite digraph represented
by the 4-tuple G = (P, T, I, O) where: P = {p1, p2, ..., p|P|} and T = {t1, t2, ..., t|T|} are
finite sets of vertices named places and transitions respectively; I(O) : P × T → {0,1}
is a function representing the arcs going from places to transitions (from transitions to
places). For any node x ∈ P∪T, •x = {y | I((y, x))=1}, and x• = {y | O((x, y)=1}.
The incidence matrix of G is C = C+ − C−, where
C− = [cij−]; cij− = I(pi, tj); and C+ = [cij+]; cij+ = O(pi, tj) are the pre-incidence and post-
incidence matrices respectively.
A marking function M : P→ Z+ represents the number of tokens residing inside
each place; it is usually expressed as an |P|-entry vector. Z+ is the set of nonnegative
integers.
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Definition 2. A Petri Net system or Petri Net (PN) is the pair N = (G,M0), where G
is a PN structure and M0 is an initial marking.
In a PN system, a transition tj is enabled at marking Mk if ∀pi ∈ P, Mk(pi) ≥ I(pi, tj);
an enabled transition tj can be fired reaching a new marking Mk+1, which can be com-
puted as Mk+1 = Mk + Cuk, where uk(i) = 0, i≠j, uk(j) = 1; this equation is called the PN
state equation. The reachability set of a PN is the set of all possible reachable mark-
ings from M0 firing only enabled transitions; this set is denoted by R(G,M0).
Definition 3. A PN system is 1-bounded or safe iff for any Mi ∈ R(G,M0) and any
p ∈ P, Mi(p) ≤ 1. A PN system is live iff for every reachable marking Mi ∈ R(G,M0)
and ∀t ∈ T there is a reachable marking Mk ∈ R(G, Mi) such that t is enabled in Mk.
Definition 4. A t-invariant Yi of a PN is an integer solution to the equation CYi=0
such that Yi≥0 and Yi≠0. The support of Yi denoted as is the set of transitions
whose corresponding entries in Yi are strictly positive. Y is minimal if its support is
not included in the support of other t-invariant. A t-component G(Yi) is a subnet of PN
induced by a : G(Yi)=(Pi, Ti, Ii, Oi), where Pi =•∪•, Ti =, Ii=
Pi×Ti∩I, and Oi=Pi×Ti∩O. G(Yi) is usually denoted as ||Yi||.
In a t-invariant Yi, if we have initial marking (M0) that enables a ti ∈ , when ti
is fired, then M0 can be reached again by firing only transitions in .
3 Sequencing repetitive components
3.1. Problem statement
Consider the t-invariant based discovery method, which treats a single sequence
S∈T* and delivers a safe PN N and a set of minimal t-invariant supports ={}
such that PN executes all the t-invariants Y [11]. The refining technique has to find a
PN N’ using a set of transitions T’⊆ T and new places, such that the composed PN
N’’= N||N’ by merging transitions in T’ in both nets executes the t-components ||Yi|| in
the order they appear in S. This statement has been briefly introduced through Exam-
ple 1, where N and N’’ are illustrated in figures 2 and 4 respectively.
3.2 Sequence of t-components
A sequence SY of t-components execution can be determined by parsing S from the
first transition. Every component ||Yi|| can be identified when a given transition that
distinguishes the component is found or, in the worst case, the whole set is
found during the tracking of S.
3.2.1 Representative transitions
Definition. The distinguishable transitions of a t-component ι(||Yi||)⊆ are the
transitions that only belong to this support. When such transitions are fired, we are
sure that a t-component is executed. ι(||Yi||)=∩(⊗j∀j).
Notice that ι(||Yi||)=∅ when every tj ∈ belongs also to other t-invariant sup-
ports. Eventually, all the ι(||Yi||)=∅.
134
Definition. The representative transition ρi of is a tj∈ such that it deter-
mines precisely the t-component is being executed is S. If ι(||Yi||)≠∅ then ρi is the
first transition in ι(||Yi||) that occurs in S; else, ρi is the last transition in found in
the current subsequence tracked in S, then τi is determined.
In Example 1, ι(||Y1||)={t3, t4} and ι(||Y2||)={t5, t6}; ρ1= t3 and ρ2=t5.
Property 1. In a firing sequence S all the transitions of a t-component are fired
consecutively as a subsequence τi if there is no other nested t-components (τj, τk ...)
sharing transitions with τi. In case of nested t-components, part of τi is fired, then one
or several occurrences of τj may appear; afterwards some other transitions of τi are
fired, then other nested τk may occur and so on; finally, the remainder transitions of τi
are fired.
Proof. In a repetitive execution of a PN if a t-invariant is executed, all the transi-
tions of the support must be fired. When a nested t-component is executed, then this
component will be executed (one or several times), and then the remaining transitions
of the first t-component will be fired.
It is clear from S of Example 1 that all the transitions of are fired first and
then those of . Thus, it can be expressed as SY=τ1τ2τ1τ2τ1τ2...
Example 2. The PN in Figure 5, discovered from S= t1 t2 t4 t2 t3 t1 t2 t4 t2 t3 t1 t2 t4 t2
t3..., has two supports: ={t1, t2, t3} and ={t2, t4}. ι(||Y1||)={t1, t3} and
ι(||Y2||)={t4}; ρ1= t1 and t3, and ρ2=t4. Then SY=τ11τ2τ12τ11τ2τ12..., which means that τ1
is split into two sub-sequences τ11 and τ12. Since ||Y2|| is a nested t-component, ρ11= t1
ρ12= t3
When some ι(||Yi||)=∅, the transitions in must be tracked during the parsing
of S until the last transition or the support is found; then τk is determined.
3.2.2 Obtaining the Y-Sequence
The sequence of t-components SY, thus Sρ can be obtained from S by considering
the representative transitions and the cardinality of the t-invariant support. A proce-
dure that parses S and delivers Sρ is decidable and yields always the same Sρ; the out-
line of a simple procedure for the case in which all ι(||Yi||)≠∅, and there are not nested
t-invariants is given below.
Algorithm1. Building SY
Input: S, = {,,... }
Output: Sρ
1. Determine ρ={ρ1, ρ2, ...ρNY} from S and
2. SY ← ε; k ← 1
3. Repeat
a. If S(k)∈ ρ, where S(k)= ρr
b. Then Sρ← Sρ•ρr; SY← SY•τr
c. Endif
4. k ← k+1
5. Until k>|S|
6. Return Sρ
135
The procedure for determining ρ must deal with the case in which some ι(||Yi||)=∅;
in the worst all the transitions in must be tracked in S to determine uniquely the
t-component that is being executed. Furthermore, when there are nested t-
components, a stack can be used to parse the components execution, in particular
when there is nesting at several levels; in this case, regarding the t-component at
higher level ρr, several representative transitions ρr1, ρr2, ... could be determined to go
ahead the execution of the “interrupted” t-component. The structure of the discovered
PN can be also exploited.
In Example 1, SY=τ1τ2τ1τ2τ1τ2... can be straightforward obtained by observing ρ1
and ρ2 in S=...t3...t5...t3...t5.... In Example 2 instead, one must consider two representa-
tive transitions ρ11=t1 ρ12=t3 for one component; S = t1...t4...t3...t1...t4...t3..., thus
SY=τ11τ2τ12τ11τ2τ12... Finally, the sequence of representative transitions Sρ is derived.
In Examples 1 and 2 the sequences are Sρ=t3 t5 t3 t5... and Sρ=t1 t4 t3 t1... respectively.
3.3 Repetitive patterns
Now, the next stage is to determine from Sρ the repetitive pattern that schedules the
execution of the t-components. The problem is similar to that of process discovery but
some other facts must be considered.
3.3.1 Simple patterns
It is easy to see in Example 1 that the Sρ=t3 t5 t3 t5 corresponds to a pattern that can
be represented by a PN N’ that is a cycle p6 t3 p7 t5 p6 with p6 initially marked. Simi-
larly, in Example 2, the pattern N’ derived from Sρ=t1 t4 t3 t1 is a cycle including the
representative transitions; the merging of N’ with N is shown in Figure 6; when the
implicit place (marked) is removed, the simplified PN is shown in Figure 7. Notice
that in previous examples, the refined PNs have one t-component.
t4 t4 t4
p1 t1 p2 t2 p3 t3 p1 t1 p2 t2 p3 t3 p1 t1 p2 t2 p3 t3
Figure 5. Nested cycles Figure 6. Sequenced t-components Figure 7. Simplified PN
3.3.2 Other patterns
Consider a sequence S1=t1 t2 t3 t4 t1 t2 t5 t6 t1 t2 t5 t6 t1 t2 t3 t4 t1 t2 t5 t6 t1 t2 t5 t6 t1...
The invariant-based method discovers the same PN of Figure 2 and the same t-
invariants. Thus, the representative transitions are the same; however, the sequence of
representative transitions is not the same: Sρ=t3 t5 t5 t3 t5 t3...; in the pattern the t-
component ||Y1|| is executed once, whilst ||Y2|| twice. This cyclic pattern, which estab-
lishes a production rate 1-2 for the jobs, is designed as a 2-bounded PN N’ that is
shown in Figure 8. For patterns that handle k jobs given by SY=(τ1)n(τ2)m...(τk)r(τ1)n...
with regular executions (fixed n, m, ..., r) of jobs, the PN can be designed similarly.
Now, consider the discovered PN, which describes the behaviour of a process in-
volving three jobs. The t-invariant supports are: = {t1, t2, t3}, = {t1, t4, t5},
and = {t1, t6, t7}; the representative transitions are: ρ1= t2, ρ2= t4, and ρ3= t6. Be-
136
sides consider that Sρ=t2 t4 t2 t6 t2 t4 t2 t6..., that is, τ1 is executed between the alterna-
tion of τ2 and τ3. The PN N’ that describes this pattern is shown in Figure 10. Notice
that N’ has two invariants; thus, in a recursive manner, being t4 and t6 the representa-
tive transitions of these t-components, N’’ can be obtained (Figure 11). The refined
PN is obtained by merging N with N’’; such a PN, shown in Figure 12 has only a t-
invariant.
p33 p3
t5 t6 t2 t3
t4
p4 t1 t2 p5 p6 p7 p1 t1 p2 p4
t4 t5 p6 t2 p7
p11 2 2
t3 t4 t6 p5 t7
p22 t6
Fig. 8. Sequencing jobs rate Fig.9. Modelling three jobs: N Fig. 10. Sequencing ||Yi||
t2 p3
t3
p6
t4 p1 t1 p 2 p4
t4 t5
p6 t2 p7
p7
p8 p8 p9
p9
t6 t6 p5 t7
Fig. 11. Refining N’: N’’ Fig. 12. The refined PN: N||N’’
4 Remarks and challenges
We have sketched a refining technique for PN discovered by a t-invariant method.
If the scheduling pattern can be determined, t-components relied by N’ become a
single t-component; thus, the refined PN has less exceeding behaviour than the first
discovered PN. In this meta-analysis of a discovered model, the patterns considered in
this short paper are found often in discrete manufacturing systems; however, the strat-
egy to discover more complex patterns is still being studied.
Determining the patterns from SY is an interesting problem. In general, the depend-
ency of occurrence among the τi must be found; this pose a new discovery problem in
which, besides the order of execution of the τi in SY, the regularity of the repetitions of
τi must be established.
References
[1] M. E. Gold, “Language identification in the limit”, Information and Control, 10(5), pp. 447-
474, 1967
[2] D. Angluin, “Queries and Concept Learning”, Machine Learning, vol. 2, pp. 319-342, 1988
[3] M. Meda-Campana, A. Ramirez-Treviño, and E. Lopez-Mellado, “Asymptotic identifica-
tion of discrete event systems”, in Proc. of the 39th IEEE Conf. on Decision and Control,
pp. 2266-2271, 2000
137
[4] M. Meda-Campana and E. Lopez-Mellado, “Identification of concurrent discrete event
systems using Petri nets”, in Proc. of the 17th IMACS World Congress on Computational
and Applied Mathematics, pp. 11-15, 2005
[5] M.P. Cabasino, A. Giua, C. Seatzu, "Identification of Petri nets from knowledge of their
language," Discrete Event Dynamic Systems, Vol. 17, No. 4, pp. 447-474, 2007
[6] M. Dotoli, M. Pia Fanti, A. M. Mangini, and W. Ukovich, “Identification of the unobserv-
able behaviour of industrial automation systems by Petri nets”, Control Engineering Prac-
tice, 19( 9), pp. 958-966, 2011
[7] A. P. Estrada-Vargas, E. Lopez-Mellado, and J.-J. Lesage, “A Black-Box Identification
Method for Automated Discrete-Event Systems,” IEEE Transactions on Automation Sci-
ence and Engineering, Early Access 2015, pp. 1 - 16, DOI: 10.1109/TASE.2015.2445332
[8] A.P. Estrada-Vargas, E. Lopez-Mellado, and J.-J. Lesage, “A comparative analysis of re-
cent identification approaches for discrete event systems”, Mathematical Problems in Engi-
neering, vol. 2010, 2010
[9] M. P. Cabasino, P. Darondeau, M. P. Fanti, and C. Seatzu, “Model identification and syn-
thesis of discrete-event systems”, Contemporary Issues in Systems Science and Engineer-
ing, IEEE/Wiley Press Book Series 2013
[10] W. Van der Aalst, Process Mining: Discovery, “Conformance and Enhancement of Busi-
ness Processes”, Berlin: Springer-Verlag, 2011.
[11] T. Tapia-Flores, E. López-Mellado, A. P. Estrada-Vargas, and J. J. Lesage, “Discovering
Petri Net Models of Discrete Event Processes by Computing T-invariants”. IEEE Transac-
tions on Automation Science and Engineering. May, 2017 DOI:
10.1109/TASE.2017.2682060
[12] E. Rodríguez-Pérez, T. Tapia-Flores, E. López-Mellado, “Identification of Timed Discrete
Event Processes. Building Input-Output Petri Net Models”. ATAED@Petri Nets/ACSD
2016: 153-167. Torun, Poland, June 2016
138