Abstract:The so-called synthesis problem for nets, which consists in deciding whether a given graph is isomorphic to the case graph of some net, and then constructing the net, has been solved in the litterature for various types of nets, ranging from elementary nets to Petri nets. The common principle for the synthesis is the idea of regions in graphs, representing possible extensions of places in nets. However, no practical algorithm has been defined so far for the synthesis. We give here exphcit algorithms solving in… Show more
“…In this paper, we consider language-based region theory [8,13,17], of which [17] presents a nice overview. In [17], the authors show how for different classes of languages (step languages, regular languages and partial languages) a Petri net can be derived such that the resulting net is the smallest Petri net in which the words in the language are possible firing sequences.…”
Section: Theory Of Regionsmentioning
confidence: 99%
“…each solution (x, y, c) of the inequation system can be regarded in the context of a Petri net, where the region corresponds to a feasible place with preset {t|t ∈ T, x(t) = 1} and postset {t|t ∈ T, y(t) = 1}, and initially marked with c tokens. Note that we do not assume arc-weights, where the authors of [8,11,13,17] do. However, in process modelling languages, such arc weights typically do not exist, hence we decided to ignore them.…”
Section: Language-based Theory Of Regionsmentioning
Abstract. The research domain of process discovery aims at constructing a process model (e.g. a Petri net) which is an abstract representation of an execution log. Such a Petri net should (1) be able to reproduce the log under consideration and (2) be independent of the number of cases in the log. In this paper, we present a process discovery algorithm where we use concepts taken from the language-based theory of regions, a wellknown Petri net research area. We identify a number of shortcomings of this theory from the process discovery perspective, and we provide solutions based on integer linear programming.
“…In this paper, we consider language-based region theory [8,13,17], of which [17] presents a nice overview. In [17], the authors show how for different classes of languages (step languages, regular languages and partial languages) a Petri net can be derived such that the resulting net is the smallest Petri net in which the words in the language are possible firing sequences.…”
Section: Theory Of Regionsmentioning
confidence: 99%
“…each solution (x, y, c) of the inequation system can be regarded in the context of a Petri net, where the region corresponds to a feasible place with preset {t|t ∈ T, x(t) = 1} and postset {t|t ∈ T, y(t) = 1}, and initially marked with c tokens. Note that we do not assume arc-weights, where the authors of [8,11,13,17] do. However, in process modelling languages, such arc weights typically do not exist, hence we decided to ignore them.…”
Section: Language-based Theory Of Regionsmentioning
Abstract. The research domain of process discovery aims at constructing a process model (e.g. a Petri net) which is an abstract representation of an execution log. Such a Petri net should (1) be able to reproduce the log under consideration and (2) be independent of the number of cases in the log. In this paper, we present a process discovery algorithm where we use concepts taken from the language-based theory of regions, a wellknown Petri net research area. We identify a number of shortcomings of this theory from the process discovery perspective, and we provide solutions based on integer linear programming.
“…The aim of the theory of regions (Badouel et al, 1995) is to decide wether a given automaton is isomorphic to the reachability graph of a net, then constructing it. Ghaffari et al (Ghaffari et al, 2003) are the first who proposed an adaptation of this theory for the synthesis controller problem using Petri nets.…”
“…The application of statebased region algorithms to process mining was studied in [6,9,21]. Algorithms based on regions of languages were presented in [7,14,18] and then applied to process mining [8,24]. State-based region algorithms and algorithms based on regions of languages map discovered regions into places of a target Petri net.…”
Abstract. Process mining aims to discover and analyze processes by extracting information from event logs. Process mining discovery algorithms deal with large data sets to learn automatically process models. As more event data become available there is the desire to learn larger and more complex process models. To tackle problems related to the readability of the resulting model and to ensure tractability, various decomposition methods have been proposed. This paper presents a novel decomposition approach for discovering more readable models from event logs on the basis of a priori knowledge about the event log structure: regular and special cases of the process execution are treated separately. The transition system, corresponding to a given event log, is decomposed into a regular part and a specific part. Then one of the known discovery algorithms is applied to both parts, and finally these models are combined into a single process model. It is proven, that the structural and behavioral properties of submodels are inherited by the unified process model. The proposed discovery algorithm is illustrated using a running example.
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