Workflows with transition execution times having exponential distributions are considered. The aim is to determine the overall execution time without looking into the reachability space and its analysis using Markov processes. We concentrate on the so called structural workflows, which are represented with Petri nets constructed by means of specific refinement rules. With each refinement rule (sequence, choice, parallelization, loop) we associate formulas which allow to compute the overall execution time distribution. The class of exponential distributions is too narrow to keep the result within itself. We analyze the so called exponential polynomials, generalizing exponential distributions. They are closed under SUM and MAX functions. This closure property combined with the knowledge of refinements history enables us to find the requested formulas.
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Workflow mining is the task of automatically producing a workflow model from a set of event logs recording sequences of workflow events; each sequence corresponds to a use case or workflow instance. Formal approaches to workflow mining assume that the event log is complete (contains enough information to infer the workflow) which is often not the case. We present a learning approach that relaxes this assumption: if the event log is incomplete, our learning algorithm automatically derives queries about the executability of some event sequences. If a teacher answers these queries, the algorithm is guaranteed to terminate with a correct model. We provide matching upper and lower bounds on the number of queries required by the algorithm, and report on the application of an implementation to some examples.
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Classical workflow nets (WF-nets for short) are an important subclass of Petri nets that are widely used to model and analyze workflow systems. Soundness is a crucial property of workflow systems and guarantees that these systems are deadlock-free and bounded. Aalst et al. proved that the soundness problem is decidable for WF-nets and can be polynomially solvable for free-choice WF-nets. This paper proves that the soundness problem is PSPACE-hard for WF-nets. Furthermore, it is proven that the soundness problem is PSPACE-complete for bounded WF-nets. Based on the above conclusion, it is derived that the soundness problem is also PSPACE-complete for bounded WF-nets with reset or inhibitor arcs (ReWF-nets and InWF-nets for short, resp.). ReWF- and InWF-nets are two extensions to WF-nets and their soundness problems were proven by Aalst et al. to be undecidable. Additionally, we prove that the soundness problem is co-NP-hard for asymmetric-choice WF-nets that are a larger class and can model more cases of interaction and resource allocation than free-choice ones.
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In this paper we consider workflow nets as dynamical systems governed by ordinary difference equations described by a particular class of Petri nets. Workflow nets are a formal model of business processes. Well-formed business processes correspond to sound workflow nets. Even if it seems necessary to require the soundness of workflow nets, there exist business processes with conditional behavior that will not necessarily satisfy the soundness property. In this sense, we propose an analytical method for showing that a workflow net satisfies the classical soundness property using a Petri net. To present our statement, we use Lyapunov stability theory to tackle the classical soundness verification problem for a class of dynamical systems described by Petri nets. This class of Petri nets allows a dynamical model representation that can be expressed in terms of difference equations. As a result, by applying Lyapunov theory, the classical soundness property for workflow nets is solved proving that the Petri net representation is stable. We show that a finite and non-blocking workflow net satisfies the sound property if and only if its corresponding PN is stable, i.e., given the incidence matrix A of the corresponding PN, there exists a Φ strictly positive m vector such that AΦ ≤ 0. The key contribution of the paper is the analytical method itself that satisfies part of the definition of the classical soundness requirements. The method is designed for practical applications, guarantees that anomalies can be detected without domain knowledge, and can be easily implemented into existing commercial systems that do not support the verification of workflows. The validity of the proposed method is successfully demonstrated by application examples.
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