Discovering duplicate tasks in transition systems for the simplification of process models

This work presents a set of methods to improve the understandability of process models. Traditionally, simplification methods trade off quality metrics, such as fitness or precision. Conversely, the methods proposed in this paper produce simplified models while preserving or even increasing fidelity metrics. The first problem addressed in the paper is the discovery of duplicate tasks. A new method is proposed that avoids overfitting by working on the transition system generated by the log. The method is able to discover duplicate tasks even in the presence of concurrency and choice. The second problem is the structural simplification of the model by identifying optional and repetitive tasks. The tasks are substituted by annotated events that allow the removal of silent tasks and reduce the complexity of the model. An important feature of the methods proposed in this paper is that they are independent from the actual miner used for process discovery.Peer ReviewedPostprint (author's final draft

Anti-alignments in conformance checking: the dark side of process models

Conformance checking techniques asses the suitability of a process model in representing an underlying process, observed through a collection of real executions. These techniques suffer from the wellknown state space explosion problem, hence handling process models exhibiting large or even infinite state spaces remains a challenge. One important metric in conformance checking is to asses the precision of the model with respect to the observed executions, i.e., characterize the ability of the model to produce behavior unrelated to the one observed. By avoiding the computation of the full state space of a model, current techniques only provide estimations of the precision metric, which in some situations tend to be very optimistic, thus hiding real problems a process model may have. In this paper we present the notion of antialignment as a concept to help unveiling traces in the model that may deviate significantly from the observed behavior. Using anti-alignments, current estimations can be improved, e.g., in precision checking. We show how to express the problem of finding anti-alignments as the satisfiability of a Boolean formula, and provide a tool which can deal with large models efficiently.Peer ReviewedPostprint (author's final draft

Methodologies for investigating occupational accidents and their use in occupational health and safety research. Literature review.

El objetivo de este trabajo es revisar los principales estudios publicados sobre accidentes de trabajo para reconocer, clasificar y describir las metodologías científicas utilizadas. Para lograr el objetivo planteado utilizamos un método ya implementado y validado, consistente en una extensa revisión de la literatura científica internacional relacionada con las metodologías de investigación de accidentes en seguridad y salud laboral. A continuación, para evaluar la importancia de estas metodologías, analizamos el número de veces que se citan las publicaciones seleccionadas y el factor de impacto de la revista en la que se publicó. Los resultados de esta revisión muestran que en las últimas décadas se han desarrollado muchas metodologías de investigación diferentes. Estas metodologías cubren diferentes áreas de aplicación, cualidades y limitaciones, entendiendo que una investigación exhaustiva de un accidente requiere una combinación de diferentes actividades incluidas en estos métodos. El presente estudio describe cuáles son las metodologías más utilizadas en el ámbito de la investigación de accidentes laborales. Se identificaron un total de 35 metodologías diferentes. Este estudio revela que, incluso a día de hoy, no se dispone de muchas metodologías centradas en el ámbito de la salud y la seguridad en el trabajo. Por otro lado, para desarrollar y avanzar en la aplicación de las técnicas de investigación de accidentes de trabajo, sería recomendable promover estudios que verifiquen la correcta selección y uso de las metodologías en casos reales de accidentes de trabaj

On Deadlockability, Liveness and Reversibility in Subclasses of Weighted Petri Nets

International audienceLiveness, (non-)deadlockability and reversibility are behavioral properties of Petri nets that are fundamental for many real-world systems. Such properties are often required to be mono-tonic, meaning preserved upon any increase of the marking. However, their checking is intractable in general and their monotonicity is not always satisfied. To simplify the analysis of these features, structural approaches have been fruitfully exploited in particular subclasses of Petri nets, deriving the behavior from the underlying graph and the initial marking only, often in polynomial time. In this paper, we further develop these efficient structural methods to analyze deadlockability, live-ness, reversibility and their monotonicity in weighted Petri nets. We focus on the join-free subclass, which forbids synchronizations, and on the homogeneous asymmetric-choice subclass, which allows conflicts and synchronizations in a restricted fashion. For the join-free nets, we provide several structural conditions for checking liveness, (non-)deadlock-ability, reversibility and their monotonicity. Some of these methods operate in polynomial time. Furthermore , in this class, we show that liveness, non-deadlockability and reversibility, taken together or separately, are not always monotonic, even under the assumptions of structural boundedness and structural liveness. These facts delineate more sharply the frontier between monotonicity and non-monotonicity of the behavior in weighted Petri nets, present already in the join-free subclass. In addition, we use part of this new material to correct a flaw in the proof of a previous characterization of monotonic liveness and boundedness for homogeneous asymmetric-choice nets, published in 2004 and left unnoticed

Modeling of Object-Oriented Programs with Petri Net Structured Objects

The article presents a method for constructing a model of an object-oriented program in terms of multilabeled Petri nets. Only encapsulation - one of the three concepts of object-oriented paradigm - is considered. To model a different aspects of encapsulation a Petri net structured object is proposed. It consists of a Petri net defining its behavior and a set of organized access points specifying its structural properties. Formal composition operations to construct a program model from the models of its methods, classes, objects, functions, and modules are introduced and a source code translation algorithm to Petri net representation is proposed. A special section of the article considers in detail a process of model construction of a real object-oriented program (OOP). Source code of the program, figures with Petri net objects modeling different elements of the program and the resulting model of the program are presented

Can recurrent neural networks learn process model structure?

Various methods using machine and deep learning have been proposed to tackle different tasks in predictive process monitoring, forecasting for an ongoing case e.g. the most likely next event or suffix, its remaining time, or an outcome-related variable. Recurrent neural networks (RNNs), and more specifically long short-term memory nets (LSTMs), stand out in terms of popularity. In this work, we investigate the capabilities of such an LSTM to actually learn the underlying process model structure of an event log. We introduce an evaluation framework that combines variant-based resampling and custom metrics for fitness, precision and generalization. We evaluate 4 hypotheses concerning the learning capabilities of LSTMs, the effect of overfitting countermeasures, the level of incompleteness in the training set and the level of parallelism in the underlying process model. We confirm that LSTMs can struggle to learn process model structure, even with simplistic process data and in a very lenient setup. Taking the correct anti-overfitting measures can alleviate the problem. However, these measures did not present themselves to be optimal when selecting hyperparameters purely on predicting accuracy. We also found that decreasing the amount of information seen by the LSTM during training, causes a sharp drop in generalization and precision scores. In our experiments, we could not identify a relationship between the extent of parallelism in the model and the generalization capability, but they do indicate that the process' complexity might have impact

A survey of petri nets slicing

Petri nets slicing is a technique that aims to improve the verification of systems modeled in Petri nets. Petri nets slicing was first developed to facilitate debugging but then used for the alleviation of the state space explosion problem for the model checking of Petri nets. In this article, different slicing techniques are studied along with their algorithms introducing: i) a classification of Petri nets slicing algorithms based on their construction methodology and objective (such as improving state space analysis or testing), ii) a qualitative and quantitative discussion and comparison of major differences such as accuracy and efficiency, iii) a syntactic unification of slicing algorithms that improve state space analysis for easy and clear understanding, and iv) applications of slicing for multiple perspectives. Furthermore, some recent improvements to slicing algorithms are presented, which can certainly reduce the slice size even for strongly connected nets. A noteworthy use of this survey is for the selection and improvement of slicing techniques for optimizing the verification of state event models