Formal analysis of ubiquitous computing environments through the APEX framework

Ubiquitous computing (ubicomp) systems involve complex interactions between multiple devices and users. This com-plexity makes it difficult to establish whether: (1) observa-tions made about use are truly representative of all possible interactions; (2) desirable characteristics of the system are true in all possible scenarios. To address these issues, tech-niques are needed that support an exhaustive analysis of a system’s design. This paper demonstrates one such exhaus-tive analysis technique that supports the early evaluation of alternative designs for ubiquitous computing environments. The technique combines models of behavior within the environment with a virtual world that allows its simulation. The models support checking of properties based on pat-terns. These patterns help the analyst to generate and verify relevant properties. Where these properties fail then scenar-ios suggested by the failure provide an important aid to redesign. The proposed technique uses APEX, a framework for rapid prototyping of ubiquitous environments based on Petri nets. The approach is illustrated through a smart li-brary example. Its benefits and limitations are discussed.(undefined

Contributions to the deadlock problem in multithreaded software applications observed as Resource Allocation Systems

Desde el punto de vista de la competencia por recursos compartidos sucesivamente reutilizables, se dice que un sistema concurrente compuesto por procesos secuenciales está en situación de bloqueo si existe en él un conjunto de procesos que están indefinidamente esperando la liberación de ciertos recursos retenidos por miembros del mismo conjunto de procesos. En sistemas razonablemente complejos o distribuidos, establecer una política de asignación de recursos que sea libre de bloqueos puede ser un problema muy difícil de resolver de forma eficiente. En este sentido, los modelos formales, y particularmente las redes de Petri, se han ido afianzando como herramientas fructíferas que permiten abstraer el problema de asignación de recursos en este tipo de sistemas, con el fin de abordarlo analíticamente y proveer métodos eficientes para la correcta construcción o corrección de estos sistemas. En particular, la teoría estructural de redes de Petri se postula como un potente aliado para lidiar con el problema de la explosión de estados inherente a aquéllos. En este fértil contexto han florecido una serie de trabajos que defienden una propuesta metodológica de diseño orientada al estudio estructural y la correspondiente corrección física del problema de asignación de recursos en familias de sistemas muy significativas en determinados contextos de aplicación, como el de los Sistemas de Fabricación Flexible. Las clases de modelos de redes de Petri resultantes asumen ciertas restricciones, con significado físico en el contexto de aplicación para el que están destinadas, que alivian en buena medida la complejidad del problema. En la presente tesis, se intenta acercar ese tipo de aproximación metodológica al diseño de aplicaciones software multihilo libres de bloqueos. A tal efecto, se pone de manifiesto cómo aquellas restricciones procedentes del mundo de los Sistemas de Fabricación Flexible se muestran demasiado severas para aprehender la versatilidad inherente a los sistemas software en lo que respecta a la interacción de los procesos con los recursos compartidos. En particular, se han de resaltar dos necesidades de modelado fundamentales que obstaculizan la mera adopción de antiguas aproximaciones surgidas bajo el prisma de otros dominios: (1) la necesidad de soportar el anidamiento de bucles no desplegables en el interior de los procesos, y (2) la posible compartición de recursos no disponibles en el arranque del sistema pero que son creados o declarados por un proceso en ejecución. A resultas, se identifica una serie de requerimientos básicos para la definición de un tipo de modelos orientado al estudio de sistemas software multihilo y se presenta una clase de redes de Petri, llamada PC2R, que cumple dicha lista de requerimientos, manteniéndose a su vez respetuosa con la filosofía de diseño de anteriores subclases enfocadas a otros contextos de aplicación. Junto con la revisión e integración de anteriores resultados en el nuevo marco conceptual, se aborda el estudio de propiedades inherentes a los sistemas resultantes y su relación profunda con otros tipos de modelos, la confección de resultados y algoritmos eficientes para el análisis estructural de vivacidad en la nueva clase, así como la revisión y propuesta de métodos de resolución de los problemas de bloqueo adaptadas a las particularidades físicas del dominio de aplicación. Asimismo, se estudia la complejidad computacional de ciertas vertientes relacionadas con el problema de asignación de recursos en el nuevo contexto, así como la traslación de los resultados anteriormente mencionados sobre el dominio de la ingeniería de software multihilo, donde la nueva clase de redes permite afrontar problemas inabordables considerando el marco teórico y las herramientas suministradas para subclases anteriormente explotadas

A framework for the analysis and comparison of process mining algorithms

Process mining algorithms use event logs to learn and reason about business processes. Although process mining is essentially a machine learning task, little work has been done on systematically analysing algorithms to understand their fundamental properties, such as how much data is needed for confidence in mining. Nor does any rigorous basis exist on which to choose between algorithms and representations, or compare results. We propose a framework for analysing process mining algorithms. Processes are viewed as distributions over traces of activities and mining algorithms as learning these distributions. We use probabilistic automata as a unifying representation to which other representation languages can be converted. To validate the theory we present analyses of the Alpha and Heuristics Miner algorithms under the framework, and two practical applications. We propose a model of noise in process mining and extend the framework to mining from ‘noisy’ event logs. From the probabilities and sub-structures in a model, bounds can be given for the amount of data needed for mining. We also consider mining in non-stationary environments, and a method for recovery of the sequence of changed models over time. We conclude by critically evaluating this framework and suggesting directions for future research

Mining complete, precise and simple process models

Process discovery algorithms are generally used to discover the underlying process that has been followed to achieve an objective. In general, these algorithms do not take into account any domain knowledge to derive process models, allowing to apply them in a general manner. However, depending on the selected approach, a different kind of process models can be discovered, as each technique has its strengths and weaknesses, e.g., the expressiveness of the used notation. Hence, it is important to take into account the requirements of the domain when deciding which algorithm to use, as the correct assumptions can lead to richer process models. For instance, among the different domains of application of process mining we can identify several fields that share an interesting requirement about the discovered process models. In security audits, discovered processes have to fulfill strict requisites. This means that the process model should reproduce as much behavior as possible; otherwise some violations may go undetected (replay fitness). On the other hand, in order to avoid false positives, process models should reproduce only the recorded behavior (precision). Finally, process models should be easily readable to better detect deviations (simplicity). Another clear example concerns the educational domain, as in order to be of value for both teachers and learners, a discovered learning process should satisfy the aforementioned requirements. That is, to guarantee feasible and correct evaluations, teachers need to access to all the activities performed by learners, thereby the learning process should be able to reproduce as much behavior as possible (replay fitness). Furthermore, the learning process should focus on the recorded behavior seen in the event log (precision), i.e., show only what the students did, and not what they might have done, while being easily interpretable by the teachers (simplicity). One of the previous requirements is related to the readability of process models: simplicity. In process mining, one of the identified challenges is the appropriate visualization of process models, i.e., to present the results of process discovery in such a way that people actually gain insights about the process. Process models that are unnecessary complex can hinder the real behavior of the process rather than to provide an intuition of what is really happening in an organization. However, achieving a good level of readability is not always straightforward, for instance, due the used representation. Within the different approaches focused to reduce the complexity of a process model, the interest in this PhD Thesis relies on two techniques. On the one hand, to improve the readability of an already discovered process model through the inclusion of duplicate labels. On the other hand, the hierarchization of a process model, i.e., to provide a well known structure to the process model. However, regarding the latter, this technique requires to take into account domain knowledge, as different domains may rely on different requirements when improving the readability of the process model. In other words, in order to improve the interpretability and understandability of a process model, the hierarchization has to be driven by the domain. To sum up, concerning the aim of this PhD Thesis, we can identify two main topics of interest. On the one hand, we are interested in retrieving process models that reproduce as much behavior recorded in the log as possible, without introducing unseen behavior. On the other hand, we try to reduce the complexity of the mined models in order to improve their readability. Hence, the aim of this PhD Thesis is to discover process models considering replay fitness, precision and simplicity, while paying special attention in retrieving highly interpretable process models

starMC: an automata based CTL* model checker

Model-checking of temporal logic formulae is a widely used technique for the verification of systems. CTL [Image: see text] is a temporal logic that allows to consider an intermix of both branching behaviours (like in CTL) and linear behaviours (LTL), overcoming the limitations of LTL (that cannot express “possibility”) and CTL (cannot fully express fairness). Nevertheless CTL [Image: see text] model-checkers are uncommon. This paper presents (1) the algorithms for a fully symbolic automata-based approach for CTL [Image: see text] , and (2) their implementation in the open-source tool starMC, a CTL [Image: see text] model checker for systems specified as Petri nets. Testing has been conducted on thousands of formulas over almost a hundred models. The experiments show that the fully symbolic automata-based approach of starMC can compute the set of states that satisfy a CTL [Image: see text] formula for very large models (non trivial formulas for state spaces larger than 10(480) states are evaluated in less than a minute)

Petri nets for modelling metabolic pathways: a survey

In the last 15 years, several research efforts have been directed towards the representation and the analysis of metabolic pathways by using Petri nets. The goal of this paper is twofold. First, we discuss how the knowledge about metabolic pathways can be represented with Petri nets. We point out the main problems that arise in the construction of a Petri net model of a metabolic pathway and we outline some solutions proposed in the literature. Second, we present a comprehensive review of recent research on this topic, in order to assess the maturity of the field and the availability of a methodology for modelling a metabolic pathway by a corresponding Petri net

Partitionnement d’instances de processus basé sur les techniques de conformité de modèles

As event data becomes an ubiquitous source of information, data science techniques represent an unprecedented opportunity to analyze and react to the processes that generate this data. Process Mining is an emerging field that bridges the gap between traditional data analysis techniques, like Data Mining, and Business Process Management. One core value of Process Mining is the discovery of formal process models like Petri nets or BPMN models which attempt to make sense of the events recorded in logs. Due to the complexity of event data, automated process discovery algorithms tend to create dense process models which are hard to interpret by humans. Fortunately, Conformance Checking, a sub-field of Process Mining, enables relating observed and modeled behavior, so that humans can map these two pieces of process information. Conformance checking is possible through alignment artefacts, which associate process models and event logs. Different types of alignment artefacts exist, namely alignments, multi-alignments and anti-alignments. Currently, only alignment artefacts are deeply addressed in the literature. It allows to relate the process model to a given process instance. However, because many behaviors exist in logs, identifying an alignment per process instance hinders the readability of the log-to-model relationships.The present thesis proposes to exploit the conformance checking artefacts for clustering the process executions recorded in event logs, thereby extracting a restrictive number of modeled representatives. Data clustering is a common method for extracting information from dense and complex data. By grouping objects by similarities into clusters, data clustering enables to mine simpler datasets which embrace the similarities and the differences contained in data. Using the conformance checking artefacts in a clustering approach allows to consider a reliable process model as a baseline for grouping the process instances. Hence, the discovered clusters are associated with modeled artefacts, that we call model-based trace variants, which provides opportune log-to-model explanations.From this motivation, we have elaborated a set of methods for computing conformance checking artefacts. The first contribution is the computation of a unique modeled behavior that represents of a set of process instances, namely multi-alignment. Then, we propose several alignment-based clustering approaches which provide clusters of process instances associated to a modeled artefact. Finally, we highlight the interest of anti-alignment for extracting deviations of process models with respect to the log. This latter artefact enables to estimate model precision, and we show its impact in model-based clustering. We provide SAT encoding for all the proposed techniques. Heuristic algorithms are then added to deal with computing capacity of today’s computers, at the expense of loosing optimality.Les données d'événements devenant une source d'information omniprésente, les techniques d'analyse de données représentent une opportunité sans précédent pour étudier et réagir aux processus qui génèrent ces données. Le Process Mining est un domaine émergent qui comble le fossé entre les techniques d'analyse de données, comme le Data Mining, et les techniques de management des entreprises, à savoir, le Business Process Management. L'une des bases fondamentales du Process Mining est la découverte de modèles de processus formels tels que les réseaux de Petri ou les modèles BPMN qui tentent de donner un sens aux événements enregistrés dans les journaux. En raison de la complexité des données d'événements, les algorithmes de découverte de processus ont tendance à créer des modèles de processus denses, qui sont difficiles à interpréter par les humains. Heureusement, la Vérification de Conformité, un sous-domaine du Process Mining, permet d'établir des liens entre le comportement observé et le comportement modélisé, facilitant ainsi la compréhension des correspondance entre ces deux éléments d'information sur les processus. La Vérification de Conformité est possible grâce aux artefacts d'alignement, qui associent les modèles de processus et les journaux d'événements. Il existe différents types d'artefacts d'alignement, à savoir les alignements, les multi-alignements et les anti-alignements. Actuellement, seuls les alignements sont traités en profondeur dans la littérature scientifique. Un alignement permet de relier le modèle de processus à une instance de processus donnée. Cependant, étant donné que de nombreux comportements existent dans les logs, l'identification d'un alignement par instance de processus nuit à la lisibilité des relations log-modèle.La présente thèse propose d'exploiter les artefacts de conformité pour regrouper les exécutions de processus enregistrées dans les journaux d'événements, et ainsi extraire un nombre restrictif de représentations modélisées. Le regroupement de données, communément appelé partitionnement, est une méthode courante pour extraire l'information de données denses et complexes. En regroupant les objets par similarité dans des clusters, le partitionnement permet d'extraire des ensembles de données plus simples qui englobent les similarités et les différences contenues dans les données. L'utilisation des artefacts de conformité dans une approche de partitionnement permet de considérer un modèle de processus fiable comme une base de référence pour le regroupement des instances de processus. Ainsi, les clusters découverts sont associés à des artefacts modélisés, que nous appelons variantes modélisées des traces, ce qui fournit des explications opportunes sur les relations entre le journal et le modèle.Avec cette motivation, nous avons élaboré un ensemble de méthodes pour calculer les artefacts de conformité. La première contribution est le calcul d'un comportement modélisé unique qui représente un ensemble d'instances de processus, à savoir le multi-alignement. Ensuite, nous proposons plusieurs approches de partitionnement basées sur l'alignement qui fournissent des clusters d'instances de processus associés à un artefact modélisé. Enfin, nous soulignons l'intérêt de l'anti-alignement pour extraire les déviations des modèles de processus par rapport au journal. Ce dernier artefact permet d'estimer la précision du modèle. Nous montrons son impact sur nos approches de partitionnement basées sur des modèles. Nous fournissons un encodage SAT pour toutes les techniques proposées. Des heuristiques sont ensuite ajoutées pour tenir compte de la capacité de calcul des ordinateurs actuels, au prix d'une perte d'optimalité