Detecting anomalies in computer-based systems, including Cyber-Physical Systems (CPS), has attracted a large interest recently. Behavioral anomalies represent deviations from what is regarded as the nominal expected behavior of the system. Both Process science and Data science can yield satisfactory results in detecting behavioral anomalies. Within Process Mining, Conformance Checking addresses data retrieval and the connection of data to behavioral models with the aim to detect behavioral anomalies. Nowadays, computer-based systems are increasingly complex and require appropriate validation, monitoring, and maintenance techniques. Within complex computer-based systems, the European Rail Traffic Management System/European Train Control System (ERTMS/ETCS) represents the specification of a standard Railway System integrating heterogeneous hardware and software components, with the aim of providing international interoperability with trains seemingly interacting within standardized infrastructures. Compliance with the standard as well as expected behavior is essential, considering the criticality of the system in terms of performance, availability, and safety. To that aim, a Process Mining Conformance Checking process can be employed to validate the requirements through run-time model-checking techniques against design-time process models. A Process Mining Conformance Checking methodology has been developed and applied with the goal of validating the behavior exposed by an ERTMS/ETCS system during the execution of specific scenarios. The methodology has been tested and demonstrated correct classification of valid behaviors exposed by the ERTMS/ETCS system prototype. Results also showed that the Fitness metric developed in the methodology allows the detection of latent errors in the system before they can generate any failures.

L'évolution rapide dans les environnements métier d'aujourd'hui impose de nouveaux défis pour la gestion efficace et rentable des processus métiers. Dans un tel environnement très dynamique, la conception des processus métiers devient une tâche fastidieuse, source d'erreurs et coûteuse. Par conséquent, l'adoption d'une approche permettant la réutilisation et l'adaptabilité devient un besoin urgent pour une conception de processus prospère. Les modèles de processus configurables récemment introduits représentent l'une des solutions recherchées permettant une conception de processus par la réutilisation, tout en offrant la flexibilité. Un modèle de processus configurable est un modèle générique qui intègre de multiples variantes de procédés d'un même processus métier à travers des points de variation. Ces points de variation sont appelés éléments configurables et permettent de multiples options de conception dans le modèle de processus. Un modèle de processus configurable doit être configuré selon une exigence spécifique en sélectionnant une option de conception pour chaque élément configurable.Les activités de recherche récentes sur les modèles de processus configurables ont conduit à la spécification des langages de modélisation de processus configurables comme par exemple configurable Event-Driven Process Chain (C-EPC) qui étend la notation de l'EPC avec des éléments configurables. Depuis lors, la question de la conception et de la configuration des modèles de processus configurables a été étudiée. D'une part, puisque les modèles de processus configurables ont tendance à être très complexe avec un grand nombre d'éléments configurables, de nombreuses approches automatisées ont été proposées afin d'assister leur conception. Cependant, les approches existantes proposent de recommander des modèles de processus configurables entiers qui sont difficiles à réutiliser, nécessitent un temps complexe de calcul et peuvent confondre le concepteur du processus. D'autre part, les résultats de la recherche sur la conception des modèles de processus configurables ont mis en évidence la nécessité des moyens de soutien pour configurer le processus. Par conséquent, de nombreuses approches ont proposé de construire un système de support de configuration pour aider les utilisateurs finaux à sélectionner les choix de configuration souhaitables en fonction de leurs exigences. Cependant, ces systèmes sont actuellement créés manuellement par des experts du domaine qui est sans aucun doute une tâche fastidieuse et source d'erreurs .Dans cette thèse, nous visons à automatiser le soutien de la variabilité dans les modèles de processus configurables. Notre objectif est double: (i) assister la conception des processus configurables d'une manière à ne pas confondre les concepteurs par des recommandations complexes et (i) assister la création des systèmes de soutien de configuration afin de libérer les analystes de processus de la charge de les construire manuellement. Pour atteindre le premier objectif, nous proposons d'apprendre de l'expérience acquise grâce à la modélisation des processus passés afin d'aider les concepteurs de processus avec des fragments de processus configurables. Les fragments proposés inspirent le concepteur du processus pour compléter la conception du processus en cours. Pour atteindre le deuxième objectif, nous nous rendons compte que les modèles de processus préalablement conçus et configurés contiennent des connaissances implicites et utiles pour la configuration de processus. Par conséquent, nous proposons de bénéficier de l'expérience acquise grâce à la modélisation et à la configuration passées des processus afin d'aider les analystes de processus dans la construction de leurs systèmes de support de configuration. / Today's fast changing environment imposes new challenges for effective management of business processes. In such a highly dynamic environment, the business process design becomes time-consuming, error-prone, and costly. Therefore, seeking reuse and adaptability is a pressing need for a successful business process design. Configurable reference models recently introduced were a step toward enabling a process design by reuse while providing flexibility. A configurable process model is a generic model that integrates multiple process variants of a same business process in a given domain through variation points. These variation points are referred to as configurable elements and allow for multiple design options in the process model. A configurable process model needs to be configured according to a specific requirement by selecting one design option for each configurable element.Recent research activities on configurable process models have led to the specification of configurable process modeling notations as for example configurable Event-Driven Process Chain (C-EPC) that extends the EPC notation with configurable elements. Since then, the issue of building and configuring configurable process models has been investigated. On the one hand, as configurable process models tend to be very complex with a large number of configurable elements, many automated approaches have been proposed to assist their design. However, existing approaches propose to recommend entire configurable process models which are difficult to reuse, cost much computation time and may confuse the process designer. On the other hand, the research results on configurable process model design highlight the need for means of support to configure the process. Therefore, many approaches proposed to build a configuration support system for assisting end users selecting desirable configuration choices according to their requirements. However, these systems are currently manually created by domain experts which is undoubtedly a time-consuming and error-prone task.In this thesis, we aim at automating the support of the variability in configurable process models. Our objective is twofold: (i) assisting the configurable process design in a fin-grained way using configurable process fragments that are close to the designers interest and (ii) automating the creation of configuration support systems in order to release the process analysts from the burden of manually building them. In order to achieve the first objective, we propose to learn from the experience gained through past process modeling in order to assist the process designers with configurable process fragments. The proposed fragments inspire the process designer to complete the design of the ongoing process. To achieve the second objective, we realize that previously designed and configured process models contain implicit and useful knowledge for process configuration. Therefore, we propose to benefit from the experience gained through past process modeling and configuration in order to assist process analysts building their configuration support systems. Such systems assist end users interactively configuring the process by recommending suitable configuration decisions.

De plus en plus les organisations adoptent les systèmes d'informations sensibles aux processus basés sur Cloud en tant qu'un environnement pour gérer et exécuter des processus dans le Cloud dans l'objectif de partager et de déployer leurs applications de manière optimale. Cela est particulièrement vrai pour les grandes organisations ayant des succursales opérant dans des différentes régions avec des processus considérablement similaires. Telles organisations doivent soutenir de nombreuses variantes du même processus en raison de la culture locale de leurs succursales, de leurs règlements, etc. Cependant, le développement d'une nouvelle variante de processus à partir de zéro est sujet à l'erreur et peut prendre beaucoup du temps. Motivés par le paradigme "la conception par la réutilisation", les succursales peuvent collaborer pour développer de nouvelles variantes de processus en apprenant de leurs processus similaires. Ces processus sont souvent hétérogènes, ce qui empêche une interopérabilité facile et dynamique entre les différentes succursales. Une variante de processus est un ajustement d'un modèle de processus afin de s'adapter d'une façon flexible aux besoins spécifiques. De nombreuses recherches dans les universités et les industries visent à faciliter la conception des variantes de processus. Plusieurs approches ont été développées pour aider les concepteurs de processus en recherchant des modèles de processus métier similaires ou en utilisant des modèles de référence. Cependant, ces approches sont lourdes, longues et sujettes à des erreurs. De même, telles approches recommandent des modèles de processus pas pratiques pour les concepteurs de processus qui ont besoin d'ajuster une partie spécifique d'un modèle de processus. En fait, les concepteurs de processus peuvent mieux développer des variantes de processus ayant une approche qui recommande un ensemble bien défini d'activités à partir d'un modèle de processus défini comme un fragment de processus. Les grandes organisations multi-sites exécutent les variantes de processus BP dans l'environnement Cloud pour optimiser le déploiement et partager les ressources communes. Cependant, ces ressources Cloud peuvent être décrites en utilisant des différents standards de description des ressources Cloud ce qui empêche l'interopérabilité entre les différentes succursales. Dans cette thèse, nous abordons les limites citées ci-dessus en proposant une approche basée sur les ontologies pour peupler sémantiquement une base de connaissance commune de processus et de ressources Cloud, ce qui permet une interopérabilité entre les succursales de l'organisation. Nous construisons notre base de connaissance en étendant les ontologies existantes. Ensuite, nous proposons une approche pour exploiter cette base de connaissances afin de supporter le développement des variantes BP. De plus, nous adoptons un algorithme génétique pour allouer d'une manière optimale les ressources Cloud aux BPs. Pour valider notre approche, nous développons deux preuves de concepts et effectuons des expériences sur des ensembles de données réels. Les résultats expérimentaux montrent que notre approche est réalisable et précise dans des cas d'utilisation réels / More and more organizations are adopting cloud-based Process-Aware Information Systems (PAIS) to manage and execute processes in the cloud as an environment to optimally share and deploy their applications. This is especially true for large organizations having branches operating in different regions with a considerable amount of similar processes. Such organizations need to support many variants of the same process due to their branches' local culture, regulations, etc. However, developing new process variant from scratch is error-prone and time consuming. Motivated by the "Design by Reuse" paradigm, branches may collaborate to develop new process variants by learning from their similar processes. These processes are often heterogeneous which prevents an easy and dynamic interoperability between different branches. A process variant is an adjustment of a process model in order to flexibly adapt to specific needs. Many researches in both academics and industry are aiming to facilitate the design of process variants. Several approaches have been developed to assist process designers by searching for similar business process models or using reference models. However, these approaches are cumbersome, time-consuming and error-prone. Likewise, such approaches recommend entire process models which are not handy for process designers who need to adjust a specific part of a process model. In fact, process designers can better develop process variants having an approach that recommends a well-selected set of activities from a process model, referred to as process fragment. Large organizations with multiple branches execute BP variants in the cloud as environment to optimally deploy and share common resources. However, these cloud resources may be described using different cloud resources description standards which prevent the interoperability between different branches. In this thesis, we address the above shortcomings by proposing an ontology-based approach to semantically populate a common knowledge base of processes and cloud resources and thus enable interoperability between organization's branches. We construct our knowledge base built by extending existing ontologies. We thereafter propose an approach to mine such knowledge base to assist the development of BP variants. Furthermore, we adopt a genetic algorithm to optimally allocate cloud resources to BPs. To validate our approach, we develop two proof of concepts and perform experiments on real datasets. Experimental results show that our approach is feasible and accurate in real use-cases

En este trabajo se propone un modelo integrado de protección de eventos para resguardar la información del paciente utilizando minería de procesos y visualización de datos. Por ello, el estándar 27001 se utiliza como relación para detectar diversos tipos de ataques informáticos orientados a la evaluación de datos recolectados en procesos de negocio, con el propósito de mejorar la gestión de sus riesgos de seguridad de la información del establecimiento médico. El modelo propuesto se basa en la aplicación de un conjunto de herramientas de análisis de ataques informáticos para aumentar el nivel de seguridad de los procesos de la empresa. La propuesta está conformada por 6 fases. 1. Evaluar riesgos, 2. Implementar controles, 3. Definir un plan de tratamiento, 4. Minería de Procesos, 5. Visualización de Datos y 6. Evaluación de Resultados. La propuesta fue validada mediante un caso de un ciberataque a un establecimiento médico el cual no contaba con controles y planes de contingencia adecuados dentro de sus procesos de negocio. Los resultados preliminares muestran que ante al apoyo de los instrumentos de nuestro modelo el nivel de seguridad ha aumentado en un 25% con nuestra propuesta. / In this work, an integrated event protection model is proposed to protect patient information using process mining and data visualization. Therefore, standard 27001 is used as a relationship to detect various types of computer attacks aimed at evaluating data collected in business processes, to improve the management of its information security risks in the medical establishment. The proposed model is based on the application of a set of computer attack analysis tools to increase the level of security of the company's processes. The proposal is made up of 6 phases. 1. Assess risks, 2. Implement controls, 3. Define a treatment plan, 4. Process Mining, 5. Data visualization and 6. Results evaluation. The proposal was validated through a case of a cyber-attack on a medical establishment which did not have adequate controls and contingency plans within its business processes. Preliminary results show that with the support of the instruments of our model, the level of security has increased by 25% with our proposal. / Trabajo de investigación

[pt] Procedimentos e processos são essenciais para garantir a qualidade de qualquer operação. Porém, o processo realizado na prática nem sempre está de acordo com o processo idealizado. Além disso, uma análise mais refinada de gargalos e inconsistências só é possível a partir do registro de eventos do processo (log). Mineração de processos (process mining) é uma área que reúne um conjunto de métodos para reconstruir, monitorar e aprimorar um processo a partir de seu registro de eventos. Mas, ao aplicar as soluções já existentes no log de uma unidade de coqueamento retardado, os resultados foram insatisfatórios. O núcleo do problema está na forma como o log está estruturado, carecendo de uma identificação de casos, essencial para a mineração do processo. Para contornar esse problema, aplicamos agrupamento hierárquico aglomerativo no log, separando as válvulas em grupos que exercem uma função na operação. Desenvolvemos uma ferramenta (PLANTSTATE) para avaliar a qualidade desses grupos no contexto da planta e ajustar conforme a necessidade do domínio. Identificando os momentos de ativação desses grupos no log chegamos a uma estrutura de sequência e paralelismo entre os grupos. Finalmente, propomos um modelo capaz de representar as relações entre os grupos, resultando em um processo que representa a operações em uma unidade de coqueamento retardado. / [en] Procedures and processes are essential to guarantee the quality of any operation. However, processes carried out in the real world are not always in accordance with the imagined process. Furthermore, a more refined analysis of obstacles and inconsistencies is only possible from the process events record (log). Process mining is an area that brings together a set of methods to rebuild, monitor and improve processes from their log. Nevertheless, when applying existing solutions to the log of a delayed coker unit, the results were unsatisfactory. The core of the problem is how the log is structured, lacking a case identification, essential for process mining. To deal with this issue, we apply agglomerative hierarchical clustering in the log, separating the valves into groups that perform a task in an operation. We developed a tool (PLANTSTATE) to assess the quality of these groups in the context of the plant and to adjust in accord to the needs of the domain. By identifying the moments of activation of these groups in the log we arrive at a structure of sequence and parallelism between the groups. Finally, we propose a model capable of representing the relationships between groups, resulting in a process that represents the operations in a delayed coker unit.

Within the architecture, engineering, and construction (AEC) domain, simulation modeling is mainly used to facilitate decision-making by enabling the assessment of different operational plans and resource arrangements, that are otherwise difficult (if not impossible), expensive, or time consuming to be evaluated in real world settings. The accuracy of such models directly affects their reliability to serve as a basis for important decisions such as project completion time estimation and resource allocation. Compared to other industries, this is particularly important in construction and infrastructure projects due to the high resource costs and the societal impacts of these projects. Discrete event simulation (DES) is a decision making tool that can benefit the process of design, control, and management of construction operations. Despite recent advancements, most DES models used in construction are created during the early planning and design stage when the lack of factual information from the project prohibits the use of realistic data in simulation modeling. The resulting models, therefore, are often built using rigid (subjective) assumptions and design parameters (e.g. precedence logic, activity durations). In all such cases and in the absence of an inclusive methodology to incorporate real field data as the project evolves, modelers rely on information from previous projects (a.k.a. secondary data), expert judgments, and subjective assumptions to generate simulations to predict future performance. These and similar shortcomings have to a large extent limited the use of traditional DES tools to preliminary studies and long-term planning of construction projects. In the realm of the business process management, process mining as a relatively new research domain seeks to automatically discover a process model by observing activity records and extracting information about processes. The research presented in this Ph.D. Dissertation was in part inspired by the prospect of construction process mining using sensory data collected from field agents. This enabled the extraction of operational knowledge necessary to generate and maintain the fidelity of simulation models. A preliminary study was conducted to demonstrate the feasibility and applicability of data-driven knowledge-based simulation modeling with focus on data collection using wireless sensor network (WSN) and rule-based taxonomy of activities. The resulting knowledge-based simulation models performed very well in properly predicting key performance measures of real construction systems. Next, a pervasive mobile data collection and mining technique was adopted and an activity recognition framework for construction equipment and worker tasks was developed. Data was collected using smartphone accelerometers and gyroscopes from construction entities to generate significant statistical time- and frequency-domain features. The extracted features served as the input of different types of machine learning algorithms that were applied to various construction activities. The trained predictive algorithms were then used to extract activity durations and calculate probability distributions to be fused into corresponding DES models. Results indicated that the generated data-driven knowledge-based simulation models outperform static models created based upon engineering assumptions and estimations with regard to compatibility of performance measure outputs to reality.

[pt] Devido ao grande aumento da competitividade e da, cada vez maior, demanda por eficiência, muitas empresas perceberam que é necessário repensar e melhorar seus processos. Para atingir este objetivo, elas têm cada vez mais buscado técnicas computacionais que sejam capazes de extrair novas informações e conhecimentos de suas grandes bases de dados. Os processos das empresas, normalmente, possuem momentos em que uma decisão deve ser tomada. É razoável esperar que casos similares tenham decisões parecidas sendo tomadas ao longo do processo. O objetivo desta dissertação é criar um minerador de decisão que seja capaz the automatizar a tomada de decisão dentro de um processo. A primeira parte do trabalho consiste na identificação dos pontos de decisão em uma rede de Petri. Em seguida, transformamos a tomada de decisão em um problema de classificação no qual cada possibilidade da decisão se torna uma classe. Para fazer a automatização, é utilizada uma árvore de decisão treinada com os atributos dos dados que estão presentes nos logs dos eventos. Um estudo de caso real é utilizado para validar que o minerador de decisão é confiável para processos reais. / [en] Due to the increasing competitiveness and demand for higher performance, many companies realized that it is necessary to rethink and enhance their business processes. In order to achieve this goal, companies have been turning to computational techniques that are capable of extracting new information and insights from their, ever-increasing, datasets. Business processes, normally, have many places where a decision has to be made. It is reasonable to expect that similar inputs have the same decisions made to them during the process. The goal of this dissertation is to create a decision miner that automates the decision-making inside a process. First, we will identify decision points in a Petri net model. Then, we will transform the decision-making problem into a classification one, where each of the possible decisions becomes a class. In order to automate the decision-making, a decision tree is trained using data attributes from the event logs. A real world case study is used to validate that the decision miner is reliable when using real world data.

[pt] Descoberta de Processo significa determinar um modelo de processo a partir de um registro histórico de eventos de um processo de negócios. Muitos algoritmos de descoberta de processos tentam sintetizar uma rede de Petri que representa o registro localizando locais e arcos que relacionam as classes de eventos. Bergenthum et al (2007) e van der Werf et al. (2008) propõem formulações para este problema descobrir um place de cada vez, em que cada solução básica do conjunto de desigualdades representa um lugar candidato. Propomos uma formulação global de programação inteira que, dado um registro histórico, determina todos os places e arcos que definem uma rede de Petri de uma só vez. Este modelo é uma alternativa a seleção de locais, mas tem um problema de eficiência devido à grande quantidade de variáveis inteiras usadas. Também propomos um método de decomposição para o modelo ILP global para tratar cada place e suas restrições associadas como um subproblema separado. Conseguimos então executar o algoritmo em instâncias sintéticas grandes, o que é inédito para esta classe de mineradores de processo. / [en] Process Discovery amounts to determine a process model from an event log of a business process. Many process discovery algorithms try to synthesize a Petri net representing the log by finding places and arcs that relate the event classes. Bergenthum et al. (2007) and van der Werf et al. (2008) propose formulations for this problem discover one place at a time, in which each basic solution of the set of inequalities represents a candidate place. We propose a global integer programming formulation that, given a log, determines all places and arcs defining a Petri net. This model simplifies the selection of places but has an efficiency problem due to a large number of integer variables used. We also propose a decomposition method for the global ILP model to treat each place and their associated constraints as a separate sub-problem. We can run the algorithm on large synthetic instances, which is unprecedented for this kind of process miner.

Les deux dernières décennies ont été marquées par une augmentation significative des données collectées dans les systèmes d'informations. Cette masse de données contient des informations riches et peu exploitées. Cette réalité s’applique au secteur de la santé où l'informatisation est un enjeu pour l’amélioration de la qualité des soins. Les méthodes existantes dans les domaines de l'extraction de processus, de l'exploration de données et de la modélisation mathématique ne parviennent pas à gérer des données aussi hétérogènes et volumineuses que celles de la santé. Notre objectif est de développer une méthodologie complète pour transformer des données de santé brutes en modèles de simulation des parcours de soins cliniques. Nous introduisons d'abord un cadre mathématique dédié à la découverte de modèles décrivant les parcours de soin, en combinant optimisation combinatoire et Process Mining. Ensuite, nous enrichissons ce modèle par l’utilisation conjointe d’un algorithme d’alignement de séquences et de techniques classiques de Data Mining. Notre approche est capable de gérer des données bruitées et de grande taille. Enfin, nous proposons une procédure pour la conversion automatique d'un modèle descriptif des parcours de soins en un modèle de simulation dynamique. Après validation, le modèle obtenu est exécuté pour effectuer des analyses de sensibilité et évaluer de nouveaux scénarios. Un cas d’étude sur les maladies cardiovasculaires est présenté, avec l’utilisation de la base nationale des hospitalisations entre 2006 et 2015. La méthodologie présentée dans cette thèse est réutilisable dans d'autres aires thérapeutiques et sur d'autres sources de données de santé. / During the last two decades, the amount of data collected in Information Systems has drastically increased. This large amount of data is highly valuable. This reality applies to health-care where the computerization is still an ongoing process. Existing methods from the fields of process mining, data mining and mathematical modeling cannot handle large-sized and variable event logs. Our goal is to develop an extensive methodology to turn health data from event logs into simulation models of clinical pathways. We first introduce a mathematical framework to discover optimal process models. Our approach shows the benefits of combining combinatorial optimization and process mining techniques. Then, we enrich the discovered model with additional data from the log. An innovative combination of a sequence alignment algorithm and of classical data mining techniques is used to analyse path choices within long-term clinical pathways. The approach is suitable for noisy and large logs. Finally, we propose an automatic procedure to convert static models of clinical pathways into dynamic simulation models. The resulting models perform sensitivity analyses to quantify the impact of determinant factors on several key performance indicators related to care processes. They are also used to evaluate what-if scenarios. The presented methodology was proven to be highly reusable on various medical fields and on any source of event logs. Using the national French database of all the hospital events from 2006 to 2015, an extensive case study on cardiovascular diseases is presented to show the efficiency of the proposed framework.

Este proyecto tiene como objetivo analizar la complejidad de los procesos de negocio en las empresas retail de una forma profunda que en otras técnicas resulta muy difícil o incluso imposible de realizar. Con Process Mining es posible superar esta brecha y eso es lo que queremos demostrar a través de la implementación de un modelo. El proyecto propone un modelo de Process Mining que contemple la presencia de diversas fuentes de información de un proceso logístico en una empresa minorista, así como la aplicación de las tres fases de Process Mining (Descubrimiento, Conformidad y Mejora) y adicionalmente se propone una fase de diagnóstico la cual detalla un conjunto de métricas de control para evaluar el proceso de logística y así poder generar una plan de mejora que dé las pautas para optimizar el proceso en base a lo analizado mediante esta técnica. El modelo desarrollado se implementó en una empresa peruana del sector retail (TopiTop S.A) para el análisis del proceso de logística, específicamente el de gestión de órdenes de compra. Este se analizó dando como resultado de la aplicación del modelo y de la evaluación de las métricas propuestas, la identificación de anomalías en el proceso a través de la aplicación de cada una de las fases del modelo propuesto, asegurando la calidad del análisis en la fase de preprocesamiento, generando el modelo de procesos y extrayendo información que se derivó en métricas de control a través de la herramienta de código abierto ProM Tools. / This project aims to analyze the complexity of business processes in retail companies in a deep way that in other techniques is very difficult or even impossible to do. With Process Mining it is possible to overcome this gap and that is what we want to demonstrate through the implementation of a Process Mining model. The project proposes a Process Mining model that contemplates the presence of various sources of information of a logistic process in a retail company, as well as the application of the three phases of Process Mining (Discovery, Compliance and Improvement). Additionally, a diagnostic phase is proposed, which details a set of control metrics to evaluate the logistic process and thus be able to generate an improvement plan that gives the guidelines to optimize the process based on what has been analyzed through this technique. The model developed was implemented in a peruvian company in the retail sector (TopiTop S.A.) for the analysis of the logistics process, specifically the management of purchase orders. This was analyzed giving as a result of the application of the model and the evaluation of the proposed metrics, the identification of anomalies in the process through the application of each of the phases of the proposed model, ensuring the quality of the analysis in the pre-processing phase, generating the process model and extracting information that was derived in control metrics through the open source tool ProM Tools. / Tesis