[pt] Business Process Management é a ciência de observar como o trabalho é realizado em determinada organização garantindo produtos consistentes e se aproveitando de oportunidades de melhoria. Atualmente, boa parte dos processos são realizados em frameworks, muitos com armazenamento de arquivos de log, no qual é disponibilizada uma grande quantidade de informação que pode ser explorada de diferentes formas e com diferentes objetivos, área denominada como Mineração de Processos. Apesar de muitos desses dados contemplarem o modo como os recursos são alocados para cada atividade, o foco maior dos trabalhos nessa área é na descoberta do processo e na verificação de conformidade do mesmo. Nesta dissertação é proposto um modelo em petri net que incorpora a alocação de recurso, de forma a poder explorar as propriedades deste tipo de modelagem, como por exemplo a definição de todos os estados possíveis. Como aplicação do modelo, realizou-se um estudo comparativo entre duas políticas, uma mais especialista, de alocação de recurso, e outra mais generalista usando simulações de Monte Carlo com distribuição de probabilidade exponencial para o início de novos casos do processo e para estimação do tempo de execução do par recurso atividade. Sendo assim, para avaliação de cada política foi usado um sistema de pontuação que considera o andamento do processo e o tempo total de execução do mesmo. / [en] Business Process Management is the science of observing how the work is performed in a given organization ensuring consistent products and seeking opportunities for improvement. Currently, most of the processes are performed in frameworks, many with log files, in which a large amount of data is available. These data can be explored in different ways and with different objectives, giving rise to the Process Mining area. Although many of these data informs how resources are allocated for each activity, the major focus of previous work is on the discovery process techniques and process compliance. In this thesis a petri net model that incorporates resource allocation is proposed exploring the properties of this type of modeling, such as the definition of all possible states. As a model validation, it is applied in a comparative study between two resource allocation policies, one considering the expertise of each resource and other with a more generalist allocation. The arrival of new cases and the resource-activity pair execution time were estimated by Monte Carlo simulations with exponential probability distribution. Thus, for the evaluation of each policy a scoring system was used considering the progress of the process and the total execution time.

Anomaly detection in computer systems operating within complex environments,such as cyber-physical systems (CPS), has become increasingly popularduring these last years due to useful insights this process can provide aboutcomputer systems’ health conditions against known reference nominal states.As performance anomalies lead degraded service delivery, and, eventually,system-wide failures, promptly detecting such anomalies may trigger timelyrecovery responses. In this thesis, Process Mining, a discipline aiming at connectingdata science with process science, is broadly explored and employedfor detecting performance anomalies in complex computer systems, proposinga methodology for connecting event data to high-level process models forvalidating functional and non-functional requirements, evaluating system performances,and detecting anomalies. The proposed methodology is appliedto the industry-relevant European Rail Traffic Management System/EuropeanTrain Control System (ERTMS/ETCS) case-study. Experimental results sampledfrom an ERTMS/ETCS system Demonstrator implementing one of thescenarios the standard prescribe have shown Process Mining allows characterizingnominal system performances and detect deviations from such nominalconditions, opening the opportunity to apply recovery routines for steeringsystem performances to acceptable levels.

Organizations try to gain competitive advantages, and to increase customer satisfaction. To ensure the quality and efficiency of their business processes, they perform business process management. An important part of process management that happens on the daily operational level is process controlling. A prerequisite of controlling is process monitoring, i.e., keeping track of the performed activities in running process instances. Only by process monitoring can business analysts detect delays and react to deviations from the expected or guaranteed performance of a process instance. To enable monitoring, process events need to be collected from the process environment. When a business process is orchestrated by a process execution engine, monitoring is available for all orchestrated process activities. Many business processes, however, do not lend themselves to automatic orchestration, e.g., because of required freedom of action. This situation is often encountered in hospitals, where most business processes are manually enacted. Hence, in practice it is often inefficient or infeasible to document and monitor every process activity. Additionally, manual process execution and documentation is prone to errors, e.g., documentation of activities can be forgotten. Thus, organizations face the challenge of process events that occur, but are not observed by the monitoring environment. These unobserved process events can serve as basis for operational process decisions, even without exact knowledge of when they happened or when they will happen. An exemplary decision is whether to invest more resources to manage timely completion of a case, anticipating that the process end event will occur too late. This thesis offers means to reason about unobserved process events in a probabilistic way. We address decisive questions of process managers (e.g., "when will the case be finished?", or "when did we perform the activity that we forgot to document?") in this thesis. As main contribution, we introduce an advanced probabilistic model to business process management that is based on a stochastic variant of Petri nets. We present a holistic approach to use the model effectively along the business process lifecycle. Therefore, we provide techniques to discover such models from historical observations, to predict the termination time of processes, and to ensure quality by missing data management. We propose mechanisms to optimize configuration for monitoring and prediction, i.e., to offer guidance in selecting important activities to monitor. An implementation is provided as a proof of concept. For evaluation, we compare the accuracy of the approach with that of state-of-the-art approaches using real process data of a hospital. Additionally, we show its more general applicability in other domains by applying the approach on process data from logistics and finance. / Unternehmen versuchen Wettbewerbsvorteile zu gewinnen und die Kundenzufriedenheit zu erhöhen. Um die Qualität und die Effizienz ihrer Prozesse zu gewährleisten, wenden Unternehmen Geschäftsprozessmanagement an. Hierbei spielt die Prozesskontrolle im täglichen Betrieb eine wichtige Rolle. Prozesskontrolle wird durch Prozessmonitoring ermöglicht, d.h. durch die Überwachung des Prozessfortschritts laufender Prozessinstanzen. So können Verzögerungen entdeckt und es kann entsprechend reagiert werden, um Prozesse wie erwartet und termingerecht beenden zu können. Um Prozessmonitoring zu ermöglichen, müssen prozessrelevante Ereignisse aus der Prozessumgebung gesammelt und ausgewertet werden. Sofern eine Prozessausführungsengine die Orchestrierung von Geschäftsprozessen übernimmt, kann jede Prozessaktivität überwacht werden. Aber viele Geschäftsprozesse eignen sich nicht für automatisierte Orchestrierung, da sie z.B. besonders viel Handlungsfreiheit erfordern. Dies ist in Krankenhäusern der Fall, in denen Geschäftsprozesse oft manuell durchgeführt werden. Daher ist es meist umständlich oder unmöglich, jeden Prozessfortschritt zu erfassen. Zudem ist händische Prozessausführung und -dokumentation fehleranfällig, so wird z.B. manchmal vergessen zu dokumentieren. Eine Herausforderung für Unternehmen ist, dass manche Prozessereignisse nicht im Prozessmonitoring erfasst werden. Solch unbeobachtete Prozessereignisse können jedoch als Entscheidungsgrundlage dienen, selbst wenn kein exaktes Wissen über den Zeitpunkt ihres Auftretens vorliegt. Zum Beispiel ist bei der Prozesskontrolle zu entscheiden, ob zusätzliche Ressourcen eingesetzt werden sollen, wenn eine Verspätung angenommen wird. Diese Arbeit stellt einen probabilistischen Ansatz für den Umgang mit unbeobachteten Prozessereignissen vor. Dabei werden entscheidende Fragen von Prozessmanagern beantwortet (z.B. "Wann werden wir den Fall beenden?", oder "Wann wurde die Aktivität ausgeführt, die nicht dokumentiert wurde?"). Der Hauptbeitrag der Arbeit ist die Einführung eines erweiterten probabilistischen Modells ins Geschäftsprozessmanagement, das auf stochastischen Petri Netzen basiert. Dabei wird ein ganzheitlicher Ansatz zur Unterstützung der einzelnen Phasen des Geschäftsprozesslebenszyklus verfolgt. Es werden Techniken zum Lernen des probabilistischen Modells, zum Vorhersagen des Zeitpunkts des Prozessendes, zum Qualitätsmanagement von Dokumentationen durch Erkennung fehlender Einträge, und zur Optimierung von Monitoringkonfigurationen bereitgestellt. Letztere dient zur Auswahl von relevanten Stellen im Prozess, die beobachtet werden sollten. Diese Techniken wurden in einer quelloffenen prototypischen Anwendung implementiert. Zur Evaluierung wird der Ansatz mit existierenden Alternativen an echten Prozessdaten eines Krankenhauses gemessen. Die generelle Anwendbarkeit in weiteren Domänen wird examplarisch an Prozessdaten aus der Logistik und dem Finanzwesen gezeigt.

Depuis le Baby-Boom d'après guerre, la France, comme d'autres pays, est confrontée à un vieillissement de la population et à des pathologies qui deviennent de plus en plus chroniques. Ces nouveaux problèmes de santé impliquent des prises en charge plus fréquentes, plus complexes et plus transversales. Cependant, plusieurs freins liés à l'évolution de la société où à l'organisation interne du système de santé, viennent entraver le développement de prises en charge adaptées pour répondre aux nouveaux besoins. Dans un contexte de réduction des dépenses, il est nécessaire d'avoir une meilleure maîtrise des processus de santé.L'objectif de nos travaux est de proposer un ensemble de méthodes pour une meilleure compréhension du parcours de santé de la personne âgée en Auvergne. Pour cela, une description des parcours des personnes âgées est nécessaire pour avoir cette vue d'ensemble aujourd'hui manquante. De plus, cela permettra d'identifier les intervenants, les interactions ou encore les contraintes impliquées dans les différents parcours. Les travaux présentés dans ce manuscrit s'intéressent à deux problématiques. La première consiste à mettre au point des méthodes pour modéliser rapidement et efficacement les parcours de santé. Ces modèles permettront d'analyser comment les différents segments d'une prise en charge s'enchaînent. La seconde problématique consiste à proposer des méthodes pour extraire des informations pertinentes à partir des données selon un point de vue métier prédéfini, propre à la personne qui souhaite analyser ce modèle. Ces informations permettront par exemple de détecter des segments de parcours fréquents, ou encore anormaux.Pour répondre à ces problématiques, les méthodes que nous proposons dans ce manuscrit sont issues du process mining et du data mining. Ces disciplines ont pour objectif d'exploiter les données disponibles dans les systèmes d'information pour en extraire des connaissances pertinentes. Dans un premier temps, nous proposons une méthodologie qui se base sur le pouvoir d'expression des modèles de processus pour extraire des connaissances intéressantes. Dans un second temps, nous proposons des techniques pour construire des modèles de processus partiels, dont l'objectif est de permettre l'extraction de fragments de comportements intéressants.Les expérimentations effectuées démontrent l'efficacité des méthodes proposées. De plus, différentes études de cas ont été menées dans divers domaines pour prouver la généricité des techniques développées. / Ever since the post-World War II baby-boom, France has had to cope with an aging population and with pathologies that have become chronic. These new health problems imply more reccurent, more complex and multidisciplinary medical care. However, multiple obstacles related to the evolution of the society or to the internal organization of the healthcare system hinder the development of new and more adapted health procedures to meet new medical needs. In a context where health expanses have to be reduced, it is necessary to have a better management of health processes.Our work aims at proposing a set of methods to gain an understanding of the elderly health path in the Auvergne region. To this end, a description of the elderly health path is necessary in order to have this missing overview. Moreover, this will enable the identification of the stakeholders, their interactions and the constraints to which they are submitted in the different health procedures.The work presented in this thesis focuses on two problems. The first one consists in developing techniques to efficiently model health paths. With these models, we will be able to analyze how the different segments of a medical care are ordered. The second problem consists in developing techniques to extract relevant information from the data, according to a predefined business point of view, specific to the user who analyzes the model. This knowledge will enable the detection of frequent or abnormal parts of a health path.To resolve these problems, the methods we propose in this thesis are related to process mining and data mining. These disciplines aim at exploiting data available in today's information systems in order to discover useful knowledge. In a first part, we propose a methodology that relies on the expressive power of process models to extract relevant information. In a second part, we propose techniques to build local process models that represent interesting fragments of behavior.The experiments we performed show the efficiency of the methods we propose. Moreover, we analyze data from different application domains to prove the genericity of the developed techniques.

[ES] Los modelos de riesgo en el ámbito de la salud son métodos estadísticos que brindan advertencias tempranas sobre el riesgo de una persona de sufrir un episodio adverso en el futuro. Por lo general, utilizan la información almacenada de forma rutinaria en los sistemas de información hospitalaria para ofrecer una probabilidad individual de desarrollar un resultado negativo futuro en un período determinado. Concretamente, en el campo de las enfermedades crónicas que comparten factores de riesgo comunes, los modelos de riesgo se basan en el análisis de esos factores de riesgo -tensión arterial elevada, glucemia elevada, lípidos sanguíneos anormales, sobrepeso y obesidad- y sus medidas biométricas asociadas. Estas medidas se recopilan durante la práctica clínica de manera periódica y, se incorporan a los modelos de riesgo para apoyar a los médicos en la toma de decisiones. Para crear modelos de riesgo que incluyan la variable temporal, se podrían utilizar técnicas basadas en datos (Data-Driven), de forma que se tuviera en cuenta el historial de los pacientes almacenado en los registros médicos electrónicos, extrayendo conocimiento de los datos en bruto. Sin embargo, en el ámbito de la salud, los resultados de la minería de datos suelen ser percibidos por los expertos en salud como cajas negras y, en consecuencia, no confían en sus decisiones. El paradigma Interactivo permite a los expertos comprender los resultados, para que los profesionales puedan corregir esos modelos de acuerdo con su conocimiento y experiencia, proporcionando modelos perceptivos y cognitivos. En este contexto, la minería de procesos es una técnica de minería de datos que permite la implementación del paradigma Interactivo, ofreciendo una comprensión clara del proceso de atención y proporcionando modelos comprensibles para el ser humano. Las condiciones crónicas generalmente se describen mediante imágenes estáticas de variables, como factores genéticos, fisiológicos, ambientales y de comportamiento. Sin embargo, la perspectiva dinámica, temporal y de comportamiento no se consideran comúnmente en los modelos de riesgo. Eso significa que el último estado de riesgo se convierte en el estado real del paciente. No obstante, la condición de los pacientes podría verse influenciada por sus condiciones dinámicas pasadas. El objetivo de esta tesis es proporcionar una visión novedosa del riesgo asociado a un paciente, basada en tecnologías Data-Driven que ofrezcan una visión dinámica de su evolución con respecto a su condición crónica. Técnicamente, supone abordar los modelos de riesgo incorporando la perspectiva dinámica y comportamental de los pacientes gracias a la información incluida en la Historia Clínica Electrónica. Los resultados obtenidos a lo largo de esta tesis muestran cómo las tecnologías de minería de procesos pueden aportar una visión dinámica e interactiva de los modelos de riesgo de enfermedades crónicas. Estos resultados pueden ayudar a los profesionales de la salud en la práctica diaria para una mejor comprensión del estado de salud de los pacientes y una mejor clasificación de su estado de riesgo. / [CA] Els models de risc en l'àmbit de la salut són mètodes estadístics que brinden advertències primerenques sobre el risc d'una persona de patir un episodi advers en el futur. Generalment, utilitzen la informació emmagatzemada de forma rutinària en els sistemes d'informació hospitalària per a oferir una probabilitat individual de desenrotllar un resultat negatiu futur en un període determinat. Concretament, en el camp de les malalties cròniques que compartixen factors de risc comú, els models de risc es basen en l'anàlisi d'eixos factors de risc -tensió arterial elevada, glucèmia elevada, lípids sanguinis anormals, sobrecàrrega i obesitat- i les seues mesures biomètriques associades. Estes mesures es recopilen durant la pràctica clínica ben sovint de manera periòdica i, en conseqüència, s'incorporen als models de risc i recolzen la presa de decisions dels metges. Per a crear estos models de risc que incloguen la variable temporal es podrien utilitzar tècniques basades en dades (Data-Driven) , de manera que es tinguera en compte l'historial dels pacients disponible en els registres mèdics electrònics, extraient coneixement de les dades en brut. No obstant això, en l'àmbit de la salut, els resultats de la mineria de dades solen ser percebuts pels experts en salut com a caixes negres i, en conseqüència, no confien en les decisions dels algoritmes. El paradigma Interactiu permet als experts comprendre els resultats, perquè els professionals puguen corregir eixos models d'acord amb el seu coneixement i experiència, proporcionant models perceptius i cognitius. En este context, la mineria de processos és una tècnica de mineria de dades que permet la implementació del paradigma Interactiu, oferint una comprensió clara del procés d'atenció i proporcionant models comprensibles per al ser humà. Les condicions cròniques generalment es descriuen per mitjà d'imatges estàtiques de variables, com a factors genètics, fisiològics, ambientals i de comportament. No obstant això, la perspectiva dinàmica, temporal i de comportament no es consideren comunament en els models de risc. Això significa que l'últim estat de risc es convertix en l'estat real del pacient. No obstant això, la condició dels pacients podria veure's influenciada per les seues condicions dinàmiques passades. L'objectiu d'esta tesi és proporcionar una visió nova del risc, associat a un pacient, basada en tecnologies Data-Driven que oferisquen una visió dinàmica de l'evo\-lució dels pacients respecte a la seua condició crònica. Tècnicament, suposa abordar els models de risc incorporant la perspectiva dinàmica i el comportament dels pacients als models de risc gràcies a la informació inclosa en la Història Clínica Electrònica. Els resultats obtinguts al llarg d'esta tesi mostren com les tecnologies de mineria de processos poden aportar una visió dinàmica i interactiva dels models de risc de malalties cròniques. Estos resultats poden ajudar els professionals de la salut en la pràctica diària per a una millor comprensió de l'estat de salut dels pacients i una millor classificació del seu estat de risc. / [EN] Risk models in the healthcare domain are statistical methods that provide early warnings about a person's risk for an adverse episode in the future. They usually use the information routinely stored in Hospital Information Systems to offer an individual probability for developing a future negative outcome in a given period. Concretely, in the field of chronic diseases that share common risk factors, risk models are based on the analysis of those risk factors -raised blood pressure, raised glucose levels, abnormal blood lipids, and overweight and obesity- and their associated biometric measures. These measures are collected during clinical practice frequently in a periodic manner, and accordingly, they are incorporated into the risk models to support clinicians' decision-making. Data-Driven techniques could be used to create these temporal-aware risk models, considering the patients' history included in Electronic Health Records, and extracting knowledge from raw data. However, in the healthcare domain, Data Mining results are usually perceived by the health experts as black-boxes, and in consequence, they do not trust in the algorithms' decisions. The Interactive paradigm allows experts to understand the results, in that sense, professionals can correct those models according to their knowledge and experience, providing perceptual and cognitive models. In this context, Process Mining is a Data Mining technique that enables the implementation of the Interactive paradigm, offering a clear care process understanding and providing human-understandable models. Chronic conditions are usually described by static pictures of variables, such as genetic, physiological, environmental, and behavioural factors. Nevertheless, the dynamic, temporal, and behavioural perspectives are not commonly considered in the risk models. That means the last status of the risk becomes the actual status of the patient. However, the patients' condition could be influenced by their past dynamic circumstances. The objective of this thesis is to provide a novel risk vision based on Data-Driven technologies offering a dynamic view of the patients' evolution regarding their chro\-nic condition. Technically, it supposes to approach risk models incorporating the dynamic and behavioural perspective of patients to the risk models thanks to the information included in the Electronic Health Records. The results obtained throughout this thesis show how Process Mining technologies can bring a dynamic and interactive view of chronic disease risk models. These results can support health professionals in daily practice for a better understanding of the patients' health condition and a better classification of their risk status. / Valero Ramón, Z. (2022). Dynamic Risk Models for Characterising Chronic Diseases' Behaviour Using Process Mining Techniques [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/181652

L'inférence d’un système, par la reconstruction de la structure à partir de l’analyse de son comportement, est reconnue comme un problème critique. Dans la théorie des systèmes, la structure et le comportement se situent aux extrémités de la hiérarchie qui définit la connaissance du système. L'inférence d’un système peut être également considérée comme l’escalade de la hiérarchie depuis la connaissance de bas niveau vers la connaissance de plus haut niveau. Ceci n'est possible que sous des conditions maitrisées et justifiées. Dans cette thèse, une nouvelle méthode d'inférence de système est proposée. La méthode proposée étend la technique Process Mining pour extraire des connaissances depuis les données des événements du système. Les aspects de modularité, de fréquence et de synchronisation peuvent être extraits des données. Ils sont intégrés ensemble pour construire un modèle Fuzzy-Discrete Event System Specification (Fuzzy-DEVS). La méthode proposée, également appelée méthode D2FD (Data to Fuzzy-DEVS), comprend trois étapes: (1) l’extraction depuis des journaux d’évènements (registres) obtenus à partir des données générées par le système en utilisant une approche conceptuelle; (2) la découverte d'un système de transition, en utilisant des techniques de découverte de processus; (3) l'intégration de méthodes Fuzzy pour générer automatiquement un modèle Fuzzy-DEVS à partir du système de transition. La dernière étape est de l’implémenter cette contribution en tant que plugin dans l'environnement Process Mining Framework (ProM). Afin de valider les modèles construits, une approximation de modèle basée sur le morphisme et une méthode prédictive intégrée à Granger Causality sont proposées. Deux études de cas sont présentées dans lesquelles le modèle Fuzzy-DEVS est déduit à partir de données réelles, où l'outil SimStudio est utilisé pour sa simulation. Les modèles ainsi construits et les résultats de simulation sont validés par comparaison à d'autres modèles. / System inference, i.e., the building of system structure from system behavior, is widely recognized as a critical challenging issue. In System Theory, structure and behavior are at the extreme sides of the hierarchy that defines knowledge about the system. System inference is known as climbing the hierarchy from less to more knowledge. In addition, it is possible only under justifying conditions. In this thesis, a new system inference method is proposed. The proposed method extends the process mining technique to extract knowledge from event data and to represent complex systems. The modularity, frequency and timing aspects can be extracted from the data. They are integrated together to construct the Fuzzy Discrete Event System Specification (Fuzzy-DEVS) model. The proposed method is also called D2FD (Data to Fuzzy-DEVS) method, and consists of three stages: (1) extraction of event logs from event data by using the conceptual structure; (2) discovery of a transition system, using process discovery techniques; (3) integration of fuzzy methods to automatically generate a Fuzzy-DEVS model from the transition system. The last stage is implemented as a plugin in the Process Mining Framework (ProM) environment. In order to validate constructed models, morphism-based model approximation and predictive method integrated with Granger Causality are proposed. Two case studies are presented in which Fuzzy-DEVS model is inferred from real life data, and the SimStudio tool is used for its simulation. The constructed models and simulation results are validated by comparing to other models.

Les organisations doivent relever le défi d'adapter leurs processus aux changements qui peuvent survenir dans l'environnement dynamique dans lequel elles opèrent. Les adaptations dans le processus aboutissent à plusieurs variantes de processus, c'est-à-dire dans différentes versions du modèle de processus. Les variantes de processus peuvent différer en termes d'activités, de ressources, de flux de contrôle et de données. Ainsi, le concept d'un modèle de processus personnalisable est apparu et il vise à adapter le modèle de processus en fonction des exigences d'un contexte spécifique. Un modèle de processus personnalisable peut représenter toutes les variantes de processus dans un modèle unique dans lequel les parties communes ne sont représentées qu’une seule fois et les spécificités de chaque variante sont préservées. Alors, grâce à des transformations dans le modèle de processus générique, une variante de processus peut en être dérivée. En tant qu'avantages, cette approche permet d'éliminer les redondances, favorise la réutilisation, entre autres. Cependant, la personnalisation des modèles de processus n'est pas une tâche triviale. La personnalisation doit assurer que la variante obtenue est correcte du point de vue structurel et comportemental, c'est-à-dire la variante obtenue ne doit pas présenter d'activités déconnectées, d’interblocages actifs ou d'interblocages, entre autres. En outre, la variante de processus doit satisfaire à toutes les exigences du contexte de l'application, aux réglementations internes et externes, entre autres. De plus, il est nécessaire de fournir à l'utilisateur des directives et des recommandations lors de la personnalisation du processus. Les directives permettent la personnalisation correcte des variantes de processus, en évitant les problèmes de comportement. Les recommandations concernant le contexte de l'entreprise rendent possible l'amélioration du processus et aussi la personnalisation des variantes en fonction des besoins spécifiques. Dans ce contexte, cette recherche propose un cadre pour la personnalisation des variantes de processus en fonction des besoins de l'utilisateur. La personnalisation est réalisée grâce à l'utilisation d'ontologies pour la sélection des variantes. Le cadre est composé de trois étapes. La première correspond à l'identification des variantes à partir d'un journal d'événements au moyen de techniques d'exploration de processus, qui permettent de découvrir des points de variation, c'est-à-dire les parties du processus sujettes à variation, les alternatives disponibles pour chaque point de variation et les règles de sélection des alternatives disponibles. L'identification des variantes de processus et de leurs caractéristiques à partir d'un journal des événements permet de personnaliser un modèle de processus en fonction du contexte de l'application. À partir de ces aspects, la deuxième étape peut être développée. Cette étape concerne le développement d'un questionnaire, dans lequel chaque question est liée à un point de variation et chaque réponse correspond à la sélection d'une variante. Dans la troisième étape, deux ontologies sont proposées. La première formalise les connaissances liées aux réglementations externes et internes et aux connaissances des spécialistes. La deuxième ontologie se réfère aux points de variation, aux alternatives existantes pour chaque point de variation et aux règles liées à la sélection de chaque alternative. Ensuite, ces ontologies sont intégrées dans une nouvelle ontologie, qui contient les connaissances nécessaires pour personnaliser la variante de processus. Ainsi, à travers le questionnaire et le raisonnement sémantique, la variante est sélectionnée et les recommandations concernant le processus d’affaires sont fournies en fonction de la sélection de l'utilisateur lors de la personnalisation du processus. Le cadre proposé est évalué au moyen d'une étude de cas liée au traitement des patients chez qui [...] / The efficiency of organizations relies on its ability to adapt their business processes according to changes that may occur in the dynamic environment in which they operate. These adaptations result in new versions of the process model, known as process variants. Thus, several process variants can exist, which aim to represent all the related contexts that may differ in activities, resources, control flow, and data. Thus, has emerged the concept of customizable process model. It aims to adapt the process model according to changes in the business context. A process model can be customized by representing the process family in one single model enabling to derive a process variant through transformations in this single model. As benefits, this approach enables to avoid redundancies, promotes the model reuse and comparison, among others. However, the process variant customization is not a trivial-task. It must be ensured that the variant is correct in a structural and behavioural way (e.g. avoiding disconnected activities or deadlocks), and respecting all the requirements of the application context. Besides, the resulting process variant must respect all requirements related to the application context, internal and external regulations, among others. In addition, recommendations and guidance should be provided during the process customization. Guidance help the user to customize correct process variants, i.e., without behavioural problems. Recommendations about the process context help the user in customizing process variants according specific requirements. Recommendations about the business context refers to providing information about the best practices that can improve the quality of the process. In this context, this research aims to propose a framework for customizing process variants according to the user’s requirements. The customization is achieved by reasoning on ontologies based on the rules for selecting a process variant and in the internal/external regulations and expert knowledge. The framework is composed by three steps. The first step proposes to identify the process variants from an event log through process mining techniques, which enable to discover the variation points, i.e., the parts of the model that are subject to variation, the alternatives for the variation points and the rules to select the alternatives. By identifying the process variants and their characteristics from an event log, the process model can be correctly individualized by meeting the requirements of the context of application. Based on these aspects, the second step can be developed. This step refers to the development of the questionnaire-model approach. In the questionnaire approach each variation point is related to a question, and the alternatives for each question corresponds to the selection of the process variants. The third step corresponds to apply two ontologies for process model customization. One ontology formalizes the knowledge related with the internal and/or external regulations and expert knowledge. The other refers to the variation points, the alternatives for them and the rules for choosing each path. The ontologies then are merged into one new ontology, which contain the necessary knowledge for customize the process variants. Thus, by answering the questionnaire and by reasoning on the ontology, the alternatives related with the business process and the recommendations about the business context are provided for the user. The framework is evaluated through a case study related to the treatment of patients diagnosed with acute ischemic stroke. As result, the proposed framework provides a support decision-making during the process model customization

Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2018-07-10T13:52:54Z No. of bitstreams: 1 Ricardo Gerhardt_.pdf: 1506176 bytes, checksum: 17a8809b35aefa1b7cc92caf3c5be35d (MD5) / Made available in DSpace on 2018-07-10T13:52:54Z (GMT). No. of bitstreams: 1 Ricardo Gerhardt_.pdf: 1506176 bytes, checksum: 17a8809b35aefa1b7cc92caf3c5be35d (MD5) Previous issue date: 2018-03-28 / Nenhuma / O impacto do processo de reembolso das despesas médico-hospitalares das prestadoras de serviço de saúde tem sido enorme. Com o surgimento de novos procedimentos clínicos, mudanças em regulamentações e políticas há uma elevação da complexidade do processo de reembolso e consequentemente a sua duração e seus custos. Desse modo, métodos de análise de processos têm sido empregados como estratégia básica para melhorar a eficácia organizacional de instituições hospitalares. Perante a isso, o presente trabalho investiga fatores que levam ao atraso da submissão das contas hospitalares às respectivas seguradoras de saúde no sentido de reduzir seu tempo de faturamento. A abordagem proposta constitui-se em combinar técnicas da Mineração de Processos e Mineração de Dados com o intuito de identificar fatores que contribuem para o atraso do processo de reembolso. A Mineração de Processos permite vislumbrar detalhadamente o impacto causado pela realização de atividades durante a execução de processos, bem como a ocorrência de gargalos que podem indicar a necessidade de uma investigação mais apurada para detectar as suas prováveis causas. Nesse ponto, a Mineração de Dados pode ser empregada através de técnicas, como as regras associativas que possibilitam identificar relacionamentos não tão evidentes. Desta forma, este estudo investigativo demonstra sobre um caso real os benefícios do emprego da Mineração de Processos e da Mineração de Dados objetivando fornecer suporte as atividades de auditoria e de faturamento do processo de reembolso. A avaliação subjetiva das regras associativas mostrou que quase 45% das regras associativas geradas foram consideradas relevantes ou muito relevantes para a identificação de fatores que contribuem para o atraso no processo de reembolso de contas hospitalares. / The healthcare reimbursement process impact has been enormous for the healthcare providers and the economy. The arising of new clinical procedures, changes in regulations and policies have been increasing the complexity of the reimbursement process and consequently its duration and costs. Therefore, methods of process analysis have been used as a basic strategy to improve the organizational effectiveness of healthcare institutions. In this context, the present study investigates factors that cause delays in the reimbursement process. The proposed approach aims to combine Process Mining and Data Mining techniques to identify factors that can explain the reimbursement process delay. Process Mining techniques allow exploring in detail how activities can impact the process execution, as well as the occurrence of bottlenecks that may indicate the need for a systematic investigation to detect its root causes. Considering this, Data Mining can be employed through techniques, such as associative rules that can be used to identify unknown relationships. Hence, this study demonstrates through a real case the benefits that the combination of Process Mining and Data Mining techniques to support the audit and billing activities of the reimbursement process. A subjective evaluation of the mined rules showed that almost 45% of them were considered relevant or very relevant for the identification of factors that can lead to delay in the reimbursement process.

The managing of complex business processes, which are changed due to globalization, calls for the development of powerful information systems that offer generic process modeling and process execution capabilities. Even though contemporary information systems are more and more utilized in enterprises, their actual impact in automatizing complex business process is still limited by the difficulties encountered in design phase. Actually this design phase is time consuming, often subjective and incomplete. In the scope of this study, a reverse approach is followed. Instead of starting with process design, the method of discovering interesting patterns from the navigation traces is taken as basis and a new data analysis methodology named &ldquo / From-to Chart Based Process Discovery&rdquo / is proposed. In this hybrid methodology &ldquo / from-to chart&rdquo / , which is fundamentally dedicated to material handling issues on production floor, is used as the front-end to monitor the transitions among activities of a realistic event log and convert these raw relations into optimum activity sequence. Then a revised version of process mining, which is the back-end of this methodology, upgrades optimum activity sequence into process model.

The study of the software development process is a relatively new research area but it is growing rapidly. This development process, also called 'the software life cycle' or 'the software process', is the methodology used throughout the industry for the planning, design, implementation, testing and maintenance that takes place during the creation of a software product. Over the years a variety of different process models have been developed. From the numerous process models now available, project managers need validation of the choice he/she has made for a software development model that he/she believes will provide the best results. Yet the quality software so sought after by software project managers can be enhanced by improving the development process through which it is delivered. Well tested, reliable evidence is needed to assist these project managers in choosing and planning a superior software process as well as for improving the adopted software process. While some guidelines for software process validation and improvement have been provided, such as CMMI, quantitative evidence is, in fact, scarce. The quantitative evidence sometimes may not be able to be obtained from high level processes that refer to a planned process model, such as a waterfall model. Furthermore, there has been little analysis of low level processes. These low level processes refer to the actions of how a development team follow a high level software process model to develop a software product. We describe these low level processes as project enactment. Normally there is a gap between the high level software process and the project enactment. In order to improve this software development process, this gap needs to be identified, measured and analyzed. In this dissertation, we propose an approach that examines the deviation between a planned process model and the project enactment of that plan. We measure the discrepancy from two aspects: consistency and inconsistency. The analytical results of the proposed approach, which include both qualitative and quantitative data, provide powerful and precise evidence for tailoring, planning and selecting any software process model. The entire approach is composed of four major phases: 1) re-presentation of the planned process model, 2) pre-processing the low level process data, 3) process mining, and 4) analysis and comparison of the recovered process model and planned process model. We evaluate the proposed approach in three case studies: a small, a medium, and a large-sized project obtained from an industrial software development organization. The appropriate data on low level processes is collected and our approach is then applied to these projects individually. From each case study we then performed a detailed analysis of the inconsistencies that had surfaced as well as the consistencies between the plan and the enactment models. An analysis of the inconsistencies revealed that several 'agile' practices were introduced during the project's development even though the planned process model was initially based on 'ISO-12207' instead of the 'agile' method. In addition, our analysis identifies the patterns in the process that are frequently repeated. The outcome of the case studies shows that our approach is applicable to a range of software projects. The conclusions derived from these case studies confirmed that our approach could be used to enhance the entire software development process, including tailoring and assessment.