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.

[pt] As instituições de ensino superior mantêm uma quantidade considerável de dados que incluem tanto os registros dos alunos como a estrutura dos currículos dos cursos de graduação. Este trabalho, adotando uma abordagem de mineração de processos, centra-se no problema de identificar quão próximo os alunos seguem a ordem recomendada das disciplinas em um currículo de graduação, e até que ponto o desempenho de cada aluno é afetado pela ordem que eles realmente adotam. O problema é abordado aplicando-se duas técnicas já existentes aos registros dos alunos: descoberta de processos e verificação de conformidade; e frequência de conjuntos de itens. Finalmente, a dissertação cobre experimentos realizados aplicando-se essas técnicas a um estudo de caso com mais de 60.000 registros de alunos da PUC-Rio. Os experimentos indicam que a técnica de frequência de conjuntos de itens produz melhores resultados do que as técnicas de descoberta de processos e verificação de conformidade. E confirmam igualmente a relevância de análises baseadas na abordagem de mineração de processos para ajudar coordenadores acadêmicos na busca de melhores currículos universitários. / [en] Higher Education Institutions keep a sizable amount of data, including student records and the structure of degree curricula. This work, adopting a process mining approach, focuses on the problem of identifying how closely students follow the recommended order of the courses in a degree curriculum, and to what extent their performance is affected by the order they actually adopt. It addresses this problem by applying to student records two already existing techniques: process discovery and conformance checking, and frequent itemsets. Finally, the dissertation covers experiments performed by applying these techniques to a case study involving over 60,000 student records from PUC-Rio. The experiments show that the frequent itemsets technique performs better than the process discovery and conformance checking techniques. They equally confirm the relevance of analyses based on the process mining approach to help academic coordinators in their quest for better degree curricula.

abstract: The following is a case study composed of three workflow investigations at the open source software development (OSSD) based Apache Software Foundation (Apache). I start with an examination of the workload inequality within the Apache, particularly with regard to requirements writing. I established that the stronger a participant's experience indicators are, the more likely they are to propose a requirement that is not a defect and the more likely the requirement is eventually implemented. Requirements at Apache are divided into work tickets (tickets). In our second investigation, I reported many insights into the distribution patterns of these tickets. The participants that create the tickets often had the best track records for determining who should participate in that ticket. Tickets that were at one point volunteered for (self-assigned) had a lower incident of neglect but in some cases were also associated with severe delay. When a participant claims a ticket but postpones the work involved, these tickets exist without a solution for five to ten times as long, depending on the circumstances. I make recommendations that may reduce the incidence of tickets that are claimed but not implemented in a timely manner. After giving an in-depth explanation of how I obtained this data set through web crawlers, I describe the pattern mining platform I developed to make my data mining efforts highly scalable and repeatable. Lastly, I used process mining techniques to show that workflow patterns vary greatly within teams at Apache. I investigated a variety of process choices and how they might be influencing the outcomes of OSSD projects. I report a moderately negative association between how often a team updates the specifics of a requirement and how often requirements are completed. I also verified that the prevalence of volunteerism indicators is positively associated with work completion but what was surprising is that this correlation is stronger if I exclude the very large projects. I suggest the largest projects at Apache may benefit from some level of traditional delegation in addition to the phenomenon of volunteerism that OSSD is normally associated with. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2017

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado. / In this paper, we proposed a method that allows us to formulate and evaluate process mining indicators through questions related to the process traceability, and to bring about a clear understanding of the process variables through data visualization techniques. This proposal identifies bottlenecks and violations of policies that arise due to the difficulty of carrying out measurements and analysis for the improvement of process quality assurance and process transformation. The proposal validation was carried out in a health clinic in Lima (Peru) with data obtained from an information system that supports the surgery block process. Finally, the results contribute to the optimization of decision-making by the medical staff involved in the surgery block process. / Revisión por pares

Softwérové a hardvérové aplikácie zaznamenávajú veľké množstvo informácií do protokolov udalostí. Každé dva roky sa množstvo zaznamenaných dát viac než zdvojnásobí. Dolovanie procesov je relatívne mladá disciplína, ktorá sa nachádza na rozmedzí strojového učenia a dolovania dát na jednej strane a modelovania a analýzy procesov na druhej strane. Cieľom dolovania procesov je popísať a analyzovať skutočné procesy extrahovaním znalostí z protokolov udalostí, ktoré sú v dnešných aplikáciách bežne dostupné. Táto práca mieri na spojenie obchodných príležitostí (organizácie bohaté na dáta; dopyt po službách BPM; limitácie na strane tradičnej dodávky BPM služieb) s technickými možnosťammi Dolovania procesov. Cieľom práce je návrh produktu, ktorý bude riešiť potreby zákazníkov a poskytovateľov služieb v oblasti Dolovania procesov lepšie než súčasné riešenie vybranej spoločnosti.

In complex information systems, it is often crucial to evaluate whether a sequence of activities obtained from a system log complies with behavioural rules. This process of evaluation is called conformance checking, and the most classical approach to specifying the behavioural rules is in the form of flow chartlike process diagrams, e.g., in the Business Process Model and Notation (BPMN) language. Traditionally, control flow constraints are extracted using Petri net replay-based approaches. Though, with the use of industrial process query languages such as Signavio Analytics Language (SIGNAL) that allows for temporal row matching, the possibility of performing conformance checking using temporal constraints opens up. To this end, this thesis presents a parser for extracting control-flow objects from BPMN-based business process models and a compiler for generating both linear temporal logic-like rules as well as SIGNAL queries. The parser succeeds at parsing all industry models and most academic models; the exceptions in the latter case can presumably be traced back to edge cases and unidiomatic modelling. The constraints generated by the compiler are in some, but not in all cases, identical to constraints extracted via Petri net replay as an intermediate step, indicating some differences in the formal interpretation of BPMN control flow. In conclusion, the implementation and evaluation of the parser and compiler indicate that it is feasible to move directly from business user-oriented process models to declarative, query language-based constraints, cutting out the Petri net-replay middleman and hence facilitating elegant and more efficient process data querying.

[pt] Novos métodos de fatoração de matrizes introduzem restrições às matrizes decompostas, permitindo tipos únicos de análise. Uma modificação significativa é a fatoração de matrizes binárias para matrizes binárias. Esta técnica pode revelar subconjuntos comuns e mistura de subconjuntos, tornando-a útil em uma variedade de aplicações, como análise de cesta de mercado, modelagem de tópicos e sistemas de recomendação. Apesar das vantagens, as abordagens atuais enfrentam um trade-off entre precisão, escalabilidade e explicabilidade. Enquanto os métodos baseados em gradiente descendente são escaláveis, eles geram altos erros de reconstrução quando limitados para matrizes binárias. Por outro lado, os métodos heurísticos não são escaláveis. Para superar isso, essa tese propõe um procedimento de pós-processamento para discretizar matrizes obtidas por gradiente descendente. Esta nova abordagem recupera o erro de reconstrução após a limitação e processa com sucesso matrizes maiores dentro de um prazo razoável. Testamos esta técnica a muitas aplicações, incluindo um novo pipeline para descobrir e visualizar padrões em processos petroquímicos em batelada. / [en] Novel methods for matrix factorization introduce constraints to the decomposed matrices, allowing for unique kinds of analysis. One significant modification is the binary matrix factorization for binary matrices. This technique can reveal common subsets and mixing of subsets, making it useful in a variety of applications, such as market basket analysis, topic modeling, and recommendation systems. Despite the advantages, current approaches face a trade-off between accuracy, scalability, and explainability. While gradient descent-based methods are scalable, they yield high reconstruction errors when thresholded for binary matrices. Conversely, heuristic methods are not scalable. To overcome this, this thesis propose a post-processing procedure for discretizing matrices obtained by gradient descent. This novel approach recovers the reconstruction error post-thresholding and successfully processes larger matrices within a reasonable timeframe. We apply this technique to many applications including a novel pipeline for discovering and visualizing patterns in petrochemical batch processes.

[ES] Según la Organización Mundial de la Salud, la esperanza de vida ha aumentado en seis años en las últimas dos décadas. Esto ha llevado a un aumento de las enfermedades crónicas entre la población. Como consecuencia, los sistemas de salud se han visto obligados a buscar medidas preventivas y de mejora de los procesos de atención para garantizar su sostenibilidad. Factores clave para esta mejora son la seguridad, la eficacia, la eficiencia, la atención centrada en el paciente, la puntualidad y la equidad, los cuales buscan minimizar riesgos y brindar una atención óptima. Asimismo, los Servicios de Urgencias se enfrentan a grandes desafíos debido a la alta demanda a la que están sometidos, lo que resulta en Servicios de Urgencias saturados y errores que pueden derivar en eventos adversos. Por lo tanto, mejorar la seguridad del paciente es crucial para obtener una mejor atención en el Servicio de Urgencias. Paradigmas como el Cuidado de la Salud Basado en el Valor abogan por medir la calidad de la atención, optimizar la asignación de recursos y lograr mejores resultados a través de una mejora continua. Siendo los indicadores de rendimiento tradicionales los que han desempeñado un papel crucial en este proceso, al alinear actividades y objetivos, brindar información sobre las experiencias del paciente y su estado de salud, así como contribuir en la evaluación del rendimiento, la eficacia clínica y la mejora de la calidad. Sin embargo, estos indicadores pueden presentar limitaciones debido a su naturaleza abstracta y la propia complejidad de los datos. Por lo tanto, es posible que el uso de indicadores clave no represente en su totalidad la complejidad de estos procesos. Además, la adaptación de estos indicadores a continuos cambios puede ser un desafío, lo que dificulta la comprensión de los sistemas. Técnicas como la Inteligencia Artificial pueden ofrecer una información valiosa al procesar grandes conjuntos de datos, que son de especialmente interés en el sector de la salud. De esta forma, la Minería de Procesos, un paradigma emergente y que está ganando popularidad en varios dominios incluido salud, ofrece la oportunidad de analizar y mejorar los procesos, contribuyendo a aliviar la crisis a la que se enfrentan los sistemas de salud hoy en día. Esta tesis doctoral introduce nuevos indicadores de proceso basados en técnicas de Minería de Procesos para el proceso de urgencias como solución a cuestiones no cubiertas por las técnicas de medición tradicionales o nuevas tecnologías como la Inteligencia Artificial. Además, esta tesis presenta un método novedoso para medir la Calidad de la Atención, así como comprender el proceso de atención del ictus en los Servicios de Urgencias. Este enfoque ofrece una forma más dinámica e interactiva de analizar los procesos de atención de la salud, lo que permite un mejor entendimiento, además de medir la cadena de valor, lo que ayuda a identificar especificidades en el proceso de atención en urgencias y así descubrir el comportamiento del proceso de la enfermedad de ictus. Por último, en esta tesis se presenta una aplicación basada en Minería de Procesos para soportar este método diseñada e implementada para tal fin. / [CA] Segons l'Organització Mundial de la Salut, l'esperança de vida ha augmentat en sis anys en les últimes dues dècades. Això ha portat a un augment de les malalties cròniques entre la població. Com a conseqüència, els sistemes de salut s'han vist obligats a buscar mesures preventives i de millora dels processos d'atenció per a garantir la seua sostenibilitat. Factors clau per a aquesta millora són la seguretat, l'eficàcia, l'eficiència, l'atenció centrada en el pacient, la puntualitat i l'equitat, els quals busquen minimitzar riscos i brindar una atenció òptima. Així mateix, els Serveis d'Urgències s'enfronten a grans desafiaments a causa de l'alta demanda a la qual estan sotmesos, la qual cosa resulta en Serveis d'Urgències saturats i errors que poden derivar en esdeveniments adversos. Per tant, millorar la seguretat del pacient és crucial per a obtindre una millor atenció en el Servei d'Urgències. Paradigmes com la Cura de la Salut Basat en el Valor advoquen per mesurar la qualitat de l'atenció, optimitzar l'assignació de recursos i aconseguir millors resultats a través d'una millora contínua. Sent els indicadors de rendiment tradicionals els que han exercit un paper crucial en aquest procés, en alinear activitats i objectius, brindar informació sobre les experiències del pacient i el seu estat de salut, així com contribuir en l'avaluació del rendiment, l'eficàcia clínica i la millora de la qualitat. No obstant això, aquests indicadors poden presentar limitacions a causa de la seua naturalesa abstracta i a la pròpia complexitat de les dades. Per tant, és possible que els indicadors clau no representen íntegrament la complexitat d'aquests processos. A més, l'adaptació d'aquests indicadors a canvis continus pot ser un desafiament, la qual cosa dificulta la comprensió dels sistemes. Tècniques com la Intel·ligència Artificial poden oferir una informació valuosa en processar grans conjunts de dades, que són d'especialment interés en el sector de la salut. D'aquesta manera, la Mineria de Processos, un paradigma emergent i que està guanyant popularitat en diversos dominis inclòs salut, ofereix l'oportunitat d'analitzar i millorar els processos, contribuint a alleujar la crisi a la qual s'enfronten els sistemes de salut hui dia. Aquesta tesi doctoral introdueix nous indicadors de procés basats en tècniques de Mineria de Processos per al procés d'urgències com a solució a qüestions no cobertes per les tècniques de mesurament tradicionals o noves tecnologies com la Intel·ligència Artificial. A més, aquesta tesi presenta un mètode nou per a mesurar la Qualitat de l'Atenció, així com comprendre el procés d'atenció del ictus en els Serveis d'Urgències. Aquest enfocament ofereix una forma més dinàmica i interactiva d'analitzar els processos d'atenció de la salut, la qual cosa permet un millor enteniment, a més de mesurar la cadena de valor, la qual cosa ajuda a identificar especificitats en el procés d'atenció en urgències i així descobrir el comportament del procés de la malaltia de ictus. Finalment, en aquesta tesi es presenta una aplicació basada en Mineria de Processos per a suportar aquest mètode dissenyada i implementada per a tal fi. / [EN] According to the World Health Organization, life expectancy has increased by six years in the last two decades. This has led to an increase in chronic diseases among the population. Consequently, health systems have been forced to look for preventive measures and improvement of care processes to guarantee sustainability. Key factors for this improvement are safety, efficacy, efficiency, patient-centred care, timeliness, and equity, all of which pursue to minimize risks and provide optimal care. Likewise, Emergency Services face significant challenges due to the high demand to which they are subjected, which results in saturated Emergency Departments and errors that can lead to adverse events. Therefore, improving patient safety is crucial to obtain better care in the Emergency Department. Paradigms such as Value-Based Healthcare advocate measuring the Quality of Care, optimizing the allocation of resources, and achieving better results through continuous improvement being the traditional performance indicators, those that have played a crucial role in this process by aligning activities and objectives, providing information on the patient's experiences and their state of health, as well as contributing to the evaluation of performance, clinical efficacy and quality improvement. However, these indicators may present limitations due to their abstract nature and the complexity of the data. Therefore, the key indicators may not fully represent the complexity of these processes. Furthermore, adapting these indicators to continuous changes can be challenging, making it difficult to understand the systems. Techniques such as Artificial Intelligence can offer valuable information when processing large data sets, which are particularly interesting in the health sector. In this way, Process Mining, an emerging paradigm gaining popularity in several domains, including health, offers the opportunity to analyze and improve processes, contributing to alleviating the crisis that health systems face today. This doctoral thesis presents a new way to measure the value of the emergency process with interactive process indicators based on Process Mining techniques as a solution to issues not covered by traditional measurement techniques or new technologies such as Artificial Intelligence. In addition, this thesis proposes a novel method to measure the Quality of Care in addition to understanding the stroke care process in Emergency Services. This approach offers a more dynamic and interactive way of analyzing healthcare processes, which allows for a better understanding and measuring of the value chain, which helps identify specificities in the emergency care process and thus discover the behaviour of the stroke disease process. Finally, this thesis presents an application based on Process Mining to support this method, designed and implemented for this purpose. / Ibáñez Sánchez, G. (2023). Interactive Process Mining Techniques to Co-create Interactive Process Indicators to Evaluate and Characterize the Clinical Practice in Emergency Departments [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/202611

Mineração de processos é uma área de pesquisa relativamente recente que se situa entre mineração de dados e aprendizado de máquina, de um lado, e modelagem e análise de processos de negócio, de outro lado. Mineração de processos visa descobrir, monitorar e aprimorar processos de negócio reais por meio da extração de conhecimento a partir de logs de eventos disponíveis em sistemas de informação orientados a processos. O principal objetivo deste trabalho foi avaliar o contexto de aplicação de técnicas provenientes das áreas de inteligência computacional e de aprendizado de máquina, incluindo redes neurais artificiais. Para fins de simplificação, denominadas no restante deste texto apenas como ``redes neurais\'\'. e máquinas de vetores de suporte, no contexto de mineração de processos. Considerando que essas técnicas são, atualmente, as mais aplicadas em tarefas de mineração de dados, seria esperado que elas também estivessem sendo majoritariamente aplicadas em mineração de processos, o que não tinha sido demonstrado na literatura recente e foi confirmado por este trabalho. Buscou-se compreender o amplo cenário envolvido na área de mineração de processos, incluindo as principais caraterísticas que têm sido encontradas ao longo dos últimos dez anos em termos de: tipos de mineração de processos, tarefas de mineração de dados usadas, e técnicas usadas para resolver tais tarefas. O principal enfoque do trabalho foi identificar se as técnicas de inteligência computacional e de aprendizado de máquina realmente não estavam sendo amplamente usadas em mineração de processos, ao mesmo tempo que se buscou identificar os principais motivos para esse fenômeno. Isso foi realizado por meio de um estudo geral da área, que seguiu rigor científico e sistemático, seguido pela validação das lições aprendidas por meio de um exemplo de aplicação. Este estudo considera vários enfoques para delimitar a área: por um lado, as abordagens, técnicas, tarefas de mineração e ferramentas comumente mais usadas; e, por outro lado, veículos de publicação, universidades e pesquisadores interessados no desenvolvimento da área. Os resultados apresentam que 81% das publicações atuais seguem as abordagens tradicionais em mineração de dados. O tipo de mineração de processos com mais estudo é Descoberta 71% dos estudos primários. Os resultados deste trabalho são valiosos para profissionais e pesquisadores envolvidos no tema, e representam um grande aporte para a área / Mining process is a relatively new research area that lies between data mining and machine learning, on one hand, and business process modeling and analysis, on the other hand. Mining process aims at discovering, monitoring and improving business processes by extracting real knowledge from event logs available in process-oriented information systems. The main objective of this master\'s project was to assess the application of computational intelligence and machine learning techniques, including, for example, neural networks and support vector machines, in process mining. Since these techniques are currently widely applied in data mining tasks, it would be expected that they were also widely applied to the process mining context, which has been not evidenced in recent literature and confirmed by this work. We sought to understand the broad scenario involved in the process mining area, including the main features that have been found over the last ten years in terms of: types of process mining, data mining tasks used, and techniques applied to solving such tasks. The main focus of the study was to identify whether the computational intelligence and machine learning techniques were indeed not being widely used in process mining whereas we sought to identify the main reasons for this phenomenon. This was accomplished through a general study area, which followed scientific and systematic rigor, followed by validation of the lessons learned through an application example. This study considers various approaches to delimit the area: on the one hand, approaches, techniques, mining tasks and more commonly used tools; and, on the other hand, the publication vehicles, universities and researchers interested in the development area. The results show that 81% of current publications follow traditional approaches to data mining. The type of mining processes more study is Discovery 71% of the primary studies. These results are valuable for practitioners and researchers involved in the issue, and represent a major contribution to the area

Mineração de processos é um novo campo de pesquisa que liga mineração de dados e gestão de processos de negócio. A mineração de processos segue a premissa de que existe um processo desconhecido em um determinado contexto, e que ao analisar os traços do seu comportamento, com o auxílio da mineração de dados, é possível descobrir o modelo do processo. No entanto, processos de negócio realistas são difíceis de minerar por causa do excesso de comportamento registrado nos logs. Esses processos não estruturados, apesar de complexos, possuem um potencial grande para melhoria, sendo que as abordagens atuais de mineração de processos para esse contexto ainda provém pouco suporte à gestão. Este trabalho de pesquisa de mestrado visou aplicar técnicas computacionais evolutivas na mineração de modelos de processo, usando algoritmos genéticos para descobrir automaticamente modelos de processos não estruturados visando dar suporte à gestão organizacional de processos. Uma revisão da literatura foi realizada para auxiliar a proposição de uma nova abordagem focada na descoberta de modelos de processos não estruturados. A abordagem proposta introduz novas fórmulas de cálculo das métricas de completude e precisão baseadas nas informações de transições entre atividades, reorganizadas por meio de uma estrutura de matriz criada neste trabalho. A abordagem introduz também o uso de operadores genéticos e estratégias de fluxo evolutivo ainda não implementados na literatura relativa a algoritmos genéticos na descoberta de processos. Análises da parametrização da abordagem proposta, bem como os modelos de processos resultantes, indicam que a abordagem é eficaz na mineração de modelos de processos melhores a partir de amostras de um log não estruturado / Process mining is a new field of research that links data mining and business process management. Process mining follows the premise that there is an unknown process in a given context, and by analyzing the traces of its behavior, with the help of data mining, the process model can be discovered. However, realistic business processes are difficult to mine because of excessive behavior recorded in the logs. These unstructured processes, despite being complex, hold great potential for improvement, and the current process mining approaches for that context yet provide little support for management. This masters research project aims to apply evolutionary computational techniques in process mining, using genetic algorithms to automatically discover unstructured process models in order to support process management in organizations. A literature review was carried out to support the proposition of a new approach focused on the discovery of unstructured process models. The proposed approach introduces new formulas for calculating completeness and precision metrics, based on the information of transitions between activities that are reorganized through a matrix structure created in this work. The approach also introduces the use of genetic operators and evolutionary flow strategies not yet implemented in the literature regarding genetic algorithms in process discovery. Analyzes of the parameterization of the proposed approach, as well as the resulting process models, indicate that the approach is effective in mining better process models from samples of a unstructured log