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Browsing by Author "Muñoz Gama, Jorge"

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A Domain-Driven Framework to Analyze Learning Dynamics in MOOCs through Event Abstraction
(MDPI, 2023) Hidalgo Sepúlveda, Luciano; Muñoz Gama, Jorge
Interest in studying Massive Online Open Courses (MOOC) learners' sessions has grown as a result of the retention and completion issues that these courses present. Applying process mining to study this phenomenon is difficult due to the freedom of navigation that these courses give their students. The goal of this research is to provide a domain-driven top-down method that enables educators who are unfamiliar with data and process analytics to search for a set of preset high-level concepts in their own MOOC data, hence simplifying the use of typical process mining techniques. This is accomplished by defining a three-stage process that generates a low-level event log from a minimum data model and then abstracts it to a high-level event log with seven possible learning dynamics that a student may perform in a session. By examining the actions of students who successfully completed a Coursera introductory programming course, the framework was tested. As a consequence, patterns in the repetition of content and assessments were described; it was discovered that students' willingness to evaluate themselves increases as they advance through the course; and four distinct session types were characterized via clustering. This study shows the potential of employing event abstraction strategies to gain relevant insights from educational data.
A Framework for Recommending Resource Allocation based on Process Mining
(2016) Arias, Michael; Rojas, Eric; Muñoz Gama, Jorge; Sepúlveda Fernández, Marcos Ernesto
Dynamically allocating the most appropriate resource to execute the different activities of a business process is an important challenge in business process management. An ineffective allocation may lead to an inadequate resources usage, higher costs, or a poor process performance. Different approaches have been used to solve this challenge: data mining techniques, probabilistic allocation, or even manual allocation. However, there is a need for methods that support resource allocation based on multi-factor criteria. We propose a framework for recommending resource allocation based on Process Mining that does the recommendation at sub-process level, instead of activity-level. We introduce a resource process cube that provides a flexible, extensible and fine-grained mechanism to abstract historical information about past process executions. Then, several metrics are computed considering different criteria to obtain a final recommendation ranking based on the BPA algorithm. The approach is applied to a help desk scenario to demonstrate its usefulness.
A Multi-criteria Approach for Team Recommendation
(2017) Arias, Michael; Muñoz Gama, Jorge; Sepúlveda Fernández, Marcos Ernesto
A multinational study on artificial intelligence adoption: Clinical implementers' perspectives
(Elsevier Ireland Ltd, 2024) Marco-Ruiz, Luis; Tejedor Hernández, Miguel Ángel; Ngo, Phuong Dinh; Makhlysheva, Alexandra; Olsen Svenning, Therese; Dyb, Kari; Chomutare, Taridzo; Fernández Llatas, Carlos; Muñoz Gama, Jorge; Tayefi, Maryam
Background: Despite substantial progress in AI research for healthcare, translating research achievements to AI systems in clinical settings is challenging and, in many cases, unsatisfactory. As a result, many AI investments have stalled at the prototype level, never reaching clinical settings. Objective: To improve the chances of future AI implementation projects succeeding, we analyzed the experiences of clinical AI system implementers to better understand the challenges and success factors in their implementations. Methods: Thirty-seven implementers of clinical AI from European and North and South American countries were interviewed. Semi-structured interviews were transcribed and analyzed qualitatively with the framework method, identifying the success factors and the reasons for challenges as well as documenting proposals from implementers to improve AI adoption in clinical settings. Results: We gathered the implementers’ requirements for facilitating AI adoption in the clinical setting. The main findings include 1) the lesser importance of AI explainability in favor of proper clinical validation studies, 2) the need to actively involve clinical practitioners, and not only clinical researchers, in the inception of AI research projects, 3) the need for better information structures and processes to manage data access and the ethical approval of AI projects, 4) the need for better support for regulatory compliance and avoidance of duplications in data management approval bodies, 5) the need to increase both clinicians’ and citizens’ literacy as respects the benefits and limitations of AI, and 6) the need for better funding schemes to support the implementation, embedding, and validation of AI in the clinical workflow, beyond pilots. Conclusion: Participants in the interviews are positive about the future of AI in clinical settings. At the same time, they propose numerous measures to transfer research advances into implementations that will benefit healthcare personnel. Transferring AI research into benefits for healthcare workers and patients requires adjustments in regulations, data access procedures, education, funding schemes, and validation of AI systems.
A Real-world Approach to Motivate Students on the First Class of a Computer Science Course
(ASSOC COMPUTING MACHINERY, 2021) Bellino, Alessio; Herskovic, Valeria; Hund, Michael; Muñoz Gama, Jorge
A common belief among students is that computing is a boring subject that lacks a connection to the real world. The first class (one 80-minute session) in an introductory computer science course may be an appropriate instance to combat such a belief. Previous studies have used coursewide interventions, e.g., games and physical/tangible devices to improve students' motivation. However, although other approaches help motivate students, they may lack real-world context or have a high cost of deployment. This article proposes a novel real-world based approach to introduce programming concepts in the first class of the introductory computer science course. This approach, called Protobject based, is applicable to courses with over 100 students, has a low deployment entry barrier, requires low investment, and may be used creatively to implement different experiences. Furthermore, the Protobject-based approach has an equivalent motivational effect-at least in the short-term-to a Game-based approach even if it is entirely focused on the real world. The low requirements of the approach make it especially suitable for an 80-minute first class in an introductory computer science course. The Protobject-based approach has been preliminarily validated and compared to a pure game-based approach with a study with 376 participants, and we present the analysis of motivation questionnaires, a pre-test and post-test, and a homework assignment given to the students. We posit that more research into initiatives such as this one-that can show students how computer science can impact the real world around them-is warranted.
Advancing decomposed conformance checking in process mining
(2020) Lee, Wai Lam Jonathan; Muñoz Gama, Jorge; Pontificia Universidad Católica de Chile. Escuela de Ingeniería
En los últimos años, la “process mining” ha ido ganando terreno como herramienta para analizar y mejorar los procesos en la industria, como lo ejemplifican empresas como Disco, Celonis y Minit. Además, muchas herramientas comerciales de process mining se están extendiendo más allá del descubrimiento de procesos a la “conformance checking” que permite comparar el comportamiento observado y modelado para encontrar diferencias entre los dos. Realizar la conformance checking en entornos industriales significa que las técnicas deben poder abordar las diferentes dimensiones de los datos. Por ejemplo, las técnicas de conformance checking tienen que escalar desde procesos de pequeñas empresas hasta organizaciones multinacionales que pueden estar manejando muchos casos por hora. Esta tesis se centra en la conformance checking y aborda especficamente los desafíos que surgen de la aplicación de la conformance checking en diferentes escenarios. Las técnicas basadas en alineación (alignment) son el estado del arte para identificar y explicar las discrepancias entre el comportamiento observado y modelado. Sin embargo, debido a la explosión del espacio de estados con procesos con construcciones paralelas, la alignment puede ser computacionalmente costosa. La primera parte de la tesis se centra en extender las técnicas de descomposición al cálculo de alignment. La tesis muestra que la alignment se puede calcular de forma descompuesta y presenta un algoritmo de conformance checking novedoso que calcula la alignment utilizando el paradigma de dividir y conquistar. En la actualidad, existen muchas técnicas de conformance checking disponibles para los usuarios. Sin embargo, puede resultar difícil seleccionar el mejor algoritmo para el trabajo, ya que depende de los datos de entrada y del objetivo del usuario. La segunda parte de la tesis investiga técnicas de machine learning para ayudar a los usuarios a seleccionar el mejor algoritmo en función de sus datos de entrada. Específicamente, aplica el machine learning al problema de clasificación de si las técnicas de descomposición pueden mejorar el tiempo de cálculo dado el modelo y el event log. La tercera parte de la tesis se centra en la conformance checking en tiempo real. Dado el volumen y la velocidad a la que llegan los datos de eventos, es posible que las organizaciones no almacenen todos los datos generados para el análisis offline y, en su lugar, tengan que recurrir a técnicas en tiempo real. Además, realizar análisis en tiempo real permite a los dueños del proceso reaccionar y resolver los problemas en forma inmediato. Realizar la conformance checking en una configuración en tiempo real tiene sus propios desafíos únicos. Por ejemplo, la técnica de conformance checking tiene que equilibrar el énfasis en la información actual y garantizar que el resultado de conformance sea algo estable a medida que se desarrolla el caso en ejecución. La tesis presenta una novedosa técnica de conformance checking en tiempo real basada en Hidden Markov Model.En los últimos años, la “process mining” ha ido ganando terreno como herramienta para analizar y mejorar los procesos en la industria, como lo ejemplifican empresas como Disco, Celonis y Minit. Además, muchas herramientas comerciales de process mining se están extendiendo más allá del descubrimiento de procesos a la “conformance checking” que permite comparar el comportamiento observado y modelado para encontrar diferencias entre los dos. Realizar la conformance checking en entornos industriales significa que las técnicas deben poder abordar las diferentes dimensiones de los datos. Por ejemplo, las técnicas de conformance checking tienen que escalar desde procesos de pequeñas empresas hasta organizaciones multinacionales que pueden estar manejando muchos casos por hora. Esta tesis se centra en la conformance checking y aborda especficamente los desafíos que surgen de la aplicación de la conformance checking en diferentes escenarios. Las técnicas basadas en alineación (alignment) son el estado del arte para identificar y explicar las discrepancias entre el comportamiento observado y modelado. Sin embargo, debido a la explosión del espacio de estados con procesos con construcciones paralelas, la alignment puede ser computacionalmente costosa. La primera parte de la tesis se centra en extender las técnicas de descomposición al cálculo de alignment. La tesis muestra que la alignment se puede calcular de forma descompuesta y presenta un algoritmo de conformance checking novedoso que calcula la alignment utilizando el paradigma de dividir y conquistar. En la actualidad, existen muchas técnicas de conformance checking disponibles para los usuarios. Sin embargo, puede resultar difícil seleccionar el mejor algoritmo para el trabajo, ya que depende de los datos de entrada y del objetivo del usuario. La segunda parte de la tesis investiga técnicas de machine learning para ayudar a los usuarios a seleccionar el mejor algoritmo en función de sus datos de entrada. Específicamente, aplica el machine learning al problema de clasificación de si las técnicas de descomposición pueden mejorar el tiempo de cálculo dado el modelo y el event log. La tercera parte de la tesis se centra en la conformance checking en tiempo real. Dado el volumen y la velocidad a la que llegan los datos de eventos, es posible que las organizaciones no almacenen todos los datos generados para el análisis offline y, en su lugar, tengan que recurrir a técnicas en tiempo real. Además, realizar análisis en tiempo real permite a los dueños del proceso reaccionar y resolver los problemas en forma inmediato. Realizar la conformance checking en una configuración en tiempo real tiene sus propios desafíos únicos. Por ejemplo, la técnica de conformance checking tiene que equilibrar el énfasis en la información actual y garantizar que el resultado de conformance sea algo estable a medida que se desarrolla el caso en ejecución. La tesis presenta una novedosa técnica de conformance checking en tiempo real basada en Hidden Markov Model.
Análisis de clusters y de trayectorias de motivación de estudiantes de un curso de programación en modalidad remota de emergencia
(2022) Jahr Andrade, Andrés Sebastián; Herskovic, Valeria; Muñoz Gama, Jorge; Pontificia Universidad Católica de Chile. Escuela de Ingeniería
La pandemia de COVID-19 trajo consigo consecuencias no solo en el ámbito de salud y económico, sino que también en educación. Las personas se vieron obligadas a cumplir estrictos protocolos sanitarios los cuales implicaron, entre otras cosas, restricciones de desplazamiento y cuarentenas. En consecuencia, sin mas opción, los centros educacionales se vieron obligados a pasar a un formato remoto, modalidad conocida como aprendizaje remoto de emergencia, o emergency remote teaching (ERT). ERT tiene consecuencias para profesores y estudiantes, las cuales podrían afectar la motivación y, por ende, el aprendizaje de estos últimos. Con la finalidad de analizar dicha situación se realizaron 3 encuestas en un curso universitario de programación, las cuales incluían preguntas del Motivated Strageties for Learning Questionnaire (MSLQ), cuestionario autoreporte que evalúa la motivación académica de los estudiantes en distintas escalas; preguntas demográficas y descriptivas, y preguntas respecto a la preocupación por el COVID-19. A partir de lo anterior, se generaron perfiles de estudiantes utilizando el método de clusterización k-means y se analizaron sus diferencias con diversas pruebas estadísticas. Adicionalmente, se generaron trayectorias de motivación considerando los 3 periodos estudiados. A raíz de ello se descubrió la existencia de 4 perfiles de estudiantes. El perfil más adaptativo tuvo una alta motivación, además de verse más preparado y menos frustrado con las condiciones del ERT. Sin embargo, tuvo los niveles mas altos de preocupación por el COVID-19. El perfil menos adaptativo, se comporto en general como un espejo de las variables del más adaptativo, siendo el cluster con mayor representación de mujeres. En lo que respecta a trayectorias, se evidenciaron diversas variaciones motivacionales a través del tiempo. Por último, se mostraron claras diferencias entre las trayectorias de los estudiantes más y menos preocupados por el COVID-19.
Analysis of Emergency Room Episodes Duration Through Process Mining
(2019) Rojas, Eric; Cifuentes Soto, Andrés Alonso; Burattin, A.; Muñoz Gama, Jorge; Sepúlveda, Marcos; Capurro, Daniel
This study presents the proposal of a performance analysis method for ER Processes through Process Mining. This method helps to determine which activities, sub-processes, interactions and characteristics of episodes explain why the process has long episode duration, besides providing decision makers with additional information that will help to decrease waiting times, reduce patient congestion and increment quality of provided care. By applying the exposed method to a case study, it was discovered that when a loop is formed between the Examination and Treatment sub-processes, the episode duration lengthens. Moreover, the relationship between case severity and the number of repetitions of the Examination-Treatment loop was also studied. As the case severity increases, the number of repetitions increases as well.
Backpack Process Model (BPPM): A Process Mining Approach for Curricular Analytics
(MDPI, 2021) Salazar Fernandez, Juan Pablo; Muñoz Gama, Jorge; Maldonado Mahauad, Jorge; Bustamante, Diego; Sepúlveda, Marcos
Curricular analytics is the area of learning analytics that looks for insights and evidence on the relationship between curricular elements and the degree of achievement of curricular outcomes. For higher education institutions, curricular analytics can be useful for identifying the strengths and weaknesses of the curricula and for justifying changes in learning pathways for students. This work presents the study of curricular trajectories as processes (i.e., sequence of events) using process mining techniques. Specifically, the Backpack Process Model (BPPM) is defined as a novel model to unveil student trajectories, not by the courses that they take, but according to the courses that they have failed and have yet to pass. The usefulness of the proposed model is validated through the analysis of the curricular trajectories of N = 4466 engineering students considering the first courses in their program. We found differences between backpack trajectories that resulted in retention or in dropout; specific courses in the backpack and a larger initial backpack sizes were associated with a higher proportion of dropout. BPPM can contribute to understanding how students handle failed courses they must retake, providing information that could contribute to designing and implementing timely interventions in higher education institutions.
Building Process-Oriented Data Science Solutions for Real-World Healthcare
(MDPI, 2022) Fernandez-Llatas, Carlos; Martin, Niels; Johnson, Owen; Sepúlveda, Marcos; Helm, Emmanuel; Muñoz Gama, Jorge
The COVID-19 pandemic has highlighted some of the opportunities, problems and barriers facing the application of Artificial Intelligence to the medical domain. It is becoming increasingly important to determine how Artificial Intelligence will help healthcare providers understand and improve the daily practice of medicine. As a part of the Artificial Intelligence research field, the Process-Oriented Data Science community has been active in the analysis of this situation and in identifying current challenges and available solutions. We have identified a need to integrate the best efforts made by the community to ensure that promised improvements to care processes can be achieved in real healthcare. In this paper, we argue that it is necessary to provide appropriate tools to support medical experts and that frequent, interactive communication between medical experts and data miners is needed to co-create solutions. Process-Oriented Data Science, and specifically concrete techniques such as Process Mining, can offer an easy to manage set of tools for developing understandable and explainable Artificial Intelligence solutions. Process Mining offers tools, methods and a data driven approach that can involve medical experts in the process of co-discovering real-world evidence in an interactive way. It is time for Process-Oriented Data scientists to collaborate more closely with healthcare professionals to provide and build useful, understandable solutions that answer practical questions in daily practice. With a shared vision, we should be better prepared to meet the complex challenges that will shape the future of healthcare.
Business process analysis in advertising: An extension to a methodology based on process mining projects
(IEEE, 2016) Osses, A. S.; Arias, M.; Da Silva, Luiz Quelves; Rojas Córdoba, Eric Eduardo; Cobo, B. F.; Muñoz Gama, Jorge; Sepúlveda Fernández, Marcos Ernesto
Nowadays organizations generate large amount of data. Only a few make a good use to optimize the performance of the business. Process mining appears as a branch of the data science that tries to understand the actual operational processes in the organizations through different algorithms, allowing the discovery of process models to give insight of the processes and understand how they can be improved. In this work different process mining techniques are applied to a company dedicated to the advertisement market, specifically the process of dealing with contract issues with customers. The Process Mining Project Methodology was followed to execute a case study. Additional to the basic methodology, elements from the others areas of studies were added to generate better results and have a better understanding of the problem. The case study includes three scenarios with three different hypotheses that were validated through our method.
CAI Asynchronous Methodology for Emergency Remote Teaching: An Experience in Introduction to Programming
(IEEE Computer Society, 2021) Muñoz Gama, Jorge; Salas-Morales J.; Herskovic V.
Case model landscapes: toward an improved representation of knowledge-intensive processes using the fCM-language
(2021) Gonzalez-Lopez, Fernanda; Pufahl, Luise; Muñoz Gama, Jorge; Herskovic Maida, Valeria Paz; Sepúlveda Fernández, Marcos Ernesto
Clearn: A Cost-conscious Student-led Online Judge for a Large Programming Course
(2024) Herskovic Maida, Valeria Paz; Muñoz Gama, Jorge; Balladares Conejeros, Fernando Ignacio; Quiroz Pastor, Nicolas Alberto; Flores, Pablo
Online judges in programming courses allow students to improve their coding abilities and instructors to analyze student work and detect challenging topics. Although several online judge platforms are available, most are limited in that they cannot support a large number of students simultaneously working on an assignment during a fixed time period, or can only do so at a significant cost, making the use of such systems in developing countries non-viable. This paper presents Clearn, a new platform that is (1) cost-conscious, as we have focused on lowering costs, (2) student-led, as we have empowered students and teaching assistants to lead its development and maintenance, and (3) highly simultaneous, as it allows over 1,000 students to work simultaneously on a timed assignment. This paper presents the platform, as well as the lessons learned during its development and deployment, and its reception by the students.
Comprensión de modelos de procesos por expertos de dominio
(2023) Fabia Valdatta, Paolo; Muñoz Gama, Jorge; Pontificia Universidad Católica de Chile. Escuela de Ingeniería
Los modelos de procesos tienen una amplia aplicabilidad en el campo de la ingeniería y desarrollo de sistemas, pero también se utilizan con propósitos académicos y como entrenamiento profesional en otros dominios, como la salud y el derecho. La comprensión humana de los modelos de procesos es fundamental, teniendo en cuenta que éstos no tienen utilidad a menos que permitan una comunicación adecuada sobre los procesos originales que representan. Este trabajo busca evaluar los efectos que tiene el conocimiento de dominio, que se describe como la comprensión del universo del discurso del mundo real implicado en el proceso, en la comprensión de modelos de procesos, un aspecto en gran medida desconocido hasta ahora. Se realizó un análisis de la eficiencia, eficacia y carga cognitiva de la comprensión de 30 participantes (médicos y abogados) en la resolución de tareas de comprensión sintáctica, semántica y de resolución de problemas de dos modelos diferentes, uno de derecho y otro de salud. Esto se realizó mediante la creación y aplicación de un cuestionario online en un experimento del tipo crossover. 0Los resultados del experimento muestran que la comprensión de modelos tiende a mejorar cuando se lee un proceso conocido. Evidencia estadística significativa respalda una disminución de la carga cognitiva en las tareas de sintaxis y resolución de problemas, y un aumento en la eficacia en las tareas de semántica. Aspectos como la disonancia cognitiva pueden generar esfuerzos adicionales en la comprensión. Líneas futuras de investigación son también sugeridas.
Conformance checking in UML artifact-centric business process models
(2019) Estañol, Montserrat; Muñoz Gama, Jorge; Carmona, Josep; Teniente, Ernest
Control-flow analysis of procedural skills competencies in medical training through process mining
(2020) Fuente Sanhueza, René Francisco de la; Fuentes Henríquez, Ricardo Sergio; Muñoz Gama, Jorge; Riquelme Pérez, Arnoldo; Altermatt, Fernando; Pedemonte Trewhela, Juan Cristóbal; Corvetto Aqueveque, Marcia Antonia; Sepúlveda Fernández, Marcos Ernesto
Curricular analytics to characterize educational trajectories in high-failure rate courses that lead to late dropout
(2021) Muñoz Gama, Jorge; Salazar-Fernandez J.P.; Sepúlveda M.; Nussbaum M.
Featured Application, In this work, Process Mining techniques are used, with a curricular analytics approach, to understand the educational trajectories of higher education students.", "Late dropout is one of the most pressing challenges currently facing higher education, and the process that each student follows to arrive at that decision usually involves several academic periods. This work presents a curricular analytics approach at the program level, to analyze how educational trajectories of undergraduate students in high-failure rate courses help to describe the process that leads to late dropout. Educational trajectories (n = 10,969) of high-failure rate courses are created using Process Mining techniques, and the results are discussed based on established theoretical frameworks. Late dropout was more frequent among students who took a stopout while having high-failure rate courses they must retake. Furthermore, students who ended in late dropout with high-failure rate courses they must retake had educational trajectories that were on average shorter and less satisfactory. On the other hand, the educational trajectories of students who ended in late dropout without high-failure rate courses they must retake were more similar to those of students who graduated late. Moreover, some differences found among ISCED fields are also described. The proposed approach can be replicated in any other university to understand the educational trajectories of late dropout students from a longitudinal perspective, generating new knowledge about the dynamic behavior of the students. This knowledge can trigger improvements to the curriculum and in the follow-up mechanisms used to increase student retention.
Delphi method to achieve clinical consensus for a bpmn representation of the central venous access placement for training purposes
(2020) de la Fuente Sanhueza, René Francisco; Fuentes Henríquez, Ricardo Sergio; Muñoz Gama, Jorge; Dagnino Sepulveda Jorge; Sepulveda Fernandez Marcos Ernesto
Proper teaching of the technical skills necessary to perform a medical procedure begins with its breakdown into its constituent steps. Currently available methodologies require substantial resources and their results may be biased. Therefore, it is difficult to generate the necessary breakdown capable of supporting a procedural curriculum. The aim of our work was to breakdown the steps required for ultrasound guided Central Venous Catheter (CVC) placement and represent this procedure graphically using the standard BPMN notation. Methods: We performed the first breakdown based on the activities defined in validated evaluation checklists, which were then graphically represented in BPMN. In order to establish clinical consensus, we used the Delphi method by conducting an online survey in which experts were asked to score the suitability of the proposed activities and eventually propose new activities. Results: Surveys were answered by 13 experts from three medical specialties and eight different institutions in two rounds. The final model included 28 activities proposed in the initial model and four new activities proposed by the experts; seven activities from the initial model were excluded. Conclusions: The proposed methodology proved to be simple and effective, generating a graphic representation to represent activities, decision points, and alternative paths. This approach is complementary to more classical representations for the development of a solid knowledge base that allows the standardization of medical procedures for teaching purposes.
Desarrollo de un artefacto de software para la aplicación de minería de procesos en procesos desconectados de sistemas de información
(2022) Leiva Sánchez, Luis Ignacio; Muñoz Gama, Jorge; Pontificia Universidad Católica de Chile. Escuela de Ingeniería
Los procesos son secuencias de acciones llevadas a cabo con un fin determinado. Dentro de las ciencias de la computación, se han desarrollado técnicas y algoritmos para extraer información de estos, por ejemplo, en la disciplina de Minería de Procesos. Sin embargo, existe un grupo de procesos en los que no se pueden utilizar, ya que no son soportados por sistemas de información, y por ende, no existe un registro computacional de los eventos ocurridos. Estos son denominados procesos desconectados. Recientemente surgió una nueva metodología que busca apoyar el entrenamiento en procesos desconectados, POME (Process-Oriented Medical Education), compuesta de cinco etapas: creación, ejecución, enseñanza, evaluación y rediseño. Cada una de estas tiene sus propios desafíos, y este trabajo se enfoca en apoyar el etiquetado manual de los eventos que suceden dentro de un proceso desconectado, el que carece de un soporte que permita realizar esta tarea de forma fácil e intuitiva. Por ello, se plantea el desarrollo de un artefacto de software basado en Minería de Procesos, que permita implementar esta metodología en procesos desconectados, al generar registros de eventos a partir de estos, utilizar videos de ejecuciones y una lista de actividades posibles a realizar, determinadas por un modelo de procesos.

Browsing by Author "Muñoz Gama, Jorge"

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