Nowadays, business processes are increasingly supported by IT services that produce massive amounts of event data during the execution of a process. These event data can be used to analyze the process using process mining techniques to discover the real process, measure conformance to a given process model, or to enhance existing models with performance information. Mapping the produced events to activities of a given process model is essential for conformance checking, annotation and understanding of process mining results. In order to accomplish this mapping with low manual effort, we developed a semi-automatic approach that maps events to activities using insights from behavioral analysis and label analysis. The approach extracts Declare constraints from both the log and the model to build matching constraints to efficiently reduce the number of possible mappings. These mappings are further reduced using techniques from natural language processing, which allow for a matching based on labels and external knowledge sources. The evaluation with synthetic and real-life data demonstrates the effectiveness of the approach and its robustness toward non-conforming execution logs.

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