Resultados de búsqueda para Process Mining

La ingente cantidad de información recogida por los dispositivos móviles proporciona una visión de los distintos procesos que un usuario sigue en su día a a día. Estos procesos pueden ser analizados, con el fin de saber más acerca del usuario como individuo y como parte de distintos grupos sociales. Sin embargo, analizar eventos que están sujetos al comportamiento humano, donde el indeterminismo y la variabilidad prevalecen, no es sencillo. No existen, por lo tanto, técnicas sencillas que permitan discernir que usuarios pertenecen a un determinado grupo y cuales no, impidiendo crear Social Workflows solo con la información de aquellos usuarios que tienen algo en común. En esta demo presentamos Social Events Analyzer (SEA), un toolkit que permite analizar Social Workflows mediante Federated Process Mining. SEA proporciona modelos más fieles al comportamiento de los usuarios que conforman un Social Workflow y abre la puerta al uso de la minería de procesos como base para la creación de nuevos procedimientos automáticos adaptados al comportamiento de los usuarios.

Robotic Process Automation (RPA) has received increasingattention in recent years. It enables task automation by software componentswhich interact with user interfaces in a similar way to that ofhumans. An RPA project lifecycle is closely resembling a software projectone. However, in certain contexts (e.g., business process outsourcing), atesting environment is not always available. Thus, deploying the robotsin the production environment entails high risk. To mitigate it, an innovativeapproach to automatically generate a testing environment anda test suite for an RPA project is presented. The activities of the humanswhose processes are to be robotized are monitored and a UI logis confirmed. On one side, the test environment is generated as a fakeapplication, which mimics the real environment by leveraging the UI loginformation. The control flow of the application is governed by an invisiblecontrol layer which decides which image to show depending on theinterface actions that it receives. On the other side, the test case checkswhether the robot can reproduce the behaviour of the UI log. Promisingresults were obtained and a number of limitations were identified suchthat it may be applied in more realistic domains.

It is well-known that context impacts running instances of a process. Thus, defining and using contextual information may help to improve the predictive monitoring of business processes, which is one of the main challenges in process mining. However, identifying this contextual information is not an easy task because it might change depending on the target of the prediction. In this paper, we propose a novel methodology named CAP3 (Context-aware Process Performance indicator Prediction) which involves two phases. The first phaseguides process analysts on identifying the context for the predictive monitoring of process performance indicators (PPIs), which are quantifiable metrics focused on measuring the progress of strategic objectives aimed to improve the process. The second phase involves a context-aware predictive monitoring technique that incorporates the relevant context information as input for the prediction. Our methodology leverages context-oriented domain knowledge and experts’ feedback to discover the contextual information useful to improve the quality of PPI prediction with a decrease of error rates in most cases, by adding this information as features to the datasets used as input of the predictive monitoring process. We experimentally evaluated our approach using two-real-life organizations. Process experts from both organizations applied CAP3 methodology and identified the contextual information to be used for prediction. The model learned using this information achieved lower error rates in most cases than the model learned without contextual information confirming the benefits of CAP3.This paper was published in IEEE Access, 2020, Vol. 8, pp. 222050 – 222063, doi: 10.1109/ACCESS.2020.3044670