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

Variability models are used to build configurators. Configurators are programs that guide users through the configuration process to reach a desired configuration that fulfils user requirements. The same variability model can be used to design different configurators employing different techniques. One of the elements that can change in a configurator is the configuration workflow, i.e., the order and sequence in which the different configuration elements are presented to the configuration stakeholders. When developing a configurator, a challenge is to decide the configuration workflow that better suites stakeholders according to previous configurations. For example, when configuring a Linux distribution, the configuration process start by choosing the network or the graphic card, and then other packages with respect to a given sequence. In this paper, we present COLOSSI, an automated technique that given a set of logs of previous configurations and a variability model can automatically assist to determine the configuration workflow that better fits the configuration logs generated by user activities. The technique is based on process discovery, commonly used in the process mining area, with an adaptation to configuration contexts. Our proposal is validated using existing data from an ERP configuration environment showing its feasibility. Furthermore, we open the door to new applications of process mining techniques in different areas of software product line engineering.

Several simplification techniques have been proposed to deal with the understanding of complex process models, from the structural simplification of the model to the simplification of the log to discover simpler process models. But obtaining a comprehensible model explaining the behaviour of unstructured large processes is still an open challenge. In this paper, we present a novel algorithm to simplify process models by abstracting the infrequent behaviour in the logs.

RESUMEN: Software performance engineering is a mature field that offers methods to assess system performance. Process mining is a promising research field applied to gain insight on system processes. The interplay of these two fields opens promising applications in the industry. In this work, we report our experience applying a methodology, based on process mining techniques, for the performance assessment of a commercial data-intensive software application. The methodology has successfully assessed the scalability of future versions of this system. Moreover, it has identified bottlenecks components and replication needs for fulfilling business rules. The system, an integrated port operations management system, has been developed by Prodevelop, a medium-sized software enterprise with high expertise in geospatial technologies. The performance assessment has been carried out by a team composed by practitioners and researchers. Finally, the paper offers a deep discussion on the lessons learned during the experience, that will be useful for practitioners to adopt the methodology and for researcher to find new routes.

REFERENCIA: Simona Bernardi, Juan L. Domínguez, Abel Gómez, Christophe Joubert, José Merseguer, Diego Perez-Palacin, José I. Requeno, Alberto Romeu. A systematic approach for performance assessment using process mining: An industrial experience report. Empirical Software Engineering, 23 (6), pp. 3394–3441, 2018. https://doi.org/10.1007/s10664-018-9606-9
First Online: 21 March 2018. Publicado en diciembre 2018. Volumen 23, Issue 6, pp 3394–3441 (48 páginas). Disponible online en: http://rdcu.be/Jz3J

INDICIOS DE CALIDAD – EMPIRICAL SOFTWARE ENGINEERING: Factor de Impacto: 2.933 (IF 2017); Categoría, Posición y Cuartil: Computer Science, Software Engineering; 11/104 (Q1)

Summary of the contribution

Predictive monitoring of business processes is a challenging topic of process min- ing which is concerned with the prediction of process indicators of running pro- cess instances. The main value of predictive monitoring is to provide information in order to take proactive and corrective actions to improve process performance and mitigate risks in real time. In this paper, we present an approach for pre- dictive monitoring based on the use of evolutionary algorithms. Our method provides a novel event window-based encoding and generates a set of decision rules for the run-time prediction of process indicators according to event log properties. These rules can be interpreted by users to extract further insight of the business processes while keeping a high level of accuracy. Furthermore, a full software stack consisting of a tool to support the training phase and a framework that enables the integration of run-time predictions with business process man- agement systems, has been developed. Obtained results show the validity of our proposal for two large real-life datasets: BPI Challenge 2013 and IT Department of Andalusian Health Service (SAS).

In this paper, we present WoMine, an algorithm to retrieve frequent behavioural patterns from the model. Our approach searches in process models extracting structures with sequences, selections, parallels and loops, which are frequently executed in the logs. This proposal has been validated with a set of process models, and compared with the state of the art techniques. Experiments have validated that WoMine can find all types of patterns, extracting information that cannot be mined with the state of the art techniques.

Process mining allows the extraction of useful information from event logs and historical data of business processes. This information will improve the performance of these processes and is generally obtained after they have finished. Therefore, predictive monitoring of business process running instances is needed, in order to provide proactive and corrective actions to improve the process performance and mitigate the possible risks in real time. This monitoring allows the prediction of evaluation metrics for a runtime process. In this context, this work describes a general methodology for a business process monitoring system for the prediction of process performance indicators and their stages, such as, the processing and encoding of log events, the calculation of aggregated attributes or the application of a data mining algorithm.

Since business processes supported by traditional systems are implicitly defined, correlating events into the appropriate process instance is not trivial. This challenge is known as the event correlation problem. This paper presents an adaptation of an existing event correlation algorithm and incorporates it into a technique to collect event logs from the execution of traditional information systems. The technique first instruments the source code to collect events together with some candidate correlation attributes. Secondly, the algorithm is applied to the dataset of events to discover the best correlation conditions. Event logs are then built using such conditions. The technique has been semi-automated to facilitate its validation through an industrial case study involving a writer management system and a healthcare evaluation system. The study demonstrates that the technique is able to discover the correlation set and obtain well-formed event logs enabling business process mining techniques to be applied to traditional information systems.