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.