ResumenFlexible Runtime Support of Business Processes under Rolling Planning HorizonsBarba, Irene; Jiménez Ramírez, Andrés; Reichert, Manfred; del Valle, Carmelo; Weber, Barbara. Actas de las XVII Jornadas de Ingeniería de Ciencia e Ingeniería de Servicios (JCIS 2022), 2022-09-05.This work has been motivated by the needs we discovered when analyzing real-world processes from the healthcare domain that have revealed high flexibility demands and complex temporal constraints. When trying to model these processes with existing languages, we learned that none of the latter was able to fully address these needs. This motivated us to design TConDec-R, a declarative process modeling language enabling the specification of complex temporal constraints. Enacting business processes based on declarative process models, however, introduces a high complexity due to the required optimization of objective functions, the handling of various temporal constraints, the concurrent execution of multiple process instances, the management of cross-instance constraints, and complex resource allocations. Consequently, advanced user support through optimized schedules is required when executing the instances of such models. In previous work, we suggested a method for generating an optimized enactment plan for a given set of process instances created from a TConDec-R model. However, this approach was not applicable to scenarios with uncertain demands in which the enactment of newly created process instances starts continuously over time, as in the considered healthcare scenarios. Here, the process instances to be planned within a specific timeframe cannot be considered in isolation from the ones planned for future timeframes. To be able to support such scenarios, this article significantly extends our previous work by generating optimized enactment plans under a rolling planning horizon. We evaluate the approach by applying it to a particularly challenging healthcare process scenario, i.e., the diagnostic procedures required for treating patients with ovarian carcinoma in a Woman Hospital. The application of the approach to this sophisticated scenario allows avoiding constraint violations and effectively managing shared resources, which contributes to reduce the length of patient stays in the hospital. ResumenModeling Variability in the Performance Perspective of Business ProcessesEstrada-Torres, Bedilia; Del Río Ortega, Adela; Resinas Arias de Reyna, Manuel; Ruiz Cortés, Antonio. Actas de las XVII Jornadas de Ingeniería de Ciencia e Ingeniería de Servicios (JCIS 2022), 2022-09-05.The definition and modeling of business processes may vary from one context to another, for example, to adapt to new business requirements, to regulations in different regions or to reflect new resource allocations. These differences often lead to the definition of several variants of the same process and can be reflected in different process perspectives such as control-flow, data, resources or performance. The management of process variants can be a laborious, time-consuming and error-prone task since they require a high coordination in the management of each variant and in most cases this management is done manually. Many proposals have been developed to deal with the variability of business processes. However, none of them covers in detail the variability in the performance perspective, which is concerned with the definition of performance requirements usually specified as a set of Process Performance Indicators (PPIs). This variability can be reflected in the form of repetitive and redundant PPI definitions, and can lead to errors and inconsistencies in PPI definitions. To address this problem, we propose a detailed PPI variability classification and a formalization on how PPIs can be modeled together with the variability of other process perspectives. To this end, we considered variability management approaches, called by restriction and by extension, and we illustrated our proposal by integrating it with two existing process variability modeling languages. An evaluation conducted in two scenarios shows the feasibility of our approach and how it can be successfully used to model the variability that is present in those scenarios. ResumenA Conformance Checking-based Approach for Sudden Drift Detection in Business ProcessesGallego Fontenla, Víctor José; Vidal Aguiar, Juan Carlos; Lama Penín, Manuel. Actas de las XVII Jornadas de Ingeniería de Ciencia e Ingeniería de Servicios (JCIS 2022), 2022-09-05.Real life business processes change over time, in both planned and unexpected ways. The detection of these changes is crucial for organizations to ensure that the expected and the real behavior are as similar as possible. These changes over time are called concept drifts and its detection is a big challenge in process mining since the inherent complexity of the data makes difficult distinguishing between a change and an anomalous execution. In this paper, we present C2D2 (Conformance Checking-based Drift Detection), a new approach to detect sudden control-flow changes in the process models from event traces. C2D2 combines discovery techniques with conformance checking methods to perform an offline detection. Our approach has been validated with a synthetic benchmarking dataset formed by 68 logs, showing an improvement in the accuracy while maintaining a very low delay in the drift detection. ResumenDeep Learning for Predictive Business Process Monitoring: Review and BenchmarkRama Maneiro, Efrén; Vidal Aguiar, Juan Carlos; Lama Penín, Manuel. Actas de las XVII Jornadas de Ingeniería de Ciencia e Ingeniería de Servicios (JCIS 2022), 2022-09-05.Predictive monitoring is a discipline that aims to predict how an ongoing business process case will unfold using the information from a business process event log. Recently, deep neural networks have gained much attention for this task due to their good results. Unfortunately, the high number of neural network architectures, the different ways of encoding the partial traces and events, the number of predictive tasks available, and, sometimes, the difficulty of quantifying the differences and contributions between the works may complicate the task of defining what has already been done, what can be researched and future research directions. Furthermore, and more importantly, approaches are applied to a reduced number of datasets with significantly different experimental setups. This makes it difficult for new researchers to compare their new approach with existent state-of-the-art works. Thus, this paper identifies the most relevant deep learning approaches for predictive monitoring. Then, using these approaches as a starting point, we select those with their implementation available and perform exhaustive experimentation of 10 different approaches and a statistical comparison in a fair setting using 12 publicly available event logs. Then, we use the results to highlight the most relevant differences between them. ResumenDiscovering Configuration Workflows From Existing Logs Using Process MiningRamos, Belén; Varela Vaca, Ángel Jesús; Galindo, José A.; Gómez-López, María Teresa; Benavides Cuevas, David Felipe. Actas de las XVII Jornadas de Ingeniería de Ciencia e Ingeniería de Servicios (JCIS 2022), 2022-09-05.Variability models are used to build configurators, for guiding users through the configuration process to reach the desired setting that fulfils user requirements. The same variability model can be used to design different configurators employing different techniques. One of the design options 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 starts by choosing the network or the graphic card and then, other packages concerning a given sequence. In this paper, we present COLOSSI, a framework that can automatically assist determining the configuration workflow that better fits the configuration logs generated by user activities given a set of logs of previous configurations and a variability model. COLOSSI is based on process discovery, commonly used in the process mining area, with an adaptation to configuration contexts. Derived from the possible complexity of both logs and the discovered processes, often, it is necessary to divide the traces into small ones. This provides an easier configuration workflow to be understood and followed by the user during the configuration process. In this paper, we apply and compare four different techniques for the traces clustering: greedy, backtracking, genetic and hierarchical algorithms. Our proposal is validated in three different scenarios, to show its feasibility, an ERP configuration, a Smart Farming, and a Computer Configuration. Furthermore, we open the door to new applications of process mining techniques in different areas of software product line engineering along with the necessity to apply clustering techniques for the trace preparation in the context of configuration workflows. ArtículoHacia un modelo de Cadena de ServiciosOjeda Pérez, Juan Sebastián; Fernández Montes, Pablo; Ruiz Cortés, Antonio. Actas de las XVII Jornadas de Ingeniería de Ciencia e Ingeniería de Servicios (JCIS 2022), 2022-09-05.Hoy en día, los servicios digitales cada vez son más complejos y numerosos, por lo que se les exige una mayor calidad en su prestación. Estos pueden ser vistos como una cadena de servicios digitales (IT Service Chains), es decir, el resultado de una cadena de organizaciones que trabajan juntos para proporcionar servicios digitales. Por un lado, la problemática de las cadenas de servicios es distinta de los desafíos de las cadenas de suministros, dada la diferencia entre servicio y producto. Las cadenas de servicios empiezan a ser un elemento de estudio novedoso que están cobrando recientemente gran interés en la comunidad investigadora, debido a la creciente transformación digital de las empresas y su movimiento paulatino a la nube. Por otro lado, se ha probado con éxito en la industria que la adopción de las buenas prácticas de ITSM mejoran la prestación de los servicios digitales. Este artículo describe un modelo formal sobre las cadenas de servicios digitales tomando como base un servicio público digital de la Administración de la Junta de Andalucía y se analizan las ventajas de este modelo. Con esta publicación se abre la puerta a nuevas líneas de investigación que solventan muchos desafíos y mejoran la prestación de los servicios digitales. ResumenExogenous Shocks and Business Process ManagementRöglinger, Maximilian; Plattfaut, Ralf; Borghoff, Vincent; Kerpedzhiev, Georgi; Becker, Jörg; Beverungen, Daniel; Vom Brocke, Jan; Van Looy, Amy. Actas de las XVII Jornadas de Ingeniería de Ciencia e Ingeniería de Servicios (JCIS 2022), 2022-09-05.Business process management (BPM) drives corporate success through effective and efficient processes. In recent decades, knowledge has been accumulated regarding the identification, discovery, analysis, design, implementation, and monitoring of business processes. This includes methods and tools for tackling various kinds of process change such as continuous process improvement, process reengineering, process innovation, and process drift. However, exogenous shocks, which lead to unintentional and radical process change, have been neglected in BPM research although they severely affect an organization’s context, strategy, and business processes. This research note conceptualizes the interplay of exogenous shocks and BPM in terms of the effects that such shocks can have on organizations’ overall process performance over time. On this foundation, related challenges and opportunities for BPM via several rounds of idea generation and consolidation within a diverse team of BPM scholars are identified. The paper discusses findings in light of extant literature from BPM and related disciplines, as well as present avenues for future (BPM) research to invigorate the academic discourse on the topic.