Clinical Processes - The Killer Application for Constraint-Based Process Interactions?

For more than a decade, the interest in aligning information systems in a process-oriented way has been increasing. To enable operational support for business processes, the latter are usually specified in an imperative way. The resulting process models, however, tend to be too rigid to meet the flexibility demands of the actors involved. Declarative process modeling languages, in turn, provide a promising alternative in scenarios in which a high level of flexibility is demanded. In the scientific literature, declarative languages have been used for modeling rather simple processes or synthetic examples. However, to the best of our knowledge, they have not been used to model complex, real-world scenarios that comprise constraints going beyond control-flow. In this paper, we propose the use of a declarative language for modeling a sophisticated healthcare process scenario from the real world. The scenario is subject to complex temporal constraints and entails the need for coordinating the constraint-based interactions among the processes related to a patient treatment process. As demonstrated in this work, the selected real process scenario can be suitably modeled through a declarative approach.Ministerio de Economía y Competitividad TIN2016-76956-C3-2-RMinisterio de Economía y Competitividad TIN2015-71938-RED

Flexible runtime support of business processes under rolling planning horizons

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 crossinstance 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.Ministerio de Economía y Competitividad TIN2016-76956-C3-2-RMinisterio de Ciencia e Innovación PID2019-105455 GB-C3

Simplified literature review on the applicability of process mining to RPA

Business processes play an important role in any enterprise value chain and are involved in key activities such as the purchase of material, sales, and hiring of staff. Hence, mediumsized and large companies are inherently process-oriented. Managing business processes is yet, due to new regulations, technologies, and market changes, not a trivial task. In addition to that, the execution of business processes may be repetitive, tedious and time demanding. For this reason, there is a high motivation to automate such processes, which has been facilitated by the popularisation of Robotic Process Automation (RPA). RPA brings a cost-efficient solution for process automation along with a substantial challenge that is to decide what process to automate and how. Process Mining tools and techniques have been largely adopted to address challenges faced during RPA implementations. The goal of this work is to present the usage of Process Mining in RPA implementations through a simplified systematic literature review.Processos de negócio possuem um papel importante em qualquer cadeia de valores corporativa e estão envolvidos em atividades chave como compras de suprimentos, vendas e contratações de recursos humanos. Por esse motivo, empresas de médio e grande porte são inerentemente orientadas a processos. Devido à novas regulamentações, tecnologias e mudanças de mercado, a gestão de processos de negócio é ainda uma tarefa não trivial. Além disso, a execução de processos de negócio pode ser repetitiva, entendiante e demandar tempo. Por isso, existe uma alta motivação para automatizar processos de negócio, o que tem sido facilitado pela popularização da Automação de Processos Robóticos (Robotic Process Automation - RPA). RPA provê uma solução eficiente em custo para automação de processos e trás desafios no âmbito das escolhas de quais precessos automatizar e como. As ferramentas e metodologias de Mineração de Processos têm sido amplamente utilizadas para endereçar os desafios provenietes de implementações de RPA. O objetivo deste trabalho é apresentar as aplicações da Mineração de Processos em RPA, através de uma revisão sistemática simplificada da literatura

Aktueller Stand von Prozess Mining als Methode zur Unterstützung der Prozessautomatisierung

Prozess Mining ist eine Technologie, die Unternehmen bei der Verbesserung der Prozesse durch verschiedene Anwendungen wie Process Discovery, Conformance Checking oder Predictive Process Mining unterstützt. Prozessautomatisierung ist eine verbreitete Variante der Prozessverbesserung, da sie einen bedeutenden Wettbewerbsvorteil verspricht. Diese Studie untersucht anhand einer Literaturanalyse wie geeignet Prozess Mining für die Unterstützung der Prozessautomatisierung ist. Die Analyse bedient sich einer Systematisierung nach dem BPM-Lebenszyklus und der Level of Automation Taxonomie. Prozess Mining weist viel Potential für die Unterstützung der Automatisierung auf, aber es bleibt unklar, inwieweit dieses Potential in der Praxis umgesetzt werden kann. Die Stärken von Prozess Mining liegen im Diagnostischen Bereich, doch die Umsetzung wird kaum unterstützt. Die größten Hürden bildet hierbei die fehlende Limitation des Anwendungsbereichs von PM und das benötigte Expertenwissen für die Anwendung

Herramienta para evaluar la conformidad de procesos de negocio

Traballo Fin de Grao en Enxeñaría Informática. Curso 2018-2019Las técnicas de comprobación de conformidad de procesos (conformance checking) verifican si las trazas de ejecución de un procesos son consistentes con el modelo que describe su comportamiento. Sin embargo, en ocasiones los usuarios están interesados en consultar si un submodelo se verifica y/o si ciertas condiciones temporales o restricciones sobre indicadores clave de negocio han tenido lugar. Para resolver este problema se han propuesto trabajos basados en una aproximación declarativa y/o en chequeo de modelos. El principal problema de estas aproximaciones es su falta de flexibilidad a la hora de definir submodelos de procesos que contienen cualquier estructura de control y añadir a dichos modelos condiciones temporales complejas sobre actividades y condiciones sobre indicadores clave de negocio. En esta memoria se abordará este problema con una aproximación basada en técnicas de conformidad de procesos y en tecnologías de análisis masivo de datos (Big Data)

Process Mining Handbook

This is an open access book. This book comprises all the single courses given as part of the First Summer School on Process Mining, PMSS 2022, which was held in Aachen, Germany, during July 4-8, 2022. This volume contains 17 chapters organized into the following topical sections: Introduction; process discovery; conformance checking; data preprocessing; process enhancement and monitoring; assorted process mining topics; industrial perspective and applications; and closing

Discovery of Multi-perspective Declarative Process Models

Process discovery is one of the main branches of process mining that allows the user to build a process model representing the process behavior as recorded in the logs. Standard process discovery techniques produce as output a procedural process model (e.g., a Petri net). Recently, several approaches have been developed to derive declarative process models from logs and have been proven to be more suitable to analyze processes working in environments that are less stable and predictable. However, a large part of these techniques are focused on the analysis of the control flow perspective of a business process. Therefore, one of the challenges still open in this field is the development of techniques for the analysis of business processes also from other perspectives, like data, time, and resources. In this paper, we present a full-fledged approach for the discovery of multi-perspective declarative process models from event logs that allows the user to discover declarative models taking into consideration all the information an event log can provide. The approach has been implemented and experimented in real-life case studies

Correlating Activation and Target Conditions in Data-Aware Declarative Process Discovery

Automated process discovery is a branch of process mining that allows users to extract process models from event logs. Traditional automated process discovery techniques are designed to produce procedural process models as output (e.g., in the BPMN notation). However, when confronted to complex event logs, automatically discovered process models can become too complex to be practically usable. An alternative approach is to discover declarative process models, which represent the behavior of the process in terms of a set of business constraints. These approaches have been shown to produce simpler process models, especially in the context of processes with high levels of variability. However, the bulk of approaches for automated discovery of declarative process models are focused on the control-flow perspective of business processes and do not cover other perspectives, e.g., the data, time, and resource perspectives. In this paper, we present an approach for the automated discovery of multi-perspective declarative process models able to discover conditions involving arbitrary (categorical or numeric) data attributes, which relate the occurrence of pairs of events in the log. To discover such correlated conditions, we use clustering techniques in conjunction with interpretable classifiers. The approach has been implemented as a proof-of-concept prototype and tested on both synthetic and real-life logs