Customizing BPMN Diagrams Using Timelines

BPMN (Business Process Model and Notation) is widely used standard modeling technique for representing Business Processes by using diagrams, but lacks in some aspects. Representing execution-dependent and time-dependent decisions in BPMN Diagrams may be a daunting challenge [Carlo Combi et al., 2017]. In many cases such constraints are omitted in order to preserve the simplicity and the readability of the process model. However, for purposes such as compliance checking, process mining, and verification, formalizing such constraints could be very useful. In this paper, we propose a novel approach for annotating BPMN Diagrams with Temporal Synchronization Rules borrowed from the timeline-based planning field. We discuss the expressivity of the proposed approach and show that it is able to capture a lot of complex temporally-related constraints without affecting the structure of BPMN diagrams. Finally, we provide a mapping from annotated BPMN diagrams to timeline-based planning problems that allows one to take advantage of the last twenty years of theoretical and practical developments in the field

Tackling Dierent Business Process Perspectives

Business Process Management (BPM) has emerged as a discipline to design, control, analyze, and optimize business operations. Conceptual models lie at the core of BPM. In particular, business process models have been taken up by organizations as a means to describe the main activities that are performed to achieve a specific business goal. Process models generally cover different perspectives that underlie separate yet interrelated representations for analyzing and presenting process information. Being primarily driven by process improvement objectives, traditional business process modeling languages focus on capturing the control flow perspective of business processes, that is, the temporal and logical coordination of activities. Such approaches are usually characterized as \u201cactivity-centric\u201d. Nowadays, activity-centric process modeling languages, such as the Business Process Model and Notation (BPMN) standard, are still the most used in practice and benefit from industrial tool support. Nevertheless, evidence shows that such process modeling languages still lack of support for modeling non-control-flow perspectives, such as the temporal, informational, and decision perspectives, among others. This thesis centres on the BPMN standard and addresses the modeling the temporal, informational, and decision perspectives of process models, with particular attention to processes enacted in healthcare domains. Despite being partially interrelated, the main contributions of this thesis may be partitioned according to the modeling perspective they concern. The temporal perspective deals with the specification, management, and formal verification of temporal constraints. In this thesis, we address the specification and run-time management of temporal constraints in BPMN, by taking advantage of process modularity and of event handling mechanisms included in the standard. Then, we propose three different mappings from BPMN to formal models, to validate the behavior of the proposed process models and to check whether they are dynamically controllable. The informational perspective represents the information entities consumed, produced or manipulated by a process. This thesis focuses on the conceptual connection between processes and data, borrowing concepts from the database domain to enable the representation of which part of a database schema is accessed by a certain process activity. This novel conceptual view is then employed to detect potential data inconsistencies arising when the same data are accessed erroneously by different process activities. The decision perspective encompasses the modeling of the decision-making related to a process, considering where decisions are made in the process and how decision outcomes affect process execution. In this thesis, we investigate the use of the Decision Model and Notation (DMN) standard in conjunction with BPMN starting from a pattern-based approach to ease the derivation of DMN decision models from the data represented in BPMN processes. Besides, we propose a methodology that focuses on the integrated use of BPMN and DMN for modeling decision-intensive care pathways in a real-world application domain

Improvement of the portuguese breast cancer screening through process modelling (BPM)

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceBreast cancer is a malignant epithelial neoplasm with high incidence and mortality in women. Focusing the clinical performance on screening processes has proven to be the way to improve morbidity and mortality statistics of this recognized public health problem. Business process management (BPM) is a management field that improves and analyzes business processes according to organizations’ strategies. BPM may help manage patient and information flow, improving waiting time in healthcare delivery while integrating healthcare processes with IT. The early diagnosis of breast cancer is of great importance since it will enable more conservative treatments and a longer disease-free survival. Organized oncology screenings programs, with all elements properly prepared, revealed to be more efficient than the opportunistic screenings. The aim of this study is to identify and model BPM processes for the healthcare sector, namely, for the breast cancer screening in Portugal. To achieve this goal, the main processes were identified and new frameworks were proposed and validated through individual interviews with experts. In this study was concluded that BPM techniques can be applied to the healthcare. Through the application of these techniques it was possible to identify the main issues within the organized breast cancer screening and suggest changes to it. These changes focus on reducing the time of the process, improving its efficiency and offering greater support to the health user

Knowledge-Intensive Processes: Characteristics, Requirements and Analysis of Contemporary Approaches

Engineering of knowledge-intensive processes (KiPs) is far from being mastered, since they are genuinely knowledge- and data-centric, and require substantial flexibility, at both design- and run-time. In this work, starting from a scientific literature analysis in the area of KiPs and from three real-world domains and application scenarios, we provide a precise characterization of KiPs. Furthermore, we devise some general requirements related to KiPs management and execution. Such requirements contribute to the definition of an evaluation framework to assess current system support for KiPs. To this end, we present a critical analysis on a number of existing process-oriented approaches by discussing their efficacy against the requirements

From BPMN process models to DMN decision models

The interplay between process and decision models plays a crucial role in business process management, as decisions may be based on running processes and affect process outcomes. Often process models include decisions that are encoded through process control flow structures and data flow elements, thus reducing process model maintainability. The Decision Model and Notation (DMN) was proposed to achieve separation of concerns and to possibly complement the Business Process Model and Notation (BPMN) for designing decisions related to process models. Nevertheless, deriving decision models from process models remains challenging, especially when the same data underlie both process and decision models. In this paper, we explore how and to which extent the data modeled in BPMN processes and used for decision-making may be represented in the corresponding DMN decision models. To this end, we identify a set of patterns that capture possible representations of data in BPMN processes and that can be used to guide the derivation of decision models related to existing process models. Throughout the paper we refer to real-world healthcare processes to show the applicability of the proposed approach

Quality of process modeling using BPMN: a model-driven approach

Dissertação para obtenção do Grau de Doutor em Engenharia InformáticaContext: The BPMN 2.0 specification contains the rules regarding the correct usage of the language’s constructs. Practitioners have also proposed best-practices for producing better BPMN models. However, those rules are expressed in natural language, yielding sometimes ambiguous interpretation, and therefore, flaws in produced BPMN models. Objective: Ensuring the correctness of BPMN models is critical for the automation of processes. Hence, errors in the BPMN models specification should be detected and corrected at design time, since faults detected at latter stages of processes’ development can be more costly and hard to correct. So, we need to assess the quality of BPMN models in a rigorous and systematic way. Method: We follow a model-driven approach for formalization and empirical validation of BPMN well-formedness rules and BPMN measures for enhancing the quality of BPMN models. Results: The rule mining of BPMN specification, as well as recently published BPMN works, allowed the gathering of more than a hundred of BPMN well-formedness and best-practices rules. Furthermore, we derived a set of BPMN measures aiming to provide information to process modelers regarding the correctness of BPMN models. Both BPMN rules, as well as BPMN measures were empirically validated through samples of BPMN models. Limitations: This work does not cover control-flow formal properties in BPMN models, since they were extensively discussed in other process modeling research works. Conclusion: We intend to contribute for improving BPMN modeling tools, through the formalization of well-formedness rules and BPMN measures to be incorporated in those tools, in order to enhance the quality of process modeling outcomes

Una extensión a los esquemas preconceptuales para el refinamiento en la representación de eventos y la notación matemática

An event is an occurrence within a particular software system or domain. Software and scientific models are representations of computing and natural systems. Such models have software and scientific components—domain knowledge elements. Scientists and business analysts use such models and their components for recognizing a domain, e.g., pre-conceptual schemas (PCS) used in software engineering. Scientific software domains (SSD) comprise fields in engineering and science, which are focused on developing and simulating scientific software systems for event or phenomenon research. Event-based software development has increased in scientific domains. Approaches to event-driven modeling are used from software/scientific modeling. Some advances have emerged in such approaches for integrating software and scientific components in science and engineering projects. However, scientists and business analysts lack a computational model for SSD in order to integrate both components in the same model. PCS notation includes software components based on structural and dynamic features, which allow for representing events and mathematical operations. Nonetheless, PCS lack scientific components for representing events in SSD. In this Ph.D. Thesis, we propose an extension to pre-conceptual schemas for refining event representation and mathematical notation. Such an extension comprises scientific components as graphical, linguistic, and mathematical structures for the sake of such refinement. We validate our proposal by using both an experimental process and a software application. Extension to PCS is included as a new work product for representing events in SSD. Therefore, the extended PCS are intended to be computing models for scientists and business analysts in scientific software development and simulation processes.Un evento es una ocurrencia en un sistema de software o dominio particular. Los modelos científicos y de software son representaciones de sistemas informáticos o naturales. Esos modelos tienen componentes científicos y de software (elementos del conocimiento del dominio). Científicos y analistas de negocio usan estos modelos y sus componentes para reconocer un dominio. Un ejemplo de esos modelos son los esquemas preconceptuales (EP), que se usan en ingeniería de software. Los dominios de software científico comprenden áreas en ingeniería y ciencia que se enfocan en el desarrollo y simulación de sistemas de software científico para la investigación de eventos o fenómenos. El desarrollo de software dirigido por eventos se viene incrementando en dominios científicos. Enfoques de modelado basado en eventos se usan desde el modelado científico y el modelado de software. En estos enfoques surgen algunos avances para integrar componentes científicos y componentes de software en proyectos de ingeniería y ciencia. Sin embargo, científicos y analistas de negocio carecen de un modelo computacional para dominios de software científico que integre ambos componentes en el mismo modelo. La notación de los EP incluye componentes de software que se basan en características estructurales y dinámicas, los cuales permiten representar eventos y operaciones matemáticas. No obstante, los EP carecen de componentes científicos para representar eventos en dominios de software científico. En esta Tesis Doctoral se propone una extensión a los esquemas preconceptuales para el refinamiento en la representación de eventos y la notación matemática. Esta extensión integra componentes científicos (estructuras gráficas, lingüísticas y matemáticas) para lograr este refinamiento. También, se valida la propuesta mediante un proceso experimental y una aplicación de software. La extensión a los EP se incluye como un nuevo producto de trabajo para representar eventos en dominios de software científico. Por lo tanto, se pretende que los EP extendidos sean modelos de computación, para científicos y analistas de negocio en procesos de desarrollo y simulación de software científico.MincienciasDoctorad

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