Discovering Decision Models from Event Logs

Development of modern software systems consists of many different phases, the sequence of whom is referred to as the software life cycle. During these phases, business analysts gather requirements from clients and try to design a system in order to fulfil these requirements. Software design of complex systems exploits various notations for representing knowledge about system structure and behaviour, decisions, processes and different cases. These elements are modelled using such graphical notations, maintained by Object Management Group (OMG), such as UML (Unified Modelling Language), DMN (Decision Model and Notation), BPMN (Business Process Model and Notation), and CMMN (Case Management Model and Notation). In this paper, we present our work-in-progress analysis of the current state of the art in knowledge interchange for these notations. Moreover, we identify the integration or interchange approaches in terms of application areas. Our goal is to provide an input for an integrated method of designing systems with the use of these notations.

Reliability of BPMN Business Processes

During software maintenance, software models play a fundamental role, as they assist in understanding the software that will be corrected or evolved. Business process models, generally produced using Business Process Modeling and Notation (BPMN), help understanding the business processes supported by software. Therefore, these models are instrumental in maintaining corporate software. However, it is common that during software maintenance, models related to the software are not updated. Thus, the models are inconsistent with the version in use of the software, becoming increasingly obsolete over time. Given that, we developed the e-VOL BPMN technique to support BPMN models' evolution during software maintenance. The objective of e-VOL BPMN is to assist software engineers and developers, with little experience in BPMN notation, to carry out the necessary evolutions in the processes supported by the software. We evaluated the e-VOL BPMN in two experiments, and the results showed evidence that e-VOL BPMN helps the evolution of BPMN models in an appropriate manner. With this technique, such professionals can keep BPMN models up to date. This makes it easier to understand the processes supported by the software, which reduces the time and cost of future maintenance. Furthermore, understanding these processes correctly can reduce undue modifications to the software, contributing to the quality of the maintained software.

Currently, the number of patients with neurological diseases is increasing, especially those older than 65 suffering from Alzheimer’s disease. This development increases the emphasis on understanding and mapping treatment and care processes, not only for the elderly. Service providers (of both treatment and care) are under general pressure to decrease charges and maintain or improve existing levels of care. This situation is significantly influenced by a comprehensive knowledge of the whole process and its values. This publication therefore aims to describe the fundamental procedural aspects of caring for patients with Alzheimer’s disease, using Business Process Model and Notation (BPMN). It also aims to show the possibilities of using BPMN in the description of treatment and care. Modeling of the business process is more frequently being applied not only by businesses but also by scientists involved in process models. It is used to model medical topics, with approximately 10% of its publications only, and most of these publications deal only with clinical pathways, not with overall treatment and care processes. However, the BPMN model allows the whole process of medical and nonmedical care for patients with Alzheimer’s disease to be described, including the decomposition of partial activities into individual threads and sub-processes or atomic tasks. This paper presents the BPMN modeling and mapping of the specific care path for neurodegenerative patients. The text provides a new perspective on the BPMN modeling of Alzheimer’s disease. The presented model offers the option of expanding treatment cost calculation to simulate the process using graphical tools and languages. The overall view of this system creates a much more complex concept of the system and its surroundings.

A metamodel to integrate business processes time perspective in BPMN 2.0

The Business Process Model and Notation (BPMN) has become the de facto standard for process modeling. Currently, BPMN models can be (a) analyzed or simulated using specialized tools, (b) executed using business process management systems (BPMSs), or (c) used for requirements elicitation. Although there are many studies comparing BPMN to other modeling techniques for analyzing and executing processes, there are few showing the suitability of BPMN models as a source for requirements comprehension in projects where process-aware software is built without using BPMSs. This chapter presents a study aimed at comparing the comprehension of software requirements regarding a business process using either BPMN or traditional techniques, such as use cases. In our study, we analyzed responses of 120 undergraduate and graduate students regarding the requirements comprehension achieved when using only BPMN models, only use cases, or both. The results do not show significant impact of the artifacts on the comprehension level. However, when the understanding of the requirement involves sequence of activities, using the BPMN shows better results on the comprehension time.

BackgroundBusiness process modelling is increasingly used not only by the companies’ management but also by scientists dealing with process models. Process modeling is seldom done without decision-making nodes, which is why operational research methods are increasingly included in the process analyses.ObjectiveThis systematic literature review aimed to provide a detailed and comprehensive description of the relevant aspects of used operational research techniques in Business Process Model and Notation (BPMN) model.MethodsThe Web Of Science of Clarivate Analytics was searched for 128 studies of that used operation research techniques and business process model and notation, published in English between 1 January 2004 and 18 May 2020. The inclusion criteria were as follows: Use of Operational Research methods in conjunction with the BPMN, and is available in full-text format. Articles were not excluded based on methodological quality. The background information of the included studies, as well as specific information on the used approaches, were extracted.ResultsIn this research, thirty-six studies were included and considered. A total of 11 specific methods falling into the field of Operations Research have been identified, and their use in connection with the process model was described.ConclusionOperational research methods are a useful complement to BPMN process analysis. It serves not only to analyze the probability of the process, its economic and personnel demands but also for process reengineering.

Business process modeling and management approaches are increasingly used and disclosed between organizations as a means of optimizing and streamlining the business activities. Among the various existing modeling languages, we stress the Business Process Model and Notation (BPMN), currently in version 2.0. BPMN is a widespread OMG standard that is actually used either in academia and in organizations. BPMN enables business process modeling, but does not facilitate the modeling of the information infrastructure involved in the process. However, interest in the data and its preservation has increased in BPMN's most recent version. The aim of this paper is to study BPMN 2.0, particularly on the usage and persistence of data, and present an approach for obtaining an early data model from the business process modeling, which may then be used as a starting data model in the software development process.

Large Language Models (LLMs) are emerging as powerful tools for automating business process modeling, promising to streamline the translation of textual process descriptions into Business Process Model and Notation (BPMN) diagrams. However, the extent to which these Al systems can produce high-quality BPMN models has not yet been rigorously evaluated. This paper presents an early evaluation of five LLM-powered BPMN generation tools that automatically convert textual process descriptions into BPMN models. To assess the external quality of these Al-generated models, we introduce a novel structured evaluation framework that scores each BPMN diagram across three key process model quality dimensions: clarity, correctness, and completeness, covering both accuracy and diagram understandability. Using this framework, we conducted experiments where each tool was tasked with modeling the same set of textual process scenarios, and the resulting diagrams were systematically scored based on the criteria. This approach provides a consistent and repeatable evaluation procedure and offers a new lens for comparing LLM-based modeling capabilities. Given the focused scope of the study, the results should be interpreted as an exploratory benchmark that surfaces initial observations about tool performance rather than definitive conclusions. Our findings reveal that while current LLM-based tools can produce BPMN diagrams that capture the main elements of a process description, they often exhibit errors such as missing steps, inconsistent logic, or modeling rule violations, highlighting limitations in achieving fully correct and complete models. The clarity and readability of the generated diagrams also vary, indicating that these Al models are still maturing in generating easily interpretable process flows. We conclude that although LLMs show promise in automating BPMN modeling, significant improvements are needed for them to consistently generate both syntactically and semantically valid process models.

PurposeBusiness process modelling integrates and visualizes relevant information essential for managing day-to-day business operations. It plays a critical role in the design and execution of business transformations. Recognizing the role of process modelling, a large number of modelling languages, methods and techniques have been developed, each offering diverse advantages and having inherent limitations. Traditional and popular process modelling approaches focus on the exact specification of the control flow of business processes, whereas more recent approaches like Subject-oriented Business Process Management (S-BPM) are focused on the communication between process participants. This study aims to provide comparative insights about these two approaches through their experimental application. This study does so by comparing Business Process Model and Notation (BPMN); a control flow approach, with S-BPM; a communication approach, with a specific focus on their suitability for novice modellers.Design/methodology/approachThis paper reports on an exploratory experiment that compares BPMN to S-BPM. Applying cognitive load theory, this study compares the experiences and outcomes of novice process modellers, assessing perceived ease of use, model quality (syntactic and semantic) and modelling efficiency (time to model) across the two approaches.FindingsStudy results show that S-BPM (a communication approach) led to significantly better user performances for process modelling than BPMN (a control flow approach). This study points to how a different modelling approach such as S-BPM could be either considered as an alternative or to complement the more popular control flow approach BPMN. This observation was especially relevant for modelling contexts where domain experts are novice process modellers.Originality/valueThis study provides the first empirical evidence that communication approaches like S-BPM could outperform modelling approaches which are control-flow based (i.e. BPMN), especially when being used by novice process modellers who hold the domain and process knowledge. This study uses this as a springboard to present important considerations for practice and guide future process modelling research.

In 2014, the Object Management Group (OMG) published the Case Management Modeling and Notation (CMMN) version 1.0 specification, which is a new process modeling specification to complement its Business Process Modeling and Notation (BPMN) specification. The declarative nature of CMMN is intended to supplement the procedural perspective of BPMN. CMMN takes a data-centric view to process modeling based on business artifacts to provide flexibility for knowledge workers, while retaining the advantages of business process management (BPM) systems.

In this study, we argue that important of strategy, business, design, and technology alignment affecting an organization’s ICT enterprise architecture is also reflected by business management perspective and business process model. Enterprise Architecture (EA) is an effective way to develop current and future views of the entire enterprise. EA does this primarily by integrating the processes for strategic, business and technology planning in a way that also integrates with other business and technology process. The seamless collaborate and synergize working between technology and non-technology executives and professionals are needed in the designing and formulating enterprise architecture. Therefore, in order to facilitate this analysis, we propose an approach to relate EA specified business architecture to business process innovation, modeled using Service blueprint and Business Process Model and Notation (BPMN). Our approach is accompanied by a method that supports the process automation of business process model and is illustrated by a Healthcare service practice as a case study.

Business Process Model and Notation—BPMN

Effective monitoring and testing during each step are essential for document verification in research and development (R&D) projects. In software development, proper testing is required to verify it carefully and constantly because of the invisibility features of software. However, not enough studies on test processes for R&D projects have been done. Thus, in this paper, we introduce a Test Maturity Model integration (TMMi)-based software field R&D test process that offers five integrity levels and makes the process compatible for different types of projects. The Software & Systems Process Engineering Metamodel (SPEM) is used widely in the software process-modeling context, but it lacks built-in enactment capabilities, so there is no tool or process engine that enables one to execute the process models described in SPEM. Business Process Model and Notation (BPMN)-based workflow engines can be a solution for process execution, but process models described in SPEM need to be converted to BPMN models. Thus, we propose an approach to support enactment of SPEM-based process models by converting them into business processes. We show the effectiveness of our approach through converting software R&D test processes specified in SPEM in a case study.

Business process models expressed in languages such as BPMN (Business Process Model and Notation), play a critical role in implementing the workflows in modern enterprises. However, control flow errors such as deadlocks and lack of synchronization, and syntactic errors arising out of poor modeling practices often occur in industrial process models. A major challenge is to provide the means and methods to detect such errors and more importantly, to identify the location of each error. In this work, we develop a formal framework of diagnosing errors by locating their occurrence nodes in business process models at the level of sub-processes and swim-lanes. We use graph-theoretic techniques and Petri net-based analyses to detect syntactic and control flow-related errors respectively. While syntactic errors can be easily located on the processes themselves, we project control-related errors on processes using a mapping from Petri nets to processes. We use this framework to analyze a sample of 174 industrial BPMN process models having 1262 sub-processes in which we identify more than 2000 errors. We are further able to discover how error frequencies change with error depth, how they correlate with the size of the sub-processes and swim-lane interactions in the models, and how they can be predicted in terms of process metrics like sub-process size, coefficient of connectivity, sequentiality and structuredness.