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

Data in Business Process Models. A Preliminary Empirical Study

Traditional activity-centric process modeling languages treat data as simple black boxes acting as input or output for activities. Many alternate and emerging process modeling paradigms, such as case handling and artifact-centric process modeling, give data a more central role. This is achieved by introducing lifecycles and states for data objects, which is beneficial when modeling data-or knowledge-intensive processes. We assume that traditional activity-centric process modeling languages lack the capabilities to adequately capture the complexity of such processes. To verify this assumption we conducted an online interview among BPM experts. The results not only allow us to identify various profiles of persons modeling business processes, but also the problems that exist in contemporary modeling languages w.r.t. The modeling of business data. Overall, this preliminary empirical study confirms the necessity of data-awareness in process modeling notations in general

Cross-Collaboration Processes based on Blockchain and IoT: a survey

Cross-collaboration processes are decentralized by nature and their centralized monitoring can trigger mistrust. Nevertheless, a decentralized monitoring facility such as a blockchain-based and Internet-of-Things-aware (IoT-aware) business process management system can reduce this pitfall. However, concerns related to usability, privacy, and performance, hamper the wide adoption of these systems. To better understand the challenges at stake, this paper reviews the use of blockchain and IoT devices in cross-collaboration processes. This survey sheds some light on standard uses such as model engineering or permissioned blockchains which help adopt cross-collaboration business process management systems. Moreover, with respect to process design, two schools of thought coexist, addressing both constrained and loosely processes. Furthermore, a focus on data-centric processes appears to get some momentum, as many industries go digital. Finally, this survey underlines the need to orient future research towards a more flexible, scalable, and data-aware blockchain-based business process management system

Data flow and human tasks in business process models

In contrast with the traditional view that represents business processes as flow charts of tasks, the artifact-centric one stresses the importance of the data flow, as the main responsible for the activation of the tasks. This viewpoint leads to reconsider the interactions between the process and its tasks as well as the execution mode of the tasks. The greatest benefits concern human tasks; they should no longer be considered only as services implemented by people but they may enable their performers to make choices. Two kinds of human choices are considered in this paper: the choice of the inputs to be acted on, and the choice of the course of action to be taken. The execution mode of human tasks is also examined and three categories are illustrated: performer-driven tasks, process-driven tasks and macro tasks. These categories come with a number of patterns, which are exemplified in this paper