Abstract
The operational backbone of modern organizations is the target of business process management, where business process models are produced to describe how the organization should react to events and coordinate the execution of activities so as to satisfy its business goals. At the same time, operational decisions are made by considering internal and external contextual factors, according to decision models that are typically based on declarative, rule-based specifications that describe how input configurations correspond to output results. The increasing importance and maturity of these two intertwined dimensions, those of processes and decisions, have led to a wide range of data-aware models and associated methodologies, such as BPMN for processes and DMN for operational decisions. While it is important to analyze these two aspects independently, it has been pointed out by several authors that it is also crucial to analyze them in combination. In this paper, we provide a native, formal definition of DBPMN models, namely data-aware and decision-aware processes that build on BPMN and DMN S-FEEL, illustrating their use and giving their formal execution semantics via an encoding into Data Petri nets (DPNs). By exploiting this encoding, we then build on previous work in which we lifted the classical notion of soundness of processes to this richer, data-aware setting, and show how the abstraction and verification techniques that were devised for DPNs can be directly used for DBPMN models. This paves the way towards even richer forms of analysis, beyond that of assessing soundness, that are based on the same technique.
Highlights
We choose the Data Petri Net (DPN) formalism [17,19], which extends the Petri nets with data attributes, based on which one can express data conditions guarding the enablement of transitions
The resulting net is enriched with data manipulation operations that are essential to reconstruct the interplay of the process, the data objects, and the decision logic
In this paper we have studied the integration of BPMN with DMN S-friendly enough expression language (FEEL) decisions into a single model, called DBPMN, providing a unified modeling framework which allows to capture complex processes enriched with decision points encoding the business decision logic
Summary
Modern organizations rely on a variety of management disciplines, with IT as underlying enabling technology, to drive their internal operations and the interactions with customers and other organizations, and in turn continuously improve and optimize strategic goals. 2. We define the execution semantics of DBPMN through DPNs, by modularly translating the process controlflow using well-known methods, and enriching the resulting net with guards and updates that reflect the manipulation of data objects and the logic of decision tables linked to business rule tasks. We define the execution semantics of DBPMN through DPNs, by modularly translating the process controlflow using well-known methods, and enriching the resulting net with guards and updates that reflect the manipulation of data objects and the logic of decision tables linked to business rule tasks This is done via a two-step encoding, which first translates a DBPMN process into an extended DPN that can express full boolean guards and updates over multiple variables, and translates the extended DPN into a standard DPN.
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call