A Modeling Tool for PHILharmonicFlows Objects and Lifecycle Processes

Abstract. As opposed to contemporary activity-centric, process-aware information systems (PAIS), for which a multitude of concepts and implementations exist, there is only a very limited number of PAIS implementing data-centric, artifact-centric or object-aware approaches. This demo paper presents the implementation of a modeling environment for the object-aware approach to business process management. Our implementation is based on the PHILharmonicFlows framework, which allows for the definition and execution of business object models. The current implementation of the modeling environment supports the modeling of objects, their attributes as well as relations to other objects. Furthermore, it allows modeling object lifecycle processes, which define the runtime behavior of the various objects. Finally, the modeling environment features a simple app design, reducing complexity for process modelers while still supporting all features of PHILharmonicFlows

Enabling Process Variants and Versions in Distributed Object-Aware Process Management Systems

Business process variants are common in many enterprises and properly managing them is indispensable. Some process management suites already offer features to tackle the challenges of creating and updating multiple variants of a process. As opposed to the widespread activity-centric process modeling paradigm, however, there is little to no support for process variants in other process support paradigms, such as the recently proposed artifact-centric or object-aware process support paradigm. This paper presents concepts for supporting process variants in the object-aware process management paradigm. We offer insights into the distributed object-aware process management framework PHILharmonicFlows as well as the concepts it provides for implementing variants and versioning support based on log propagation and log replay. Finally, we examine the challenges that arise from the support of process variants and show how we solved these, thereby enabling future research into related fundamental aspects to further raise the maturity level of data-centric process support paradigms