ADEPT2 - Next Generation Process Management Technology

If current process management systems shall be applied to a broad spectrum of applications, they will have to be significantly improved with respect to their technological capabilities. In particular, in dynamic environments it must be possible to quickly implement and deploy new processes, to enable ad-hoc modifications of single process instances at runtime (e.g., to add, delete or shift process steps), and to support process schema evolution with instance migration, i.e., to propagate process schema changes to already running instances. These requirements must be met without affecting process consistency and by preserving the robustness of the process management system. In this paper we describe how these challenges have been addressed and solved in the ADEPT2 Process Management System. Our overall vision is to provide a next generation process management technology which can be used in a variety of application domains

Refactoring Process Models in Large Process Repositories.

With the increasing adoption of process-aware information systems (PAIS), large process model repositories have emerged. Over time respective models have to be re-aligned to the real-world business processes through customization or adaptation. This bears the risk that model redundancies are introduced and complexity is increased. If no continuous investment is made in keeping models simple, changes are becoming increasingly costly and error-prone. Though refactoring techniques are widely used in software engineering to address related problems, this does not yet constitute state-of-the art in business process management. Process designers either have to refactor process models by hand or cannot apply respective techniques at all. This paper proposes a set of behaviour-preserving techniques for refactoring large process repositories. This enables process designers to eectively deal with model complexity by making process models better understandable and easier to maintain

Change Mining in Adaptive Process Management Systems

The wide-spread adoption of process-aware information systems has resulted in a bulk of computerized information about real-world processes. This data can be utilized for process performance analysis as well as for process improvement. In this context process mining offers promising perspectives. So far, existing mining techniques have been applied to operational processes, i.e., knowledge is extracted from execution logs (process discovery), or execution logs are compared with some a-priori process model (conformance checking). However, execution logs only constitute one kind of data gathered during process enactment. In particular, adaptive processes provide additional information about process changes (e.g., ad-hoc changes of single process instances) which can be used to enable organizational learning. In this paper we present an approach for mining change logs in adaptive process management systems. The change process discovered through process mining provides an aggregated overview of all changes that happened so far. This, in turn, can serve as basis for all kinds of process improvement actions, e.g., it may trigger process redesign or better control mechanisms

Keeping the Cost of Process Change Low through Refactoring

With the increasing adoption of process-aware information systems (PAIS) large process model repositories have emerged. Over time respective models have to be re-aligned to the real world business processes through customization or adaptation. This bears the risk that model redundancies are introduced and complexity is increased. If no continuous investment is made in keeping models simple, changes are becoming increasingly costly and error-prone. Although refactoring techniques are widely used in software engineering to address related problems, this does not yet constitute state-of-the art in business process management. Consequently, process designers either have to refactor process models by hand or can not apply respective techniques at all. In this paper we propose a set of techniques for refactoring large process repositories, which are behaviour-preserving. The proposed refactorings enable process designers to effectively deal with model complexity by making process models easier to change, less error-prone and better understandable

Issues in Process Variants Mining

In today's dynamic business world economic success of an enterprise increasingly depends on its ability to react to internal and external changes in a quick and flexible way. In response to this need, process-aware information systems (PAIS) emerged, which support the modeling, orchestration and monitoring of business processes and services respectively. Recently, a new generation of flexible PAIS was introduced, which additionally allows for dynamic process and service changes. This, in turn, will lead to a large number of process variants, which are created from the same original process model, but might slightly differ from each other. This paper deals with issues related to the mining of such process variant collections. Our overall goal is to learn from process changes and to merge the resulting model variants into a generic process model in the best possible way. By adopting this generic process model in the PAIS, future cost of process change and need for process adaptations will decrease. Finally, we compare our approach with existing process mining techniques, and show that process variants mining is additionally needed to learn from process changes

Data Flow Correctness in Adaptive Workflow Systems

Enterprises must be able to quickly adapt their business processes to react to changes in their environment. Needed business agility is often hindered by the lacking flexibility of contemporary workflow systems. In response to this inflexibility, adaptive workflow systems have emerged, which enable the dynamic adaptation of running workflows. One of the most important challenges in this context is to avoid inconsistencies and errors. So far, approaches providing respective correctness criteria for dynamic workflow change have mainly focused on control flow correctness (e.g., avoidance of deadlocks). However, little attention has been paid to data flow correctness even though this is crucial for any application of dynamic workflow change in practice. Specifically, missing or inconsistent input data of workflow activities, for example, can lead to blocking or breakdown of the underlying workflow system. This paper deals with fundamental challenges related to data flow correctness. We revisit and discuss data flow correctness at different phases of the workflow life cycle (i.e., buildtime and runtime), and show how data flow correctness can be ensured in an efficient way when dynamically changing a workflow

On Representing Instance Changes in Adaptive Process Management Systems

By separating the process logic from the application code process management systems (PMS) offer promising perspectives for automation and management of business processes. However, the added value of PMS strongly depends on their ability to support business process changes which can affect the process type as well as the process instance level. This does not only impose challenging conceptual issues (e.g., correctness of process schemata after changes) but also requires sophisticated implementation concepts with respect to efficient algorithms, flexible architectures, and reasonable treatment of resources. In this paper we sketch the general implementation concepts for representing process type and process instance data as well as for realizating process schema evolution. All these concepts have been developed and are currently implemented in the ADEPT2 prototype within the AristaFlow project

Improving Exception Handling by Discovering Change Dependencies in Adaptive Process Management Systems

Process-aware information systems should enable the flexible alignment of business processes to new requirements by supporting deviations from the predefined process model at runtime. To facilitate such dynamic process changes we have adopted techniques from casebased reasoning (CBR). In particular, our existing approach allows to capture the semantics of ad-hoc changes, to support their memorization, and to enable their reuse in upcoming exceptional situations. To further improve change reuse this paper presents an approach for discovering dependencies between ad-hoc modifications from change history. Based on this information better user assistance can be provided when dynamic process changes have to be made

Towards Compliance of Cross-Organizational Processes and their Changes

Businesses require the ability to rapidly implement new processes and to quickly adapt existing ones to environmental changes including the optimization of their interactions with partners and customers. However, changes of either intra- or cross-organizational processes must not be done in an uncontrolled manner. In particular, processes are increasingly subject to compliance rules that usually stem from security constraints, corporate guidelines, standards, and laws. These compliance rules have to be considered when modeling business processes and changing existing ones. While change and compliance have been extensively discussed for intra-organizational business processes, albeit only in an isolated manner, their combination in the context of cross-organizational processes remains an open issue. In this paper, we discuss requirements and challenges to be tackled in order to ensure that changes of cross-organizational business processes preserve compliance with imposed regulations, standards and laws