Configurable Business Process Models Research Articles

Organization-driven business process configurable modeling for spatial variability

Nowadays, enterprises need to continually adjust their business processes to adapt to the changes of business environments, especially when one business needs to be deployed in different application scenarios, which is called spatial variability in this paper. In the field of BPM (Business Process Management), configurable business process models have demonstrated their effectiveness in aspects of process modeling and model reuse. Yet, we found that the existing techniques lead to complex configurable models, and are inadequate for model reuse especially for the spatial variability issue because they neglect the root impact of organizations on control flow. S-BPM (Subject-oriented Business Process Management) models provide a solid foundation for dealing with complex applications and help to bridge the gap between business and IT for process execution. In this paper, we propose an organization-driven business process configurable modeling approach for spatial variability by integrating both restriction and extension operations based on the S-BPM paradigm, in which business objects are also included. Our approach is validated with a general business process developed for the Real Estate Administration (REA) in a certain province of China. The resulting configurable modeling framework can express the heterogeneous activity sequences for one business and has the potential to generate process models for uncertain environments in a new organization structure.

Handling Sudden and Recurrent Changes in Business Process Variability: Change Mining based Approach

Changes are random and unavoidable actions in business processes, and they are frequently overlooked by managers, especially when managers need to deal with a collection of process variants. Because they must manage every single business process variant separately which is a time-consuming task. They exist many approaches to manage a collection of business process and deal with variability. Such as process mining approaches, that can discover configurable business process models, enhancing them and verify conformity automatically. However, those approaches do not cover changes and concept drift that occur over time. This paper presents a novel change mining approach that discovers changes in a collection of event logs and reports them on a change log. This change log can be analyzed to determine whether the changes are sudden or recurrent and recommend afterward some improvement to the configurable process model.

A semantic framework for configurable business process as a service in the cloud

With the advent of Cloud Computing, new opportunities for Business Process Outsourcing services have emerged. Business Process as a Service (BPaaS), a new cloud service model, has recently gained a great importance for outsourcing cloud-based business processes constructed for multi-tenancy. In such a multi-tenant environment, using configurable business process models enables the sharing of a reference process among different tenants that can be customized according to specific needs. With a large choice of configurable process modeling languages, different providers may deliver configurable processes with common functionalities but different representations which makes the process discovery and configuration a manual tedious task. This in turn creates cloud silos and vendors lock-in with non-reusable configurable BPaaS models. Therefore, with the aim of enabling the interoperability between multiple BPaaS providers, we propose in this paper a semantic framework for BPaaS configurable models. Taking advantage of Semantic Web technologies and data mining techniques, our framework allows for (1) an ontology-based high level abstract representation of BPaaS configurable models enriched with configuration guidelines and (2) an automated approach for extracting the configuration guidelines from existing process repositories. To show the feasibility and effectiveness of our approach, we extend Signavio with our semantic framework and conduct experiments on a dataset from SAP reference model.

Generating optimized configurable business process models in scenarios subject to uncertainty

ContextThe quality of business process models (i.e., software artifacts that capture the relations between the organizational units of a business) is essential for enhancing the management of business processes. However, such modeling is typically carried out manually. This is already challenging and time consuming when (1) input uncertainty exists, (2) activities are related, and (3) resource allocation has to be considered. When including optimization requirements regarding flexibility and robustness it becomes even more complicated potentially resulting into non-optimized models, errors, and lack of flexibility. ObjectiveTo facilitate the human work and to improve the resulting models in scenarios subject to uncertainty, we propose a software-supported approach for automatically creating configurable business process models from declarative specifications considering all the aforementioned requirements. MethodFirst, the scenario is modeled through a declarative language which allows the analysts to specify its variability and uncertainty. Thereafter, a set of optimized enactment plans (each one representing a potential execution alternative) are generated from such a model considering the input uncertainty. Finally, to deal with this uncertainty during run-time, a flexible configurable business process model is created from these plans. ResultsTo validate the proposed approach, we conduct a case study based on a real business which is subject to uncertainty. Results indicate that our approach improves the actual performance of the business and that the generated models support most of the uncertainty inherent to the business. ConclusionsThe proposed approach automatically selects the best part of the variability of a declarative specification. Unlike existing approaches, our approach considers input uncertainty, the optimization of multiple objective functions, as well as the resource and the control-flow perspectives. However, our approach also presents a few limitations: (1) it is focused on the control-flow and the data perspective is only partially addressed and (2) model attributes need to be estimated.

Configuring business process models

A configurable business process model (sometimes referred to as a reference business process model) may be configured to meet the specific requirements of an organization. The configuration activity is required to automatically determine the variability of a configurable process model and ensure the correctness of a specific process model. However, few approaches solve the problem. In this paper, we propose an innovative approach for automatically separating a configurable process model into atomic and correct sub-process models (sub-process models without abnormal behavioral problems). The atomic sub-process models that fulfill specific requirements are merged into specific process models that are provided for organizations. Compared with existing approaches, since the configuration activity is incorporated into the verification process of a process model at design time and can obtain all feasible configurations, our approach avoids independently handling the configuration activity and does not suffer from computational complexity. Moreover, our approach is language-independent. We have developed a prototype tool for configuring these process models.