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Purpose
The quality of a Business Process (BP) model is vital for the successful accomplishment of all its lifecycle phases. Indeed, a high-quality BP model makes its implementation, execution and evaluation easier. In the literature, the improvement of BP model quality has been dealt with using several techniques. For instance, modeling guidelines, refactoring techniques, and transformation rules are the most used ones. The purpose of this paper is to exploit existing initiatives in this field to help designers improve their BP models.


Design/methodology/approach
This paper draws up a systematic inventory of the existing approaches to improve the quality of BP models. Moreover, it provides a comparative evaluation with the aim of identifying the particularities of each approach as well as the common gaps in the state of the art. Finally, it proposes a guiding framework, called BP-Quality, that supports designers in improving the quality of their BP models.


Findings
The usability of BP-Quality is illustrated through a case study and a set of experiments. The preliminary experimental evaluation of this guiding framework shows encouraging results.


Originality/value
The proposed guiding framework has the merit of exploiting existing initiatives in the field of BP quality improvement. In addition, it customizes and optimizes the quality improvement process according to the particularities of each BP model.

The emergence and quick adoption of social media, exemplified with Web 2.0 applications like Facebook and Instagram, has led to a new form of business processes (BP) called social business processes (SBP). An SBP encompasses aspects from both the business world and the social world. Contrarily to the business aspects, which can be captured using existing modeling languages like the standard business process modeling notation (BPMN), the social aspects cannot be modeled by these languages. This paper addresses this limitation by defining BPMN4Social, a domain specific language for SBP modeling. BPMN4Social minimally extends BPMN with social concepts that are independent of any particular Web 2.0 application. Besides its support by the BPMN2 modeler editor, BPMN4Social is accompanied by a catalog of socialization patterns that describe common social scenarios and that can assist business process designers in their modeling activities. This paper also reports on the evaluation of BPMN4Social usability in terms of effectiveness, efficiency, and satisfaction.

Purpose
Restructuring a business process (BP) model may enhance the BP performance and improve its understandability. So-far proposed restructuring methods use either refactoring which focuses on structural aspects, social network discovery which uses semantic information to guide the affiliation process during its analysis, or social network rediscovery which uses structural information to identify clusters of actors according to their relationships. The purpose of this paper is to propose a hybrid method that exploits both the semantic and structural aspects of a BP model.


Design/methodology/approach
The proposed method first generates a social network from the BP model. Second, it applies hierarchical clustering to determine the performers' partitions; this step uses the social context which specifies features related to performers, and two new distances that account for semantic and structural information. Finally, it applies a set of behavioral and organizational restructuring rules adapted from the graph optimization domain; each rule uses the identified performers' partitions and the business context to reduce particular quality metrics.


Findings
The efficiency of the proposed method is illustrated through well-established complexity metrics. The illustration is made through the development of a tool that fully supports the proposed method and proposes a strategy for the application of the restructuring rules.


Originality/value
The proposed method has the merit of combining the semantic and structural aspects of a Business Process Modeling Notation model to identify restructuring operations whose ordered application reduces the complexity of the initial model.