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Many organizations use business process models for documenting their business operations. In recent years, the Business Process Model and Notation (BPMN) evolved into the leading standard for process modeling. However, BPMN is complex: The specification offers a huge variety of different elements and often several representational choices for the same semantics. This raises the question of how well modelers can deal with these choices. Empirical insights into BPMN usage from the perspective of practitioners are still missing. We close this gap by analyzing a large set of BPMN 2.0 process models from practice. We found that particularly representational choices for splits and joins, the correct use of message flow, the proper decomposition of models, and the consistent labeling appear to be connected with quality issues. Based on our findings we give five recommendations how these issues can be avoided in the future. The work summarized in this extended abstract has been published in [LMG16].
Many organizations use business process models to document business operations and formalize business requirements in software-engineering projects. The Business Process Model and Notation (BPMN), a specification by the Object Management Group, has evolved into the leading standard for process modeling. One challenge is BPMN's complexity: it offers a huge variety of elements and often several representational choices for the same semantics. This raises the question of how well modelers can deal with these choices. Empirical insights into BPMN use from the practitioners' perspective are still missing. To close this gap, researchers analyzed 585 BPMN 2.0 process models from six companies. They found that split and join representations, message flow, the lack of proper model decomposition, and labeling related to quality issues. They give five specific recommendations on how to avoid these issues.
Process instantiation
(2009)
Although several process modeling languages allow one to specify processes with multiple start elements, the precise semantics of such models are often unclear, both from a pragmatic and from a theoretical point of view. This paper addresses the lack of research on this problem and introduces the CASU framework (from Creation, Activation, subscription, Unsubscription). The contribution of this framework is a systematic description of design alternatives for the specification of instantiation semantics of process modeling languages. We classify six prominent languages by the help of this framework. We validate the relevance of the CASU framework through empirical investigations involving a large set of process models from practice. Our work provides the basis for the design of new correctness criteria as well as for the formalization of Event-driven Process Chains (EPCs) and extension of the Business Process Modeling Notation (BPMN). It complements research such as the workflow patterns.
While the maturity of process mining algorithms increases and more process mining tools enter the market, process mining projects still face the problem of different levels of abstraction when comparing events with modeled business activities. Current approaches for event log abstraction try to abstract from the events in an automated way that does not capture the required domain knowledge to fit business activities. This can lead to misinterpretation of discovered process models. We developed an approach that aims to abstract an event log to the same abstraction level that is needed by the business. We use domain knowledge extracted from existing process documentation to semi-automatically match events and activities. Our abstraction approach is able to deal with n:m relations between events and activities and also supports concurrency. We evaluated our approach in two case studies with a German IT outsourcing company. (C) 2014 Elsevier Ltd. All rights reserved.
Nowadays, business processes are increasingly supported by IT services that produce massive amounts of event data during the execution of a process. These event data can be used to analyze the process using process mining techniques to discover the real process, measure conformance to a given process model, or to enhance existing models with performance information. Mapping the produced events to activities of a given process model is essential for conformance checking, annotation and understanding of process mining results. In order to accomplish this mapping with low manual effort, we developed a semi-automatic approach that maps events to activities using insights from behavioral analysis and label analysis. The approach extracts Declare constraints from both the log and the model to build matching constraints to efficiently reduce the number of possible mappings. These mappings are further reduced using techniques from natural language processing, which allow for a matching based on labels and external knowledge sources. The evaluation with synthetic and real-life data demonstrates the effectiveness of the approach and its robustness toward non-conforming execution logs.