Abstract. As business process management is increasingly applied in practice, more companies document their operations in the form of process models. Since users require descriptions of one process on various levels of detail, there are often multiple models created for the same process. Business process model abstraction emerged as a technique reducing the number of models to be stored: given a detailed process model, business process model abstraction delivers abstract representations for the same process. A key problem in many abstraction scenarios is the transition from detailed activities in the initial model to coarse-grained activities in the abstract model. This transition is realized by an aggregation operation clustering multiple activities to a single one. So far, humans decide on how to aggregate, which is expensive. This paper presents a semi-automated approach to activity aggregation that reduces the human effort significantly. The approach takes advantage of an activity meronymy relation, i.e., part-of relation defined between activities. The approach is semi-automated, as it proposes sets of meaningful aggregations, while the user still decides. The approach is evaluated by a real-world use case. 1

Abstract. Process models are essential tools for managing, understanding and changing business processes. Yet, from a user perspective they can quickly become too complex to deal with. Abstraction – aggregating detailed fragments into more coarse-grained ones – has proven to be a valuable technique to simplify the view on a process model. Various techniques that automate the decision of which model fragments to aggregate have been defined and validated by recent research, but their application is hampered by the lack of abilities to generate meaningful names for such aggregated parts. In this paper, we address this problem by investigating naming strategies for individual model fragments and process models as a whole. Our contribution is an automatic naming approach that builds on the linguistic analysis of process models from industry. 1

doi:10.1016/j.is.2009.03.009 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Activity Labeling in Process Modeling:

The contemporary workplace is characterized by all kinds of (synchronous and asynchronous) human interactions both within and across organizations like meetings, discussions, consultations, conversations etc. Although such interactions are an essential part of a modern organization’s business processes, current research on business processes focuses mainly on the sequence of activities or tasks within a process (i.e. workflows), and not on human interactions. This research presents an application of an interaction-centric process modeling language named TALL for collaboration processes (CPs). For the illustration and evaluation of the language, a case study of a real-life CP is presented. The case study concerns the collaborative work of a Dutch municipality in relation to young persons who drop out from school. Shortcomings of existing workflow-based languages in the modeling of CPs are elicited. In the case study, the language is shown to address these shortcomings. The main strengths of TALL are (1) the visualization of human interactions, their roles and agents, and their relationships in a process-oriented tree diagram, and (2) the explicit recognition and specification of the local behaviors of the agents who are assigned to play the roles in a given interaction. Evaluation results are discussed. These results comprise practical insights, and contributions to theory and language development.

In order to make good decisions about the design of information systems, an essential skill is to understand process models of the business domain the system is intended to support. Yet, little knowledge to date has been established about the factors that affect how model users comprehend the content of process models. In this study, we use theories of semiotics and cognitive load to theorize how model and personal factors influence how model viewers comprehend the syntactical information of process models. We then report on a four-part series of experiments, in which we examined these factors. Our results show that additional semantical information impedes syntax comprehension, and that theoretical knowledge eases syntax comprehension. Modeling experience further contributes positively to comprehension efficiency, measured as the ratio of correct answers to the time taken to

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