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Astract: Business document types exchanged between business partners significantly differ among different industries and geopolitical regions. Therefore, the corresponding documents must be based on business document standards which can be adapted to any business context specific business scenario. In order to use a business document standard in a specific context, user groups develop so-called implementation guidelines based on a small subset of the standard. However, defining a new implementation guideline for a specific business context always starts from scratch, which is time-consuming and often leads to heterogeneous interpretations of the standard. It is our goal to speed up this development process and to create more homogeneous implementation guidelines by learning from existing models. Therefore, in this paper we define an ontology based model to formally represent business context. Furthermore, we propose an approach to assign instances of this model to already existing document implementation guidelines. This serves as a basis for calculation of the content model (subset) of a new implementation guideline which is relevant in a particular context presented by our ontology based business context model.

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Astract: One and the same inter-organizational business process - such as e-procurement - may be executed differently in different industries, geopolitical regions, etc. Thus, a standardized reference model for inter-organizational business processes must be customized to the specific business context (industry, region, etc.). In order to share, search, and (partially) re-use context specific adaptations it is essential not only to store the adaptations, but also a business context model where these adaptations are valid. In this paper we describe the Unified Context Model (UCM) introduced by the United Nations Centre for Trade Facilitation and Electronic Business (UN/CEFACT). We explain the shortcomings of the approach and show how these can be undermined by our Enhanced Unified Context Model (E-UCM). The enhanced model serves as a basis for contextualizing business documents which are exchanged between different inter-organizational business processes. Having such an approach at hand, helps prevent negative trends in today's business, such as interoperability issues, inconsistencies and heterogeneous interpretations of the exchanged data contents.

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Astract: The reliable, efficient and seamless exchange of business information is essential for a successful execution of the interwoven business processes. However, development of the contents for electronic data exchange is time consuming and usually can not follow the agile demands of the today's business. If we could contextualize the pieces of the currently valid business information, we could predict its possible context specific variations. Therefore, the business contextual knowledge in which the already existing data contents are valid could be exploited to (semi-) automatically generate new, more homogeneous contents for electronic data interchange.

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Astract: In model-driven engineering (MDE), models constitute the main development artifacts. As a consequence, their quality significantly affects the quality of the final product. Thus, adequate techniques are required for ensuring the quality of models. We present a testing framework, comprising a test specification language and an interpreter, for validating the functional correctness of UML activities. For this purpose, we utilize the executability of a subset of UML provided by the fUML standard. As UML activities are employed for different purposes, from high-level process specifications to low-level object manipulations, the proposed testing framework not only allows to validate the correctness in terms of input/output relations, but also supports testing intermediate results, as well as the execution order of activity nodes. First experiments indicate that the proposed testing framework is useful for ensuring the correct behavior of fUML activities.

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Astract: Business transactions between companies are more and more executed by a flow of well-defined electronic business documents. The resulting inter-organizational business processes are often realized by concepts known from service-oriented computing. Exchanging a business document corresponds to a service call and the input/output of the service calls commonly follows a certain business document standard. However, these standards typically present the superset of all required elements used in any business context. In a specific context (in a specific industry, in a specific geopolitical region, etc.) the input/output is adjusted to this context by constraints on the generic structure. Accordingly, a specific service is always used in a specific context. It follows that it is important to define the business context of a service (in a structured format). For this purpose we have developed the Business Context Ontology model (BCOnt). In this paper we elaborate on the theoretical concepts and the underlying algorithms as well as on their implementation in practice.

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Astract: We steadily aim at improving our first year´s university course Introduction to Object-Oriented Modelling. To that end we explore how technological teaching support may be used to extend the classical classroom experience for the students. For several years, we used Web-based out-of-the-box e-learning tools for providing additional teaching support. This solution works well, but has some severe restrictions. In this paper, we show how to overcome these restrictions with a custom implementation. Further, we are now starting to explore mobile facilities as offered by smartphone Apps as well as social media facilities in order to enhance the learning experience in our modelling course. We report on our first experiences with these kinds of technologies.

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Astract: The Resource-Event-Agent (REA) ontology is a powerful and well accepted approach towards the design of enterprise information systems in the academic world. However, it still lacks application in industry products. Software like ERP applications might benefit from REA data structure by building their product's core on a robust, economic theory based, business ontology. Consequently, we have lately started a feasibility study with an Austrian ERP vendor, in which we investigate whether or not the ERP vendor can use the REA ontology as basis for their new ERP system. This paper is aimed at raising questions about uncertainties in REA we came across in our study so far.

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