2018, Hinkelmann, Knut

Decision makers use models to understand and analyze a situation, to compare alternatives and to find solutions. Additionally, there are systems that support decision makers through data analysis, calculation or simulation. Typically, modeling languages for humans and machine are different from each other. While humans prefer graphical or textual models, machine-interpretable models have to be represented in a formal language. This chapter describes an approach to modeling that is both cognitively adequate for humans and processable by machines. In addition, the approach supports the creation and adaptation of domain-specific modeling languages. A metamodel which is represented as a formal ontology determines the semantics of the modeling language. To create a graphical modeling language, a graphical notation can be added for each class of the ontology. Every time a new modeling element is created during modeling, an instance for the corresponding class is created in the ontology. Thus, models for humans and machines are based on the same internal representation.

2018, Hinkelmann, Knut

Enterprise knowledge is currently subject to ever-changing, complex and domain-specific modeling requirements. Assimilating these requirements in modeling languages brings the benefits associated to both domain-specific modeling languages (DSMLs) and a baseline of well-established concepts. However, there are two problems that hamper the speed and efficiency of this activity: (1) the separation between the two key expertise: language engineering and domain knowledge, and (2) the sequential modeling language engineering life-cycles. In this work, we tackle these two challenges by introducing an Agile and Ontology-Aided approach implemented in our Modeling Environment - the AOAME. The approach seamlessly integrates meta-modeling and modeling in the same modeling environment, thus cooperation between language engineers and domain experts is fostered. Sequential engineering phases are avoided as the adaptation of the language is done on-the-fly. To this end, a modeling language is grounded with an ontology language providing a clear, unambiguous and machine-interpretable semantics. Mechanisms implemented in the AOAME ensure the propagation of changes from the modeling environment to the graph-based database containing the ontology.

2018, Hinkelmann, Knut

The advent of digitalization exposes enterprises to an ongoing transformation with the challenge to quickly capture relevant aspects of changes. This brings the demand to create or adapt domain-specific modeling languages (DSMLs) efficiently and in a timely manner, which, on the contrary, is a complex and time-consuming engineering task. This is not just due to the required high expertise in both knowledge engineering and targeted domain. It is also due to the sequential approach that still characterizes the accommodation of new requirements in modeling language engineering. In this paper we present a DSML adaptation approach where agility is fostered by merging engineering phases in a single modeling environment. This is supported by ontology concepts, which are tightly coupled with DSML constructs. Hence, a modeling environment is being developed that enables a modeling language to be adapted on-the-fly. An initial set of operators is presented for the rapid and efficient adaptation of both syntax and semantics of modeling languages. The approach allows modeling languages to be quickly released for usage.

2016-11-02, Hinkelmann, Knut

2018, Hinkelmann, Knut

Cloud computing offers a great opportunity for business process (BP) flexibility, adaptability and reduced costs. This leads to realising the notion of business process as a service (BPaaS), i.e., BPs offered on-demand in the cloud. This paper introduces a novel architecture focusing on BPaaS design that includes the integration of existing state-of-the-art components as well as new ones which take the form of a business and a syntactic matchmaker. The end result is an environment enabling to transform domain-specific BPs into executable workflows which can then be made deployable in the cloud so as to become real BPaaSes.

2016, Hinkelmann, Knut