Lukasz Lis

Pattern Specification and Matching in Conceptual Models - A Generic Approach Based on Set Operations

Searching for patterns in conceptual models is useful for a number of purposes, for example revealing syntactical errors, model comparison, and identification of business process improvement potentials. In this contribution, we introduce a formal approach for the specification and matching of structural patterns in conceptual models. Unlike existing approaches, we do not focus on a certain application problem or a specific modelling language. Instead, our approach is generic making it applicable for any pattern matching purpose and most conceptual modelling languages. In order to build sets representing structural model patterns, we define formal operations based on set theory, which can be applied to arbitrary models represented by sets. The basic sets represent the model elements, which in turn originate from the modelling language specification’s instances. Besides a conceptual and formal specification of our approach, we present particular application examples and a prototypical modelling tool showing its general applicability.

Supporting Distributed Conceptual Modelling through Naming Conventions - A Tool-based Linguistic Approach

Empirical studies attest that conceptual models created in distributed modelling environments often vary heavily in the way their respective model elements are labelled. Although the same issues are being modelled, different names are chosen by the involved persons. By this, the analysis and comparison of the models, which is required for their subsequent integration, is extremely challenging and time consuming. Literature analysis reveals several approaches addressing this problem by either manually or semi-automatically integrating existing models after their construction. However, this proves to be an exhaustive and error prone task. In this article we propose a domain and modelling language independent approach that prevents the emergence of naming conflicts already during the modelling process. This is done by formalising naming conventions consisting of context specific thesauri and customised phrase structures, which are both derived from natural language grammars and supplemented by domain-specific terms. These conventions serve as basis for a fully automated guidance of the modeller during the model creation process, resulting in semantically comparable conceptual models. For this, we present a research prototype that integrates our approach into a modelling tool.

Dynamic Knowledge Mapping: A Visualization Approach for Knowledge Management Systems

Visualizing the contents of knowledge management systems allows for better comprehensibility of the knowledge base, gives a condensed overview, and provides entry points to particular knowledge items. In this context, two different visualization approaches have been proposed in the literature. On the one hand, algorithm-based approaches have been introduced, which are capable of automated generation of reproducible visualizations. On the other hand, visualizations such as knowledge maps have to be created individually by domain experts. In this paper, we propose a combined approach of dynamic knowledge mapping, which is based on a manual creation of the underlying context (e.g., the metaphor) of the visualization. The particular knowledge items, however, are mapped onto this background automatically in response to changes in the knowledge base. Thus, our approach benefits from the use of a challenging graphical context while allowing for an automated generation of visualizations.