Audun Vennesland

Empirical Evaluation of the Quality of Conceptual Models Based on User Perceptions: A Case Study in the Transport Domain

Today, many companies design and maintain a vast amount of conceptual models. It has been also observed that such large model collections exhibit serious quality issues in industry practice. A number of quality frameworks have been proposed in the literature, but the practice is that practitioners continue to evaluate conceptual models in an ad-hoc and subjective way, based on common sense and experience. Therefore, there is a lack of empirical works in the evaluation of conceptual frameworks. This paper reports an empirical qualitative study on the evaluation of the quality of a conceptual framework in the domain of transport logistics, using existent quality evaluation frameworks. The results show how the users perceive the ease of understanding, the usefulness, the perceived semantic quality and satisfaction with the models included in the conceptual framework. The results also provided their view on advantages, challenges and improvements to be performed in the framework.

Semantic data containers for realizing the full potential of system wide information management

In order to unleash the full potential of System Wide Information Management (SWIM), the BEST project (Achieving the Benefits of SWIM by Making Smart Use of Semantic Technologies) proposes the semantic container approach which shields service and application developers from the complexities of data provisioning in Air Traffic Management (ATM). In combination with SWIM, semantic containers facilitate the emergence of a marketplace of value-added information services, and allow for complex derivation chains of data sets. Along these derivation chains, existing data are intelligently filtered and prioritized as well as combined and annotated with additional information.

Towards Scalability Guidelines for Semantic Data Container Management

Semantic container management is a promising approach to organize data. However, the scalability of this approach is challenging. By scalability in this paper, we mean the expressivity and size of the semantic data containers we can handle, given a suitable quality threshold. In this paper, we derive scalability characteristics of the semantic container approach in a structured way. We also describe actual experiments where we vary the number of available CPU cores and quality thresholds. We conclude this work-in-progress paper by describing how more measurements could be performed so that the missing guidelines could be provided.