Efficient Integration of Complex Information Systems in the ATM Domain with Explicit Expert Knowledge Models

Abstract

Abstract. The capability to provide a platform for flexible business services in the Air Traffic Management (ATM) domain is both a major success factor for the ATM industry and a challenge to integrate a large number of complex and heterogeneous information systems. Most of the system knowledge needed for integration is not available explicitly in machine-understandable form, resulting in timeconsuming and error-prone human integration tasks. In this chapter we introduce and evaluate a knowledge-based approach, "Semantically-Enabled Externalization of Knowledge" for the ATM domain (SEEK-ATM), which explicitly models a) expert knowledge on specific heterogeneous systems and integration requirements; and b) allows mapping of the specific knowledge to the general ATM problem domain knowledge for semantic integration. The domain-specific modeling enables a) to verify the integration knowledge base as requirements specification for later design of technical systems integration and b) to provide an application program interface (API) to the problem space knowledge to facilitate tool support for efficient and effective systems integration. Based on an industry case study, we evaluate effects of the proposed SEEK-ATM approach in comparison to traditional system integration approaches in the ATM domain. Major advantages of the novel approach are the efficient derivation of technical configurations and automated quality assurance of the expert knowledge models.

Improving Quality Assurance in Multidisciplinary Engineering Environments with Semantic Technologies

“…While the semantic integration with SWTs already supports defect detection activities, SWTs can be employed to create defect detection mechanisms, in particular, through semantic querying with SPARQL. Benefits of these approaches are: (a) defect detection tasks are explicitly represented in SPARQL queries, thus allowing periodic query repetition; (b) since queries typically address the ontology concepts, the checks remain valid even if the underlying data model, for example, of signal concepts, changes; and (c) semantic query languages rely on reasoning mechanisms and are, therefore, well fitted [38] to check model consistency and coherency.…”