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Today’s industrial production plants are complex mechatronic systems. In the course of the production plant lifecycle, engineers from a variety of disciplines (e.g., mechanics, electronics, automation) need to collaborate in multi-disciplinary settings that are characterized by heterogeneity in terminology, methods, and tools. This collaboration yields a variety of engineering artifacts that need to be linked and integrated, which on the technical level is reflected in the need to integrate heterogeneous data. Semantic Web technologies, in particular ontology-based data integration (OBDI), are promising to tackle this challenge that has attracted strong interest from the engineering research community. This interest has resulted in a growing body of literature that is dispersed across the Semantic Web and Automation System Engineering research communities and has not been systematically reviewed so far. We address this gap with a survey reflecting on OBDI applications in the context of Multi-Disciplinary Engineering Environment (MDEE). To this end, we analyze and compare 23 OBDI applications from both the Semantic Web and the Automation System Engineering research communities. Based on this analysis, we (i) categorize OBDI variants used in MDEE, (ii) identify key problem context characteristics, (iii) compare strengths and limitations of OBDI variants as a function of problem context, and (iv) provide recommendation guidelines for the selection of OBDI variants and technologies for OBDI in MDEE. ispartof: Open Journal of Information Systems vol:4 issue:1 pages:1-26 status: published

Decision-making is a complex and important task in software engineering. The current state-of-the-practice is rather non-systematic as it typically relies upon personal judgment and experience without using explicit models. Empirical studies can help but they are costly to conduct and, to some extent, context dependent. Typically it is not efficient or even possible to conduct empirical studies for a large number of context parameter variations. Process simulation offers decision support as well, but currently suffers from a lack of empirical knowledge on the determinants of underlying system dynamics. In this paper we present an assessment of empirical knowledge and simulation techniques for the area of quality assurance planning. There is a strong interdependency between process simulation and empirical models for decision-making in this area: (a) profound empirical knowledge enables process simulation to support decision-making, and (b) the analysis of simulation results can point out situations and factors for which conducting empirical studies would be most worthwhile. This paper discusses critically some of the most important challenges for decision-making in the area of quality assurance planning.