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Astract: In Anlehnung an die Umsetzungsempfehlung des deutschen Arbeitskreises zu Industrie 4.0 widmen wir uns in diesem Kapitel dem offenen Handlungsfeld der Standardisierung und Referenzarchitektur im Kontext einer modellbasierten horizontalen und vertikalen Integration. Wir zeigen, dass die Zusammenführung international etablierter Standards genutzt werden kann, um eine flexible Informationsarchitektur zu schaffen. Zu diesem Zweck präsentieren wir ein offenes, dreidimensionales Rahmenwerk von Standards für Industrie 4.0. Die erste Dimension berücksichtigt die Unterscheidung zwischen den unterschiedlichen Ebenen in einem Unternehmen, in Anlehnung an die klassische Automatisierungspyramide. Die zweite Dimension unterscheidet zwischen den internen und den externen Aspekten der horizontalen und vertikalen Integration. Die dritte Dimension differenziert zwischen der wirtschaftlichen Tätigkeit der teilneh- menden Unternehmen im Wertschöpfungsnetzwerk und den technischen Aspekten des nahtlosen Daten- und Informationsaustausches.

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Astract: When linked data applications communicate, they commonly use messaging technologies in which the message exchange itself is not represented as linked data, since it takes place on a different architectural level. When a message cannot be verified and traced on the linked data level, trust in data is moved from message originators to service providers. However, there are use cases in which the actual message exchange and its verifiability are of importance. In such situations, the separation between application data and communication data is not desirable. To address this, we propose messaging based on linked data, where communicating entities and their messages are represented as interconnected Web resources, and we show how conversations can be made verifiable using digital signatures.

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Astract: Inter-organizational systems form the basis for successful business collaboration in the Internet and B2B e-commerce era. To properly design and manage such systems one needs to understand the structure and dynamics of the relationships between organizations. The evaluation of such inter-organizational relationships (IORs) is normally conducted using "success factors". These are often referred to as constructs, such as trust and information sharing. In strategic management and performance analysis, different methods are employed for evaluating business performance and strategies, such as the Balanced Scorecard (BSC) method. The BSC utilizes success factors for measuring and monitoring IORs against business strategies. For these reasons, a thorough understanding of success factors, the relationships between them, as well as their relationship to business strategies is required. In other words, understanding success factors allows strategists deriving measurements for success factors as well as aligning these success factors with business strategies. This underpins nowadays close relationship between business strategy, IORs and their realization by means of inter-organizational systems. In this paper, we present (1) a systematic literature review studying success factors and their impact on IORs as well as (2) an analysis of the results found. The review is based on 177 publications, published between 2000 and 2012, dealing with factors influencing IORs. The work presented provides an overview on success factors, influencing relationships between success factors, as well as their influence on the success of IORs. The work is somehow "meta-empirical" as it only looks at published studies and not on own cases. Consequently, it is based on the assumption that studies in scientific literature represent the real-world. The constructs and relationships found in the review are grouped based on their scope and summarized in a cause and effect model. The grouping of constructs results in five groups including Relationship Orientation, Relational Norm, Relational Capital, Atmosphere, and Others. Since the cause and effect model represents a directed graph, different network analysis methods may be applied for analyzing the model. In particular, an in- and out-degree analysis is applied on the cause and effect model for detecting the most influencing as well as the most influenced success factors.