Keywords: Linked Data, Electronic Marketplaces, Semantic web
Astract: Electronic marketplaces are built to resemble real marketplaces structurally. Consequently, they are centralized systems, walled gardens with an intrinsic tendency to lock merchants and clients in. We argue that this structure is not necessary on the Web and that all online marketplaces could merge into one global medium for exchange. In this paper, we propose an architecture for such a medium based on semantic Web standards, encompassing the functionalities of publishing an intention to buy or sell, finding transaction partners, and conducting transactions. We focus on the basic protocol layer and explain how messaging and linked data are combined in a novel way to realize a highly dynamic communication system.

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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: Model-driven software engineering has gained momentum in academia as well as in industry for improving the development of evolving software by providing appropriate abstraction mechanisms in terms of software models and transformations thereof. With the rise of cyber-physical systems in general, and cyber-physical production systems in particular, the interplay between several engineering disciplines, such as software engineering, mechanical engineering and electrical engineering, becomes a must. Thus, a shift from pure software models to system models has to take place to develop the full potential of model-driven engineering for the whole production domain. System Models are also essential to raise the level of flexibility of production systems even further in order to better react to changing requirements, since systems are no longer designed to be, but they have to be designed to evolve. In this talk, we will present ongoing work of applying and further developing model-driven techniques, such as consistency management and co-evolution support, for the production domain.

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Astract: Model-driven software engineering has gained momentum in academia as well as in industry for improving the development of evolving software by providing appropriate abstraction mechanisms in terms of software models and transformations thereof. With the rise of cyber-physical systems in general, and cyber-physical production systems in particular, the interplay between several engineering disciplines, such as software engineering, mechanical engineering and electrical engineering, becomes a must. Thus, a shift from pure software models to cross-disciplinary models has to take place to develop the full potential of model-driven engineering for the whole production domain. Cross-disciplinary models are also essential to raise the level of flexibility of production systems in order to better react to changing requirements, since systems are no longer designed to be, but they have to be designed to evolve. In this talk, we will have a look at current practice of good, bad, and ugly cross-disciplinary modeling. We will point to ongoing work of (hopefully) improving this situation by applying and further developing model-driven techniques such as consistency management and co-evolution support for the production domain.

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Astract: Model-driven software engineering has gained momentum in academia as well as in industry for improving the development of evolving software by providing appropriate abstraction mechanisms in terms of software models and transformations thereof. With the rise of cyber-physical systems in general, and cyber-physical production systems in particular, the interplay between several engineering disciplines, such as software engineering, mechanical engineering and electrical engineering, becomes a must. Thus, a shift from pure software models to cross-disciplinary models has to take place to develop the full potential of model-driven engineering for the whole production domain. Cross-disciplinary models are also essential to raise the level of flexibility of production systems in order to better react to changing requirements, since systems are no longer designed to be, but they have to be designed to evolve. In this talk, we will have a look at current practice of cross-disciplinary modeling with special emphasis on good, bad, and ugly habits. We will point to ongoing work of (hopefully) improving this situation by applying and further developing model-driven techniques such as consistency management and co-evolution support for the production domain.

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Astract: Model-driven software engineering has gained momentum in academia as well as in industry for improving the development of evolving software by providing appropriate abstraction mechanisms in terms of software models and transformations thereof. With the rise of cyber-physical systems in general, and cyber-physical production systems in particular, the interplay between several engineering disciplines, such as software engineering, mechanical engineering and electrical engineering, becomes a must. Thus, a shift from pure software models to cross-disciplinary models has to take place to develop the full potential of model-driven engineering for the whole production domain. Cross-disciplinary models are also essential to raise the level of flexibility of production systems in order to better react to changing requirements, since systems are no longer designed to be, but they have to be designed to evolve. In this talk, we will have a look at current practice of cross-disciplinary modeling with special emphasis on good, bad, and ugly habits. We will point to ongoing work of (hopefully) improving this situation by applying and further developing model-driven techniques such as consistency management and co-evolution support for the production domain.

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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.

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Astract: Companies are increasingly embedded in B2B environments, where they have to collaborate in order to achieve their goals. Such collaborations lead to inter-organizational business processes that may be commonly supported through the exchange of electronic data interchange (EDI) messages (e.g., electronic purchase orders, invoices etc.). Despite the appearance of XML, traditional approaches to EDI, such as EDIFACT and ANSI X.12, still play an overwhelmingly dominant role. However, such traditional EDI standards lack a notion of process. In other words, the exchanged business documents are typically not embedded in the context of other exchanged business documents. This has two shortcomings: (1) the inability to apply proven business process management (BPM) methods, including process mining techniques, in such settings; and (2) the unavailability of systematic approaches to business intelligence (BI) using information from exchanged EDI messages. In this article, we present the EDImine Framework for enabling (1) the application of process mining techniques in the field of EDI-supported inter-organizational business processes, and (2) for supporting inter-organizational performance evaluation using business information from EDI messages, event logs and process models. As an enabling technology, we present a method for the semantic preprocessing of EDIFACT messages to exploit this potentially rich source of information by applying state of the art BPM and BI techniques. We show the applicability of our approach by means of a case study based on real-world EDI data of a German consumer goods manufacturing company.