The engineering process of Cyber-Physical Production systems (CPPS) involves collaboration of multiple engineering disciplines. Major obstacles arising from these multi-disciplinary engineering processes are heterogen...
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The engineering process of Cyber-Physical Production systems (CPPS) involves collaboration of multiple engineering disciplines. Major obstacles arising from these multi-disciplinary engineering processes are heterogeneous representations, weak accumulation and integration of dispersed, local engineering knowledge, and required effective coordination between multi-disciplinary engineering teams across the organization. Further, heterogeneous communication channels lead to increased information sharing effort for individual team members, ill-structured knowledge representation and management, and poor discoverability of business-critical know-how. These challenges can be addressed by Collective Intelligence systems (CIS) that enhance engineering methods and tools in large, multi-disciplinary projects. CIS help to identify important implicit, hard-to-access dispersed information and engineering knowledge, make it explicit, and promote the awareness and efficient management of this business-critical knowledge. Therefore, this paper presents a research agenda focusing on the systematic and empirically-grounded investigation of needs, basic concepts, principles, and models of CIS software architectures in particular application domains, and outlines expected results.
The Web of Needs (WoN) is an approach for expressing and publishing human needs on the Internet as linked data to allow automatic matching of the expressed needs and communication between users who expressed them. In ...
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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...
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Many platforms or marketplaces on the web offer services that describe supply in certain domains. Users have to search actively and often multiple times in different closed platforms to find what they want. This appro...
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Today, most knowledge-based companies organize work in projects. Due to different reasons knowledge gained in projects is not documented and shared in an appropriate manner resulting in the problem that critical knowl...
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Today, most knowledge-based companies organize work in projects. Due to different reasons knowledge gained in projects is not documented and shared in an appropriate manner resulting in the problem that critical knowledge is lost. We propose case-based storage of relevant knowledge and an appropriate reasoning about this knowledge to support organizations in new projects. In order to achieve this objective, we have analyzed which kind of critical knowledge is gained in projects or other way round, which missing knowledge results in the failing of projects. A case is a pre-defined knowledge structure filled out by potentially different stakeholders of the project. After the first planning of a new project, if the project characteristics are entered, case-based reasoning is used to find similar old projects and derive from them additional attributes and to make potential risks visible. This is useful to better estimate the effort of a project, the required human competences and the required communication between stakeholders. This knowledge transfer can support also other phases of a project such as scheduling activities. In a closing phase, individual problems solved in the project can be documented as a kind of lessons-learned attempt.
Current recruiting processes are largely supported by diverse online solutions. Online application systems, virtual interviewing, and search for employees through professional social networks such as LinkedIn and XING...
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Today, we recognize a discrepancy between design time models concentrating on the desired behavior of a system and its real world correspondents reflecting deviations taking place at runtime. In order to close this ga...
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Today, we recognize a discrepancy between design time models concentrating on the desired behavior of a system and its real world correspondents reflecting deviations taking place at runtime. In order to close this gap, design time models must not be static, but evolutionary artifacts so called liquid models. Such liquid models are the cornerstone of our future research project CDL-MINT: Model Integrated Smart Production. In this position paper, we present an early result of this project: the liquid models architecture for linking design models to runtime concerns, which are derived from distributed and heterogeneous systems during operation. We elaborate on the proposed technologies for the respective architecture layers and identify the research challenges ahead.
AutomationML is an emerging IEC standard for storing and exchanging engineering data among the heterogeneous software tools involved in the engineering of production systems. One important subset of such engineering d...
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AutomationML is an emerging IEC standard for storing and exchanging engineering data among the heterogeneous software tools involved in the engineering of production systems. One important subset of such engineering data is the plant behavior. To make this data exchangeable, AutomationML uses the existing industry data format PLCopen XML. However, at the development stages of production systems, the plant behavior is usually defined using other representation means, such as Gantt charts, impulse diagrams, and sequential function charts. To make such plant behavior models exchangeable, AutomationML introduces the so-called Intermediate Modeling Layer (IML) with corresponding transformation rules to decouple the employed modeling languages from the target format PLCopen XML. However, IML itself as well as the transformations from and to IML are only semi-formally described. This not only hinders the adoption of IML as a common language for representing plant behavior, but also renders impossible the composition of heterogeneous plant behavior models for carrying out integrated analyses of the global plant behavior. In this work, we aim at clarifying syntactical and semantical aspects of IML by proposing a metamodel and operational semantics for IML. This constitutes the first step towards formalizing and validating transformations between behavioral modeling languages currently employed in the production domain (e.g., Gantt charts), IML, and PLCopen XML. Having this foundation, we aim at utilizing IML as the semantic domain for the composition of heterogeneous plant behavior models.
Modelling complex system often results in different but overlapping modelling artifacts which evolve independently. Thus, inconsistencies may arise which lead to unintended effects on the modelled system. To mitigate ...
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Modelling complex system often results in different but overlapping modelling artifacts which evolve independently. Thus, inconsistencies may arise which lead to unintended effects on the modelled system. To mitigate this situation, model synchronization is seen as a recurring and crucial maintenance task which requires to restore consistency between multiple models using the most suitable changes. Currently, different languages and tools are used for inter-model consistency management than for intra-model consistency where UML/ OCL is an accepted solution. Consequently, the result of synchronizing models solely based on inter-model constraints might result into inappropriately evolved models w.r.t. intra-model constraints. In this paper, we present a synchronization model formalized in UML/OCL which covers explicit consistency and change models including costs and which considers both, inter-model and intra-model constraints at the same time. Instances of this synchronization model represent successful synchronization scenarios. In particular, models can be synchronized, also taking into account their predecessor versions, by finding a constraint violation-free extension of a partial model including those instances which may be optimized for minimal cost. We prototypically implemented this approach using a model finder to automatically retrieve synchronized models and the change operations to compute them by completing the partial model.
Interdisciplinary and assessment initiatives are two parallel educational paradigms that are being increasingly implemented in higher education institutions. Our study combines these two paradigms in order to assess t...
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Interdisciplinary and assessment initiatives are two parallel educational paradigms that are being increasingly implemented in higher education institutions. Our study combines these two paradigms in order to assess the significance and performance of factors and processes that facilitate interdisci-plinarity at the doctoral level. Using the 360-degree feedback methodology, we integrate the perspectives of different academic stakeholders in the assessment, namely students, post-doctoral researchers, professors, directors, visiting professors and research funding agencies. Therefore, this study not only provides a global assessment but also informative intermediate results, such as analyses on the alignment and discrepancies of stakeholders as well as the identification of priorities for improvement. This paper presents the development and implementation of this multiple-perspective assessment within an academic context and discusses the results of its application in a European faculty of computer science where several doctoral programs with different approaches to interdisciplinarity co-exist.
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