A conceptual framework for large-scale ecosystem interoperability and industrial product lifecycles

Selway, Matt; Stumptner, Markus; Mayer, Wolfgang; Jordan, Andreas; Großmann, Georg; Schrefl, Michael

doi:10.1016/j.datak.2017.03.006

Cited by 21 publications

(13 citation statements)

References 32 publications

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“…On the other hand, several enterprise interoperability frameworks have been proposed in the literature (Chen et al, 2008 ), such as LISI (Levels of Information Systems Interoperability) (C4ISR, 1998 ), IDEAS interoperability framework (IDEAS, 2002 ), ATHENA interoperability framework (AIF) (ATHENA, 2003 ), Framework for Enterprise Interoperability (FEI), Big Data Value (BDV) Reference Model (BDVA, 2017 ), National Institute of Standards and Technology (NIST), and Big Data Interoperability Framework (NIST, 2019 ). In addition, during the last years, new domain-specific interoperability frameworks have been proposed, such as the European Interoperability Framework (EIF) (EIF, 2017 ), the Internet of Things-based interoperability framework for fleet management (Backman et al, 2016 ), the Smart City Interoperability Framework (Ahn et al, 2016 ), the interoperability framework for software as service systems in cloud (Rezaei et al, 2014 ), the International Image Interoperability Framework (Snydman et al, 2015 ), and the conceptual interoperability framework for large-scale systems (Selway et al, 2017 ). Overall, the enterprise interoperability frameworks can be seen in the frame of three main layers (Romero and Vernadat, 2016 ; Leal et al, 2020 ; Technical, Semantic, and Organizational).…”

Section: Literature Reviewmentioning

confidence: 99%

Enterprise Integration and Interoperability for Big Data-Driven Processes in the Frame of Industry 4.0

Bousdekis

Mentzas

2021

Front. Big Data

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Traditional manufacturing businesses lack the standards, skills, processes, and technologies to meet today's challenges of Industry 4.0 driven by an interconnected world. Enterprise Integration and Interoperability can ensure efficient communication among various services driven by big data. However, the data management challenges affect not only the technical implementation of software solutions but the function of the whole organization. In this paper, we bring together Enterprise Integration and Interoperability, Big Data Processing, and Industry 4.0 in order to identify synergies that have the potential to enable the so-called “Fourth Industrial Revolution.” On this basis, we propose an architectural framework for designing and modeling Industry 4.0 solutions for big data-driven manufacturing operations. We demonstrate the applicability of the proposed framework through its instantiation to predictive maintenance, a manufacturing function that increasingly concerns manufacturers due to the high costs, safety issues, and complexity of its application.

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Section: Literature Reviewmentioning

confidence: 99%

Enterprise Integration and Interoperability for Big Data-Driven Processes in the Frame of Industry 4.0

Bousdekis

Mentzas

2021

Front. Big Data

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“…Modelverse also uses the concept of undefined potency, which is similar to star potency in the sense that it represents uncertainty. SLICER is another multi-level modeling framework that is aimed at modeling large ecosystems, mainly focusing on scalability [56]. SLICER is different from other approaches as it has no explicit levels, -therefore, it can be considered to be level-blind -and is not potency-based.…”

Section: Multi-level Approachesmentioning

confidence: 99%

“…Name Description Nature characterization potency [32] an iteration on classic potency, focusing on abstractness-related problems approach classic potency [7] the original potency notion approach classic potency with edges [5] extended support for handling edges approach cross-level relations [14] level-adjuvant, not based on potency approach Cuadrado and de Lara [14] provides adaptation and extensibility for multi-level tools approach DeepJava [34] Java with multi-level programming tool DeepRuby [49] implementation of dual deep instantiation by extending the Ruby language tool DeepTelos [30] knowledge representation system employing multi-level modeling tool DPF Workbench [35] based on the DPF tool dual deep instantiation [48] / modeling [50] uses dual potency on feature endpoints approach endurance properties [36] assigns potency to field values approach FMML x [21] modeling language using intrinsic features that also support operations approach formalisation by Rossini et al [55] multi-level modeling based on the DPF approach leap potency [39] aims to remedy identity instantiation approach Melanee [2] supports star potency and endurance properties tool MetaDepth [38] supports leap potency and star potency tool ML2 [20] provides a text-based language for conceptual multi-level modeling based on MLT tool Modelverse (AToMPM) [46] model repository that supports classic potency-based multi-level modeling tool MultEcore [41] potency-based tool based on EMF tool Nivel [1] uses a modified version of the classic potency notion tool non-potency elements [28] an iteration on classic potency, removing certain elements from the potency notion approach OMLM [28] potency-based framework that uses non-potency elements tool order [32,33] represents "the maximum number of classification relationships between an element and its most remote instances" approach SLICER [56] level-blind; scalability is the main focus tool star (*) potency [24] introduces uncertainty in the model approach TOTEM [29] focuses on closing the gap betw...…”

Section: A Summary Of Discussed Multi-level Approachesmentioning

confidence: 99%

Playground for multi-level modeling constructs

et al. 2021

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In recent years, multi-level modeling has become more and more popular. It is mainly due to the fact that multi-level modeling aims to reduce or even totally eliminate any accidental complexity inadvertently created as by-product in traditional model design. Moreover, besides reducing model complexity, multi-level modeling also improves on general comprehension of models. The key enablers of multi-level modeling are the concepts of clabjects and deep instantiation. The latter is often governed by the potency notion, of which many different interpretations and variations emerged over the years. However, there exist also some approaches that disregard the potency notion. Thus, multi-level modeling approaches tend to take advantage of different theoretical and practical backgrounds. In this paper, we propose a unifying framework, the Multi-Level Modeling Playground (MLMP), which is a validating modeling environment for multi-level modeling research. The MLMP environment is based on our multi-layer modeling framework (the Dynamic Multi-Layer Algebra), which provides useful mechanisms to validate different multi-level modeling constructs. Since beyond the structure also the well-formedness rules of the modeling constructs can be specified, our proposed MLMP environment delivers several practical benefits: i) well-formedness is always verified, ii) multi-level constructs can be experimented with independently of any concrete tool chains, and iii) relationships (i.e., correlations or exclusions) between different multi-level constructs can be easily investigated in practice. Also, the capability of the environment is demonstrated via complete examples inspired by state-of-the-art research literature.

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“…However, they are domain specific such as the Internet of Things based interoperability framework (Backman et al, 2016) for fleet management, the Smart City Interoperability Framework (Ahn et al, 2016), the interoperability framework for software as service systems in cloud (Rezaei et al, 2014a), the International Image Interoperability Framework (Snydman et al, 2015) and the conceptual interoperability framework for large-scale systems (Selway et al, 2017).…”

Section: Framework For Enterprise Interoperabilitymentioning

confidence: 99%

Enterprise interoperability assessment: a requirements engineering approach

Leal

Guédria

Panetto

2020

International Journal of Computer Integrated Manufacturing

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One of the main challenges within collaborative business ecosystems is the management of Interoperability. Indeed, Interoperability is a crucial prerequisite that must be satisfied when enterprises need to work together for seizing new business opportunities and improving competitiveness. To develop and improve enterprise systems interoperability, a set of interoperability requirements needs to be verified. Indeed, knowing the different requirements and their relationships are paramount for identifying potential impacts on the overall system. Many research work have been proposed in the literature for defining and analysing such requirements. However, existing work do not explicitly specify the interdependencies between interoperability requirements. The objective of this article is, therefore, to investigate and define the interoperability requirements and their interdependencies. This will provide a holistic and clear view of the relations between the system components and their associated requirements. Therefore, one will be able to identify potential causes of the non-fulfilment of requirements, and their impacts on the concerned system. To do so, a Requirements Engineering approach is used to identify and formalise interoperability requirements and their relationships.

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A conceptual framework for large-scale ecosystem interoperability and industrial product lifecycles

Cited by 21 publications

References 32 publications

Enterprise Integration and Interoperability for Big Data-Driven Processes in the Frame of Industry 4.0

Enterprise Integration and Interoperability for Big Data-Driven Processes in the Frame of Industry 4.0

Playground for multi-level modeling constructs

Enterprise interoperability assessment: a requirements engineering approach

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