Semantics in Industrial Distributed Systems

Obitko, Marek; Vrba, Pavel; Mařı́k, Vladimı́r; Radakovič, Miloslav

doi:10.3182/20080706-5-kr-1001.02350

Cited by 13 publications

(3 citation statements)

References 10 publications

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“…Manufacturing ontology is addressed in several domains in recent years (Obitko et al , 2008). Manufacturing's Semantics Ontology (MASON) (Lemaignan et al , 2006) is a general‐purpose manufacturing ontology describing manufacturing operations, National Institute of Standards and Technology (NIST's) shop data model (McLean et al , 2005), ontology for automation objects merging mechatronic and control models (Lopez and Martinez Lastra, 2006), Open, Object‐Oriented Knowledge Economy for Intelligent Industrial Automation (OOONEIDA) (Vyatkin et al , 2005) for automation component infrastructure.…”

Section: Context‐sensitive Computing and Ontologiesmentioning

confidence: 99%

Ontology‐based context‐sensitive computing for FMS optimization

Uddin

Puttonen

Scholze

et al. 2012

Assembly Automation

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2014),"Maximizing agreement on diverse ontologies with "wisdom of crowds" relation classification", Online Information Review, Vol. 38 Iss 5 pp. 616-633 http://dx.If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to present an ontology-based approach of context-sensitive computing for the optimization of flexible manufacturing systems (FMS). Design/methodology/approach -A context-sensitive computing approach is presented, integrated on top of FMS control platform. The approach addresses how to extract manufacturing contexts at source, how to process contextual entities by developing an ontology-based context model and how to utilize this approach for real time decision making to optimize the key performance indicators (KPIs). A framework for such an optimization support system is proposed. A practical FMS use case within SOA-based control architecture is considered as an illustrative example and the implementation of the core functionalities to the use case is reported. Findings -Continuous improvement of the factory can be enhanced utilizing context-sensitive support applications, which provides an intelligent interface for knowledge acquisition and elicitation. This can be used for improved data analysis and diagnostics, real time feedback control and support for optimization.Research limitations/implications -The performance of context-sensitive computing increases with the extraction, modeling and reasoning of as much contexts as possible. However, more computational resources and processing times are associated to this. Hence, the trade-off should be in between the extent of context processing and the required outcome of the support applications. Practical implications -This paper includes the practical implications of context-sensitive applications development in manufacturing, especially in the dynamic operating environment of FMS. Originality/value -Reported results provide a modular approach of context-sensitive computing and a practical use case implementation to achieve context awareness in FMS. The results are seen extendable to other manufacturing domains.

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Section: Context‐sensitive Computing and Ontologiesmentioning

confidence: 99%

Ontology‐based context‐sensitive computing for FMS optimization

Uddin

Puttonen

Scholze

et al. 2012

Assembly Automation

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“…For this purpose, two languages, Resource Description Format (RDF) and Web Ontology Language (OWL), are proposed. These technologies were primarily designed for the World Wide Web; nevertheless, they are suitable for applications running outside the Web as well, including industrial domain (Obitko et al, 2008).…”

Section: Semantic Webmentioning

confidence: 99%

The Impacts of Semantic Technologies on Industrial Systems

Mařı́k

Vrba²,

Obitko³

2009

Handbook of Research on Social Dimensions of Semantic Technologies and Web Services

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Industrial systems face the challenges of robust and flexible control of industrial processes while satisfying the demand of mass customization and reduced time to market. To meet these requirements, systems need to work in a distributed manner, because the traditional centralized approaches are not sufficient. To achieve automated cooperation and coordination of distributed components at a larger scale, semantic technologies are necessary to enable truly open systems. The authors review state of the art of the research of ontologies, Semantic Web and Semantic Web services, together with advances of usage of semantic technologies in industry. The usage of semantic technologies is illustrated on two applications – semantics in multi-agent manufacturing systems and structural search in industrial data.

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“…In the paper at hand, we are focusing on the potential of semantic technologies to reduce complexity and heterogeneity in the industrial systems domain [9]. Here, it is common sense that large numbers of heterogeneous systems (e.g.…”

Section: Introductionmentioning

confidence: 99%

RDFreduce

Lettner

Pichler

Kirchmayr

et al. 2013

Proceedings of International Conference on Information Integration and Web-Based Applications &Amp; Services

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Industrial manufacturing environments are typically supported by a backbone network of technical control-and business information systems. To carry out daily work routines like maintaining technical processes or managing business information like sales and orders, different user groups as, e.g., IT management or engineers, need different forms of integrated and aggregated views on the information stored in and flowing through these systems. In this paper, we are building on recent advances in the fields of Information Integration, Semantic Web and Linked Data and report on an approach called RDFreduce to meet aforementioned needs. RDFreduce allows for the creation of custom aggregated views on heterogeneous RDF datasets. The aggregates are equipped with provenance information, in order to be tracked back easily to the datasets they origin from. An industrial use case of RDFreduce stemming from steel production especially aiming at integrating and aggregating information flows is presented.

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Semantics in Industrial Distributed Systems

Cited by 13 publications

References 10 publications

Ontology‐based context‐sensitive computing for FMS optimization

Ontology‐based context‐sensitive computing for FMS optimization

The Impacts of Semantic Technologies on Industrial Systems

RDFreduce

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