Semantics for Cyber-Physical Systems: A cross-domain perspective

Sabou, Marta; Biffl, Stefan; Einfalt, Alfred; Krammer, Lukas; Kästner, Wolfgang; Ekaputra, Fajar J.

doi:10.3233/sw-190381

Cited by 20 publications

(6 citation statements)

References 43 publications

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“…Moreover, the linked nature of semantic web technologies can bring us further to smart factories, smart buildings, and smart grids, 177 as has already been demonstrated by the application of the World Avatar in smart city planning, 178 and the UK Digital Twin 157 (https://kg.cmclinnovations.com/ explore/digital-twin). By constructing a digital laboratory and linking it to the wider context, we believe it will facilitate multiscale and cross-domain interactions between scientists, engineers, and policy makers to investigate how research done in the lab would affect the whole world.…”

Section: Toward a Digital Laboratory And Beyondmentioning

confidence: 99%

From Platform to Knowledge Graph: Evolution of Laboratory Automation

Bai

Cao

Mosbach

et al. 2022

JACS Au

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High-fidelity computer-aided experimentation is becoming more accessible with the development of computing power and artificial intelligence tools. The advancement of experimental hardware also empowers researchers to reach a level of accuracy that was not possible in the past. Marching toward the next generation of self-driving laboratories, the orchestration of both resources lies at the focal point of autonomous discovery in chemical science. To achieve such a goal, algorithmically accessible data representations and standardized communication protocols are indispensable. In this perspective, we recategorize the recently introduced approach based on Materials Acceleration Platforms into five functional components and discuss recent case studies that focus on the data representation and exchange scheme between different components. Emerging technologies for interoperable data representation and multi-agent systems are also discussed with their recent applications in chemical automation. We hypothesize that knowledge graph technology, orchestrating semantic web technologies and multi-agent systems, will be the driving force to bring data to knowledge, evolving our way of automating the laboratory.

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Section: Toward a Digital Laboratory And Beyondmentioning

confidence: 99%

From Platform to Knowledge Graph: Evolution of Laboratory Automation

Bai

Cao

Mosbach

et al. 2022

JACS Au

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“…The three accepted papers also deal with three popular and important application domains of the WoT [59], Smart Agriculture, Smart Building and the Industry of the Future. 13 The paper on Weather Data Publication on the LOD using SOSA/SSN Ontology 14 by Catherine Roussey, Bernard Stephan, André Géraldine, and Boffety Daniel on Smart Agriculture is a typical application domain of WoT architectures, where semantically annotated weather/climate data [2,43] and the monitoring of cultivated fields requires various sensors that push streaming data [63] that must be collected and reasoned upon to take decisions executed by actuators.…”

Section: Overview Of the Special Issuementioning

confidence: 99%

Semantics in the Edge: Sensors and actuators in the Web of Linked Data and Things

Cena

Haller

Lefrançois

2020

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“…enter or leave the CES's context at runtime [Schlingloff et al 2016]. As CESs are usually embedded in a network of CESs (i.e., a CSG), an individual CES must be able to process and communicate complex information from/to changing communication partners during its life cycle in order to provide its functionality.…”

Section: Fig 6-2: Crest Process Building Block (Pbb) Notationmentioning

confidence: 99%

“…Ontologies provide a suitable technology for formalizing different aspects of a CES and could potentially be the provider of data, information, and knowledge for different software functionalities [Sabou et al 2019]. In the case of a CES in the manufacturing domain, an ontology can be used to formalize manufacturing-related capabilities (see Sections 6.3.2 and 6.4.2), features relevant for reconfiguration of a manufacturing system (see Section 6.3.3), or serve as input information for a simulation of the manufacturing system (see Section 6.4.1).…”

Section: Ontology Buildingmentioning

confidence: 99%

Modeling and Analyzing Context-Sensitive Changes during Runtime

Wehrstedt

Brings

Caesar

et al. 2020

Model-Based Engineering of Collaborative Embedded Systems

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For collaborative embedded systems, it is essential to consider not only the behavior of each system and the interaction between systems, but also the interaction of systems with their often dynamic and unknown context.In this chapter, we present a solution approach based on process building blocks— describing both the modelling approach as well as the model execution approach—for engineering and operation to achieve the goal of developing systems that deal with dynamics in their open context at runtime by re-using the models from the engineering phase.

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Semantics for Cyber-Physical Systems: A cross-domain perspective

Cited by 20 publications

References 43 publications

From Platform to Knowledge Graph: Evolution of Laboratory Automation

From Platform to Knowledge Graph: Evolution of Laboratory Automation

Semantics in the Edge: Sensors and actuators in the Web of Linked Data and Things

Modeling and Analyzing Context-Sensitive Changes during Runtime

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