Semantic Web Service Architecture for Simulation Model Reuse

Sun, Wei; Chen, Yu; Défago, Xavier; Zhang, Yuanyuan; Inoguchi, Yasushi

doi:10.1109/ds-rt.2007.38

Cited by 19 publications

(6 citation statements)

References 25 publications

(36 reference statements)

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“…Some efforts were made to create standard ontologies for use in simulation modeling [6]. Ontologies may be used for bridging various models: for example, one model may provide source data for another, exposing its interface as a service having ontological description [1]. OWL, the core standard for semantic knowledge representation, offers the requisite level of expressiveness to capture the rich relationships, and can be easily extended [12].…”

Section: Related Workmentioning

confidence: 99%

Building Ontologies for Agent-Based Simulation

Gorshkov¹

2015

Advances in Swarm and Computational Intelligence

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Abstract. Using ontologies for simulation models construction has some advantages that cannot be underestimated. Building the ontology, a modeler has to choose conceptualization method, which significantly affects the structure and usability of resulting models. A tendency of using standard ontologies without critically estimating their applicability for particular tasks may even lead to the loss of the model's efficiency and reliability. In this work, we are considering a simple criterion which may be used to pragmatically assess applicability of particular modeling techniques for building ontologies for simulation models. We will specially focus on the temporal aspect, states and events representation methods in the model. A fragment of ontology for the city social infrastructure optimization modeling will be considered. Keywords: Ontologies modeling · Ontology assessment · Collaborative modeling IntroductionSemantic Web technologies became attractive for the implementers of agent-based simulation methods since they have emerged. Semantic Web-oriented software is providing a convenient way for digital representation of conceptual models. The further development of this idea leads to the representation of the simulation model itself in the ontological form, and building a model execution framework around the semantic data storage [11]. This approach has many advantages, among which are:─ Simplification of the information gathering from various sources. A semantic model can be used for merge heterogeneous data into a single representation. ─ Ease of the model management during its development and exploration. Adding new object types, attributes and interaction rules into semantic model is simple. It is much more flexible that the relational data oriented model. ─ The ontologies are very convenient for expressing agent behavior and interaction rules in simulation models -especially the logical behavioral patterns. ─ The reasoning software can automate rules execution during the simulation.It is natural that the use of rather complex and flexible modeling methods and information representations is producing some uneasy problems. To ensure that the use of semantic technologies will benefit modeling process and result, it is necessary to adequately construct the target model ontology, and implement it. The term

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Section: Related Workmentioning

confidence: 99%

Building Ontologies for Agent-Based Simulation

Gorshkov¹

2015

Advances in Swarm and Computational Intelligence

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“…A service life-cycle can be defined as the succession of strategies used by service management as a service goes through its service life cycle [20]. The service life-cycle goes through many phases and requires many skills, tools and processes with respect to business, technology and organizational structure.…”

Section: Service Life-cyclementioning

confidence: 99%

A generic service system for knowledge-intensive service firms

Peng

Badr

Biennier

2009

Proceedings of the International Conference on Management of Emergent Digital EcoSystems

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Service systems are highly complex and often difficult to study, design and implement given their socio-technical-organisational nature. This complexity leads to a poor understanding as to how service systems can be innovative and competitive. Within the service sector, Knowledge-Intensive Service firms compete by the skills and knowledge of their employees to deliver online business services. However, current service systems for KnowledgeIntensive Service firms are either too static to describe the dynamic mechanism, or too visionary to use in real world applications, which makes activities such as designing, developing, and evolving adaptive service applications unpractical. In this paper, we present a generic service system architecture describing service characteristics with descriptors such as customer-centric, knowledge-driven, reusability, safeguards, interoperability, and Internet delivery channels. Finally, we propose an implementation framework to achieve secure and adaptive service delivery and a service life cycle describing the service production process.

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“…For example, the core HLA capabilities (IEEE 2000) can be extended with SOA concepts (e.g., (Chen et al 2006)) or web services used for distributed simulation (e.g., (Hu et al 2007;Mittal et al 2007)). Web services are also proposed to define an ontology with a corresponding software infrastructure for simulation model reuse (Bell et al 2007). …”

Section: Related Workmentioning

confidence: 99%

A simulation framework for service-oriented computing systems

Sarjoughian¹,

Kim²,

Ramaswamy³

et al. 2008

2008 Winter Simulation Conference

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An SOA-compliant DEVS (SOAD) simulation framework is proposed for modeling service-oriented computing systems. A set of novel abstract component models that conform to the SOA principles and are grounded in the DEVS formalism is developed. The approach supports construction of hierarchical composition of service models with feedback relationships. A SOAD Simulator (SOADS) is designed and implemented. An exemplar model of a basic serviceoriented computing system is described. A representative experiment capturing throughput and timeliness QoS attributes for the exemplar model is devised, simulated, and described. The paper concludes with the concept of community-based development of the SOAD framework and tools.

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Semantic Web Service Architecture for Simulation Model Reuse

Abstract: COTS simulation packages (CSPs)

Cited by 19 publications

References 25 publications

Building Ontologies for Agent-Based Simulation

Building Ontologies for Agent-Based Simulation

A generic service system for knowledge-intensive service firms

A simulation framework for service-oriented computing systems

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