Research on cloud manufacturing service discovery based on latent semantic preference about OWL-S

Jiao, Hua; Zhang, Jing; Li, Jun Huai; Shi, Jinfa

doi:10.1080/0951192x.2014.1002812

Cited by 12 publications

(7 citation statements)

References 2 publications

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“…OWL-S describes Web services in three aspects: (1) service profile, (2) service model, and (3) service grounding [14,15]. Ontology can improve the accuracy of simulation model search by describing simulation models based on semantics [16]. In order to support the composite modeling of complex system simulation applications, some experts have carried out research on simulation service description methods based on semantics and have proposed description ontologies of simulation model resources (e.g., OWL-SS [17] and OWL-SM [18]).…”

Section: Owl-based Simulation Model Search Methodsmentioning

confidence: 99%

Service-Oriented Model Encapsulation and Selection Method for Complex System Simulation Based on Cloud Architecture

Xiong

Zhu

Yao

et al. 2019

Entropy

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With the rise in cloud computing architecture, the development of service-oriented simulation models has gradually become a prominent topic in the field of complex system simulation. In order to support the distributed sharing of the simulation models with large computational requirements and to select the optimal service model to construct complex system simulation applications, this paper proposes a service-oriented model encapsulation and selection method. This method encapsulates models into shared simulation services, supports the distributed scheduling of model services in the network, and designs a semantic search framework which can support users in searching models according to model correlation. An optimization selection algorithm based on quality of service (QoS) is proposed to support users in customizing the weights of QoS indices and obtaining the ordered candidate model set by weighted comparison. The experimental results showed that the parallel operation of service models can effectively improve the execution efficiency of complex system simulation applications, and the performance was increased by 19.76% compared with that of scatter distribution strategy. The QoS weighted model selection method based on semantic search can support the effective search and selection of simulation models in the cloud environment according to the user’s preferences.

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Section: Owl-based Simulation Model Search Methodsmentioning

confidence: 99%

Service-Oriented Model Encapsulation and Selection Method for Complex System Simulation Based on Cloud Architecture

Xiong

Zhu

Yao

et al. 2019

Entropy

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“…There are multiple ways in which semantic web services can facilitate matching service providers to consumers. One popular method is to use semantic similarity [1,21,23,[25][26][27][28], while another is intelligent matching [24,[29][30]. In semantic similarity, a similarity score [28], degree [28], or relationship [23] is calculated between semantic concepts [25].…”

Section: Semantic Web Servicesmentioning

confidence: 99%

“…One popular method is to use semantic similarity [1,21,23,[25][26][27][28], while another is intelligent matching [24,[29][30]. In semantic similarity, a similarity score [28], degree [28], or relationship [23] is calculated between semantic concepts [25]. The similarity between a semantic description of a task and service [21] is assigned a value to denote their relative conceptual relationship.…”

Section: Semantic Web Servicesmentioning

confidence: 99%

Mapping Part Characteristics to Machining Capabilities using a Matching Algorithm.

Peck

Finke²

2022

Preprint

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The service composition problem is a major obstacle that cloud manufacturing platforms must overcome in order to match service providers to consumers such that the requirements of the consumer are met by the service provider. Previous research focuses on supplier selection, quality of service, and semantic web service approaches as a solution to the service composition problem, but rely on subjective aggregations of high-level concepts that provide a distorted perception of the service providers capabilities. Our matching algorithm methodology uses detailed information regarding operation, part, and tooling specific data to create a granular and realistic representation of the service provider. The matching algorithm is validated using a real-world part drawn from the literature and manufacturers in central Pennsylvania. Our findings indicate that the matching algorithm scales linearly as the number of inputs for a parameter increases. The research contribution of our matching algorithm is significant as its use of detailed machine specific information is novel. Additionally, its inclusion in a cloud manufacturing platform will decrease machine idleness, while providing the customer with an on-demand service that will aid downstream production and design.

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“…The model uses semantic links to realize automatic integration and distributed updating of resource service cloud. Jiao et al (2017) proposed a service discovery method and service description based on ontology web service semantics due to a lack of semantic models in current service discovery. Khalfallah et al (2016) presented and utilized the ontology model based on public standards to transform private information of enterprises into data integration standards for the aviation industry, which were shown to effectively increase the interoperability of product development in the aviation industry.…”

Section: Semantic Integration and Interaction Of Hem Information Servmentioning

confidence: 99%

Engineering management for high-end equipment intelligent manufacturing

Yang¹,

Wang²,

Shi³

et al. 2018

Front. Eng

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The high-end equipment intelligent manufacturing (HEIM) industry is of strategic importance to national and economic security. Engineering management (EM) for HEIM is a complex, innovative process that integrates natural science, technology, management science, social science, and the human spirit. Newgeneration information technology (IT), including the internet, cloud computing, big data, and artificial intelligence, have made a remarkable influence on HEIM and its engineering management activities, such as product system construction, product life cycle management, manufacturing resources organization, manufacturing model innovation, and reconstruction of the enterprise ecosystem. Engineering management for HEIM is a key topic at the frontier of international academic research. This study systematically reviews the current research on issues pertaining to engineering management for HEIM under the new-generation IT environment. These issues include cross-lifecycle management, network collaboration management, task integration management of innovative development, operation optimization of smart factories, quality and reliability management, information management, and intelligent decision making. The challenges presented by these issues and potential research opportunities are also summarized and discussed.

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Research on cloud manufacturing service discovery based on latent semantic preference about OWL-S

Cited by 12 publications

References 2 publications

Service-Oriented Model Encapsulation and Selection Method for Complex System Simulation Based on Cloud Architecture

Service-Oriented Model Encapsulation and Selection Method for Complex System Simulation Based on Cloud Architecture

Mapping Part Characteristics to Machining Capabilities using a Matching Algorithm.

Engineering management for high-end equipment intelligent manufacturing

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