Cloud Computing and Semantic Web Technologies

2016 ◽  
pp. 55-70
Keyword(s):  
Cloud Computing ◽  
Semantic Web ◽  
Semantic Web Technologies ◽  
Web Technologies

How Industry 4.0 Can Benefit From Semantic Web Technologies and Artefacts

2021 ◽  
Vol 13 (4) ◽  
pp. 64-74
Author(s):  
Leila Zemmouchi-Ghomari
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Semantic Web ◽  
Industry 4.0 ◽  
The Internet ◽  
Time Data ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Real Time Data ◽  
The Internet Of Things

Industry 4.0 is a technology-driven manufacturing process that heavily relies on technologies, such as the internet of things (IoT), cloud computing, web services, and big real-time data. Industry 4.0 has significant potential if the challenges currently being faced by introducing these technologies are effectively addressed. Some of these challenges consist of deficiencies in terms of interoperability and standardization. Semantic Web technologies can provide useful solutions for several problems in this new industrial era, such as systems integration and consistency checks of data processing and equipment assemblies and connections. This paper discusses what contribution the Semantic Web can make to Industry 4.0.

Open Taiwan Government data recommendation platform using DBpedia and Semantic Web based on cloud computing

2019 ◽  
Vol 15 (2) ◽  
pp. 236-254
Author(s):  
I-Cheng Chen ◽  
I-Ching Hsu
Keyword(s):  
Cloud Computing ◽  
Semantic Web ◽  
Cluster Model ◽  
Data Sets ◽  
Query Interface ◽  
Semantic Web Technologies ◽  
Cloud Computing Environment ◽  
Content Type ◽  
Web Technologies ◽  
Government Data

Purpose In recent years, governments around the world are actively promoting the Open Government Data (OGD) to facilitate reusing open data and developing information applications. Currently, there are more than 35,000 data sets available on the Taiwan OGD website. However, the existing Taiwan OGD website only provides keyword queries and lacks a friendly query interface. This study aims to address these issues by defining a DBpedia cloud computing framework (DCCF) for integrating DBpedia with Semantic Web technologies into Spark cluster cloud computing environment. Design/methodology/approach The proposed DCCF is used to develop a Taiwan OGD recommendation platform (TOGDRP) that provides a friendly query interface to automatically filter out the relevant data sets and visualize relationships between these data sets. Findings To demonstrate the feasibility of TOGDRP, the experimental results illustrate the efficiency of the different cloud computing models, including Hadoop YARN cluster model, Spark standalone cluster model and Spark YARN cluster model. Originality/value The novel solution proposed in this study is a hybrid approach for integrating Semantic Web technologies into Hadoop and Spark cloud computing environment to provide OGD data sets recommendation.

Cloud computing and semantic web technologies for ubiquitous management of smart cities-related competences

2020 ◽  
Author(s):  
Omiros Iatrellis ◽  
Theodor Panagiotakopoulos ◽  
Vassilis C. Gerogiannis ◽  
Panos Fitsilis ◽  
Achilles Kameas
Keyword(s):  
Cloud Computing ◽  
Semantic Web ◽  
Smart Cities ◽  
Semantic Web Technologies ◽  
Web Technologies

Cloud Computing and Semantic Web Technologies

2017 ◽  
pp. 51-66
Author(s):  
Bhavani Thuraisingham ◽  
Mohammad Mehedy Masud ◽  
Pallabi Parveen ◽  
Latifur Khan
Keyword(s):  
Cloud Computing ◽  
Semantic Web ◽  
Semantic Web Technologies ◽  
Web Technologies

Resource Allocation for Cloud Computing

2011 ◽  
pp. 90-112 ◽  
Author(s):  
Jorge Ejarque ◽  
Javier Álvarez ◽  
Raül Sirvent ◽  
Rosa M. Badia
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Semantic Web ◽  
Heterogeneous Computing ◽  
New Paradigm ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Computing Paradigm ◽  
Multi Agent ◽  
Semantically Enhanced

Cloud computing has emerged as a distributed computing paradigm where resources are requested on demand and in a very dynamic fashion and paying only for what you consume. This new paradigm created an ecosystem where several providers offer heterogeneous computing resources to satisfy the customers’ computing demand. So, the allocation and adaptation of this demand to the correct resources is a key issue in this ecosystem, because it can produce a mutual benefit for the customers and providers. However, with the wide variety of customers and providers, this allocation is not an easy task. This chapter presents a toolkit that implements a methodology for improving the resource allocation between different Cloud providers. The Semantically Enhanced Resource Allocator (SERA) toolkit introduces the semantic web and multi-agent technologies for facilitating the interoperability between the users and different resource providers. Semantic web technologies provide the required semantic interoperability between the different providers’ vocabularies; meanwhile a platform of configurable agents provides an adaptable and autonomous way of allocating and managing execution requests and resources according to the customers and providers rules.

Semantic Web Technologies for e-Learning: Models and Implementation

Informatica ◽  
2015 ◽  
Vol 26 (2) ◽  
pp. 221-240 ◽  
Author(s):  
Valentina Dagienė ◽  
Daina Gudonienė ◽  
Renata Burbaitė
Keyword(s):  
Semantic Web ◽  
Learning Models ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
E Learning
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