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An Enablement Platform for an Internet of Things Application: A Business Model
Published in Gulshan Shrivastava, Sheng-Lung Peng, Himani Bansal, Kavita Sharma, Meenakshi Sharma, New Age Analytics, 2020
Internet-based IoT application integrates cloud to provide a seamless hardware platform that allows data storage and analytics for different kind of embedded application systems. As an instance, Microsoft Azure offers a global data center networks to provide Internet access i.e., for business IoT platform. IoT exponentially enhances the storage of data that is simply characterized in the form of volume, variety, and velocity to refer as ‘big data (BD).’ This technology is used to discover the concept of data mining i.e., for the discovery of customer establishment, business process, macroeconomics, and socio-trends. The important technological development and standards are adopted to create a de-facto standard that establishes the industrial IoT standard known as GS1-EPC Global Architecture (GS1, 2015). This standard outlines the context of SCM that is emerged with the fundamentals of IoT technologies such as International Telecommunication Union (ITU), International Organization and Standardization (ISO), International Electro-Technical Commission (IEC) and Institute of Electrical and Electronics Engineer (IEEE) (Li, Xu, and Zhao, 2015).
Cloud Computing, Data Sources and Data Centers
Published in Diego Galar Pascual, Pasquale Daponte, Uday Kumar, Handbook of Industry 4.0 and SMART Systems, 2019
Diego Galar Pascual, Pasquale Daponte, Uday Kumar
There are many IaaS vendors offering a range of products and services: AWS offers storage services such as Simple Storage Services (S3) and Glacier, as well as compute services, including its Elastic Compute Cloud (EC2).GCP offers storage and compute services through Google Compute Engine.Microsoft Azure also offers storage and compute services.Smaller or niche players in the IaaS marketplace include Rackspace Managed Cloud, CenturyLink Cloud, DigitalOcean and more.
Mobile Cloud Computing
Published in Jithesh Sathyan, Anoop Narayanan, Navin Narayan, K V Shibu, A Comprehensive Guide to Enterprise Mobility, 2016
Jithesh Sathyan, Anoop Narayanan, Navin Narayan, K V Shibu
There are service providers who offer platform-as-a-service (PaaS). It allows users to create custom cloud applications using supplier-specific tools and services at lower costs. These applications can be deployed in the service provider's infrastructure either for private or for public usage. An example is the Oracle platform for SaaS, which provides Oracle Database and Application Grid Middleware. Another example is Google AppEngine. It allows users to create and maintain web applications on Google's infrastructure. Microsoft Azure allows users to develop and maintain applications in the cloud. www.Force.com from SalesForce provides tools to build and host applications for free.
The challenges of using live-streamed data in a predictive digital twin
Published in Journal of Building Performance Simulation, 2023
Rebecca Ward, Ruchi Choudary, Melanie Jans Singh, Flora Roumpani, Tomas Lazauskas, May Yong, Nicholas Barlow, Markus Hauru
The accessed data need to be stored in a secure repository, able to accept incoming data streams from different sources and accessible from the digital twin platform. The best storage solution is likely to be dependent on the individual context of the digital twin. Cloud-based platforms – e.g. Microsoft Azure – have the benefits of secure access, back-up, copious storage capacity and access to cloud computing services, but at a cost. Other solutions would be to use a stand-alone server on-site but this would also require maintenance and back-up – again with associated costs.
Programming models and systems for Big Data analysis
Published in International Journal of Parallel, Emergent and Distributed Systems, 2019
Loris Belcastro, Fabrizio Marozzo, Domenico Talia
Microsoft Azure Machine Learning11 (Azure ML) is a SaaS that provides a Web-based development environment for creating and sharing machine learning workflows as DAGs. Through its user-friendly interface, data scientists and developers can perform several common data analysis and mining tasks and automate their workflows, without needing to buy any hardware/software nor manage virtual machine manually.