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Role of IoT, AI, and Big Data Analytics in Healthcare Industry
Published in Pushpa Singh, Divya Mishra, Kirti Seth, Transformation in Healthcare with Emerging Technologies, 2022
P. Sriramalakshmi, Srimathnath Thejasvi Vondivillu, A. Sri Krishna Govind
This is the most basic and most used category of the cloud computing service.52,61 A license of the software is sold as a service in this type of system.60 This can also be referred to as applications-as-a-service (SaaS). Here, the actual hardware and operating system (OS) are relevant, as the consumer will use the service via the application or web browser, and it is often bought on “per user” basis. In addition, SaaS can run the application in a web browser and not require installation of the software, updating or maintaining the software, allow access to the data from anywhere in the world, and help in mobilizing the workforce effectively.62 SaaS remains the dominant cloud computing system, dropping in global use only slightly from 66% in 2017 to 60% in 2021.52 Examples of this system used are Microsoft’s Office 365 and Google docs. In the healthcare industry, in the clinical information system, it can be used in picture archiving and communication system (PACS), electronic health record (EHR), and telehealth; in nonclinical information systems, it can be used in billing, revenue cycle management (RCM), and supply chain.63,64,65,66,67
Integrating Cloud with IoT-Cloud IoT
Published in Monika Mangla, Ashok Kumar, Vaishali Mehta, Megha Bhushan, Sachi Nandan Mohanty, Real-Life Applications of the Internet of Things, 2022
Sakshi Kapoor, Surya Narayan Panda
SaaS (Software as a Service): A software distribution architecture where provider helps in providing various applications over a network mostly the Internet. It is on-demand software and based on pay-per-usage. The operation and installation of application software in the cloud will be done by cloud providers and the clients of the cloud will be responsible for providing the software to the users. There is no need for installing and running the software application on the user’s computer as the user does not require to handle the platform and infrastructure of the cloud where the application executes [1, 9, 11]. SaaS services are highly scalable which provide their customers with choices to access more or fewer services-on-demand. There is no need for purchasing new software; Customers can depend on SaaS providers for automatic updates. SaaS services are easily accessible for any user having an internet-enabled device and location. Google Docs where documents can be obtained by using a browser and can be shared on any number of computers via the Internet and Salesforce. com are some prominent examples. Due to the limited control over security, SaaS users face many security issues [12, 23]. Distributed computing operates an administration driven model, which we call the cloud business model. Hardware as well as platform-level resources are served on-demand basis. Each layer of the can be accomplished as a utility to the layer above. This suggests each layer goes about as a client of the layer underneath [24].
Artificial Intelligence and Public Policy
Published in Frank M. Groom, Stephan S. Jones, Artificial Intelligence and Machine Learning for Business for Non-Engineers, 2019
Impacts of technology are often met with challenges. Accenture has identified potential obstacles in the adoption of AI and Machine Learning technologies (Accenture, 2019c): Cost. Current offerings from vendors are often expensive, even for proof of concept initiatives.Security of data. The requirement to share personal data and potentially sensitive data with third parties and cloud providers can cause hesitation in some organizations.Customization and ease of use. Current software-as-a-service (SAAS) offerings are not customizable to meet specific requirements that may be requested by an organization.Vendor lock-in and future-proofing. AI is a rapidly changing technology. At the present time, choosing a vendor can be a challenging process, and industry and technological changes can take place at a rate that is faster than implementation.
Determinants of Software as a Service (SaaS) Adoption
Published in Journal of Computer Information Systems, 2023
Fatemeh Shapouri, Kerry Ward, Tenace Setor
Although enterprise SaaS applications hold great potential benefits for firms, they have their drawbacks as well. Information security and privacy concerns, a low degree of customization, and issues regarding stable access to services are among the main drawbacks that prevent firms from adopting SaaS applications.5–8 As an example, a survey conducted in 2022 reported that 95% of organizations are moderately to extremely concerned about security problems and challenges associated with cloud.9 Examples of security problems and challenges that those organizations are worried about are data breaches, account hijacking, unauthorized access, insecure interfaces, and misconfiguration.9–11 Because of its significant potential upsides and downsides, a wide variation exists with regard to SaaS adoption across firms.12 So, the relevant question to be asked is what factors affect firms’ decisions to adopt enterprise SaaS solutions?
An efficient cloud prognostic approach for aircraft engines fleet trending
Published in International Journal of Computers and Applications, 2020
Zohra Bouzidi, Labib Sadek Terrissa, Noureddine Zerhouni, Soheyb Ayad
SaaS (Software as a Service): is a model of software deployment where an application is hosted as a service provided to customers across the internet. Gmail, Hotmail, SalesForce.com and Microsoft Office Online are some of the well-known SaaS products and providers [19,20].PaaS (Platform as a Service): This refers to software and product development tools (e.g. application servers, database servers, portal servers, middleware, etc.) which clients lease so they can build and deploy their own applications for their specific use. Google App Engine and Windows Azure are examples of PaaS products and providers [19,20].IaaS (Infrastructure as a Service): is essentially hardware devices, e.g. visualized servers, storage, network devices, etc. It generally refers to a virtualization environment where services enable the Cloud platforms and applications to connect and operate. Amazon Elastic Cloud Compute (EC2), VMWare are some of the IaaS products and providers [19,20].
Big Earth data: disruptive changes in Earth observation data management and analysis?
Published in International Journal of Digital Earth, 2020
Martin Sudmanns, Dirk Tiede, Stefan Lang, Helena Bergstedt, Georg Trost, Hannah Augustin, Andrea Baraldi, Thomas Blaschke
The backbone of big Earth data analytics is some type of distributed computing platform (e.g. clouds). Cloud computing services are broadly categorised into four types: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS) and Data as a Service (DaaS) (Yang et al. 2011). IaaS provides virtualised computing resources over the internet that can be used for storage, backup or big data analytics, with a pay-as-you-go structure. PaaS provides the hardware and software tools over the internet needed for applications development, testing and delivering. Microsoft Azure and Google App Engine are some of the most popular PaaS. SaaS is often referred to as ‘on-demand-software’, where cloud providers deliver software applications over the Internet, usually on a subscription basis. Common examples of SaaS include the ArcGIS Online (ESRI) cloud implementation, Microsoft Office or Google Gmail and apps. DaaS provides access to data discovery, access, and utilisation, including the software needed to interpret the data (Olson 2009; Yang et al. 2011).