scholarly journals Cloud Computing in Singapore: Key Drivers and Recommendations for a Smart Nation

2018 ◽  
Vol 6 (4) ◽  
pp. 39-47 ◽  
Author(s):  
Reuben Ng
Keyword(s):  
Public Policy ◽  
Cloud Computing ◽  
Big Data ◽  
Business Models ◽  
Fog Computing ◽  
Novel Technologies ◽  
Cloud Computing Adoption ◽  
Big Data Applications ◽  
Key Drivers ◽  
The Government

Cloud computing adoption enables big data applications in governance and policy. Singapore’s adoption of cloud computing is propelled by five key drivers: (1) public demand for and satisfaction with e-government services; (2) focus on whole-of-government policies and practices; (3) restructuring of technology agencies to integrate strategy and implementation; (4) building the Smart Nation Platform; (5) purpose-driven cloud applications especially in healthcare. This commentary also provides recommendations to propel big data applications in public policy and management: (a) technologically, embrace cloud analytics, and explore “fog computing”—an emerging technology that enables on-site data sense-making before transmission to the cloud; (b) promote regulatory sandboxes to experiment with policies that proactively manage novel technologies and business models that may radically change society; (c) on the collaboration front, establish unconventional partnerships to co-innovate on challenges like the skills-gap—an example is the unprecedented partnership led by the Lee Kuan Yew School of Public Policy with the government, private sector and unions.

scholarly journals IoT Based Smart Garage Vehicle Washing Water Nestor

2020 ◽  
Vol 8 (5) ◽  
pp. 3521-3525
Keyword(s):  
Water Pollution ◽  
Cloud Computing ◽  
Big Data ◽  
Smart Cities ◽  
Human Life ◽  
Research Paper ◽  
Proper Treatment ◽  
Computing Services ◽  
Cloud Computing Services ◽  
The Government

Water is critical part of the human life. In most of the developing nation, water pollution is one of the bigger mess. These issues can be handled strictly by the Government organization, by implementing tougher action rules to the industries, were the water are released without any proper treatment. Where each industries (or) smart cities, should take up self-initiative responsibility for proper treatment of the polluted out flow water. In our research paper, we are not focusing on the wider area of the water pollution; our focus is limited within the smart cities vehicle washing garages. In very smart cities, were a regular multiple vehicles washing is done in the garage, our research paper will focus on the out flow of the populated water from these vehicle washing garages. Our design and implantation process is simpler and straightforward approach. Were we will monitor of the water quality; and how much level of the water is populated, and it requires at what level of the treatment. These process can be easily automated using the multiple IOT (internet of things) based sensors, the data can be streamed into the Big Data lake (or) it can be directly pushed into the cloud computing services for generating the real time graphs and analyses report instantly. These data collected in the Big Data lake (or) cloud computing services, can be used for detail analyses for research purpose. We will incorporate the block chain concept to keep track of the smart garage location address and the detail information of the number of garage in the smart cities details in the form of the blocks.

Big Data Tools for Computing on Clouds and Grids

2018 ◽  
pp. 152-174
Author(s):  
Forest Jay Handford
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Cost Effective ◽  
The Other ◽  
Relative Cost ◽  
Scientific Communities ◽  
Academic Communities ◽  
Historic Data ◽  
The Government

The number of tools available for Big Data processing have grown exponentially as cloud providers have introduced solutions for businesses that have little or no money for capital expenditures. The chapter starts by discussing historic data tools and the evolution to those of today. With Cloud Computing, the need for upfront costs has been removed, costs are continuing to fall and costs can be negotiated. This chapter reviews the current types of Big Data tools, and how they evolved. To give readers an idea of costs, the chapter shows example costs (in today's market) for a sampling of the tools and relative cost comparisons of the other tools like the Grid tools used by the government, scientific communities and academic communities. Readers will take away from this chapter an understanding of what tools work best for several scenarios and how to select cost effective tools (even tools that are unknown today).

Architecture for Big Data Storage in Different Cloud Deployment Models

2021 ◽  
pp. 178-208
Author(s):  
Chandu Thota ◽  
Gunasekaran Manogaran ◽  
Daphne Lopez ◽  
Revathi Sundarasekar
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Storage ◽  
High Performance ◽  
Data Services ◽  
Big Data Applications ◽  
Nosql Database ◽  
Amazon Web Services ◽  
Product Domains ◽  
Scalable Database

Cloud Computing is a new computing model that distributes the computation on a resource pool. The need for a scalable database capable of expanding to accommodate growth has increased with the growing data in web world. More familiar Cloud Computing vendors such as Amazon Web Services, Microsoft, Google, IBM and Rackspace offer cloud based Hadoop and NoSQL database platforms to process Big Data applications. Variety of services are available that run on top of cloud platforms freeing users from the need to deploy their own systems. Nowadays, integrating Big Data and various cloud deployment models is major concern for Internet companies especially software and data services vendors that are just getting started themselves. This chapter proposes an efficient architecture for integration with comprehensive capabilities including real time and bulk data movement, bi-directional replication, metadata management, high performance transformation, data services and data quality for customer and product domains.

scholarly journals The Importance of Business Model Factors for Cloud Computing Adoption: Role of Previous Experiences

Organizacija ◽  
2017 ◽  
Vol 50 (3) ◽  
pp. 255-272 ◽  
Author(s):  
Kristina Bogataj Habjan ◽  
Andreja Pucihar
Keyword(s):  
Cloud Computing ◽  
Business Model ◽  
Business Models ◽  
Service Providers ◽  
Organizational Factors ◽  
Research Results ◽  
Computing Services ◽  
Cloud Computing Adoption ◽  
Cloud Computing Services ◽  
The Impact

Abstract Background and Purpose: Bringing several opportunities for more effective and efficient IT governance and service exploitation, cloud computing is expected to impact the European and global economies significantly. Market data show that despite many advantages and promised benefits the adoption of cloud computing is not as fast and widespread as foreseen. This situation shows the need for further exploration of the potentials of cloud computing and its implementation on the market. The purpose of this research was to identify individual business model factors with the highest impact on cloud computing adoption. In addition, the aim was to identify the differences in opinion regarding the importance of business model factors on cloud computing adoption according to companies’ previous experiences with cloud computing services. Methodology: Based on literature review, prior research results, and interviews with cloud computing providers and users, a research model was developed. Statistical analysis focused on identification of factors’ importance on cloud computing adoption and differences in opinions according to respondents’ previous experiences with cloud computing services. The study was done among 80 companies and five major cloud computing providers in Slovenia. Results: The research results reveal statistically significant differences in opinions on the importance of cloud computing business model factors according to respondents’ previous experiences with cloud computing services. The results can provide orientation for redesign or innovation of existing business models towards the creation of a customer-oriented business model for the more successful exploitation of cloud computing services and business opportunities. For potential users, the findings represent guidelines for the successful adoption of cloud computing services. Conclusions: In our research, the investigated business model factors could be classified into so-called “business model organizational factors”, as they primarily need to be considered by cloud service providers when defining or innovating their business models. For future research, the model should also include the impact of environmental factors, such as Competition, Business Partners, Legislation, Economic Situation, in order to investigate their impact on cloud adoption.

Big-data applications in the government sector

2014 ◽  
Vol 57 (3) ◽  
pp. 78-85 ◽  
Author(s):  
Gang-Hoon Kim ◽  
Silvana Trimi ◽  
Ji-Hyong Chung
Keyword(s):  
Big Data ◽  
Government Sector ◽  
Big Data Applications ◽  
The Government

scholarly journals The Impacts of Cloud Computing and Big Data Applications on Developing World-Based Smallholder Farmers

2015 ◽  
Vol 3 (2) ◽  
pp. 16-23
Author(s):  
Nir Kshetri
Keyword(s):  
Cloud Computing ◽  
Developing Countries ◽  
Big Data ◽  
Data Privacy ◽  
Developing World ◽  
Ethical Issues ◽  
Smallholder Farmers ◽  
Agricultural Productivity ◽  
International Telecommunication Union ◽  
Big Data Applications

Cloud computing and big data applications are likely to have far-reaching and profound impacts on developing world-based smallholder farmers. Especially, the use of mobile devices to access cloudbased applications is a promising approach to deliver value to smallholder farmers in developing countries since according to the International Telecommunication Union, mobile-cellular penetration in developing countries is expected to reach 90% by the end of 2014. This article examines the contexts, mechanisms, processes and consequences associated with cloud computing and big data deployments in farming activities that could affect the lives of developing world-based smallholder farmers. We analyze the roles of big data and cloud-based applications in facilitating input availability, providing access to resources, enhancing farming processes and productivity and improving market access, marketability of products and bargaining power for smallholders. In the developing world’s context, an even bigger question than that of whether agricultural productivity can be improved by using cloud computing and big data is who is likely to benefit from the growth in productivity. The paper analyzes the conditions under which at agricultural productivity associated with the utilization cloud computing and big data applications in developing countries may benefit smallholder farmers. Also investigated in the paper are important privacy and ethical issues involved around cloud computing and big data. While some analysts view that people in developing countries do not need privacy, the paper challenged this view and points out that data privacy and security issues are even more important to smallholder farmers in developing countries.

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