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.

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 Federated Learning in Big Data Application and Sharing

2020 ◽  
Author(s):  
Jing Yang ◽  
Quan Zhang ◽  
Kunpeng Liu ◽  
Peng Jin ◽  
Guoyi Zhao
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
The State ◽  
Ideal Model ◽  
Local Data ◽  
Data Safety ◽  
Big Data Applications ◽  
Data Application ◽  
Big Data Application ◽  
Uniform Model

In recent years, electricity big data has extensive applications in the grid companies across the provinces. However, certain problems are encountered including, the inability to generate an ideal model using the isolated data possessed by each company, and the priority concerns for data privacy and safety during big data application and sharing. In this pursuit, the present research envisaged the application of federated learning to protect the local data, and to build a uniform model for different companies affiliated to the State Grid. Federated learning can serve as an essential means for realizing the grid-wide promotion of the achievements of big data applications, while ensuring the data safety.

Opportunities and Challenges of Big Data in Healthcare

2020 ◽  
pp. 1989-2001
Author(s):  
Wafaa Faisal Mukhtar ◽  
Eltayeb Salih Abuelyaman
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
Ethical Issues ◽  
Standard Of Care ◽  
Healthcare Sector ◽  
Useful Knowledge ◽  
Healthcare Data ◽  
Big Data Streams ◽  
Privacy Issues ◽  
Collection Methods

Healthcare big data streams from multiple information sources at an alarming volume, velocity, and variety. The challenge that faces the healthcare industry is extracting meaningful value from such sources. This chapter investigates the diversity and forms of data in the healthcare sector, reviews the methods used to search and analyze these data throughout the past years, and the use of machine learning and data mining techniques to mine useful knowledge from such data. The chapter will also highlight innovations of particular systems and tools which spot the fine approaches for different healthcare data, raise the standard of care and recap the tools and data collection methods. The authors emphasize some of ethical issues regarding processing these records and some data privacy issues.

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.

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