scholarly journals SOA-FOG: Secure service-oriented edge computing architecture for smart health big data analytics

2017 ◽  
Author(s):  
Rabindra K. Barik ◽  
Harishchandra Dubey ◽  
Kunal Mankodiya
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Edge Computing ◽  
Oriented Edge ◽  
Computing Architecture ◽  
Service Oriented ◽  
Smart Health

A Service-Oriented Foundation for Big Data

2020 ◽  
Vol 10 (1) ◽  
pp. 1-17
Author(s):  
Zhaohao Sun
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Business Analytics ◽  
Technological Characteristics ◽  
Service Oriented ◽  
Technological Level ◽  
Multi Level ◽  
Big Service

This paper provides a service-oriented foundation for big data. The foundation has two parts. Part 1 reveals 10 big characteristics of big data. Part 2 presents a service-oriented framework for big data. The framework has fundamental, technological, and socio-economic levels. The fundamental level has four big fundamental characteristics of big data: big volume, big velocity, big variety, and big veracity. The technological level consists of three big technological characteristics of big data: Big intelligence, big analytics, big infrastructure. The socioeconomic level has three big socioeconomic characteristics of big data: big service, big value, and big market. The article looks at each level of the proposed framework from a service-oriented perspective. The multi-level framework will help organizations and researchers understand how the 10 big characteristics relate to big opportunities, big challenges, and big impacts arising from big data. The proposed approach in this paper might facilitate the research and development of big data, big data analytics, business intelligence, and business analytics.

scholarly journals Decision Framework for Engaging Cloud-Based Big Data Analytics Vendors

2020 ◽  
Vol 22 (4) ◽  
pp. 60-74
Author(s):  
Emmanuel Wusuhon Yanibo Ayaburi ◽  
Michele Maasberg ◽  
Jaeung Lee
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Competitive Advantage ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Unique Contribution ◽  
Data Services ◽  
Business Environments ◽  
Digital Era ◽  
Service Oriented

Organizations face both opportunities and risks with big data analytics vendors, and the risks are now profound, as data has been likened to the oil of the digital era. The growing body of research at the nexus of big data analytics and cloud computing is examined from the economic perspective, based on agency theory (AT). A conceptual framework is developed for analyzing these opportunities and challenges regarding the use of big data analytics and cloud computing in e-business environments. This framework allows organizations to engage in contracts that target competitive parity with their service-oriented decision support system (SODSS) to achieve a competitive advantage related to their core business model. A unique contribution of this paper is its perspective on how to engage a vendor contractually to achieve this competitive advantage. The framework provides insights for a manager in selecting a vendor for cloud-based big data services.

Decision Framework for Engaging Cloud-Based Big Data Analytics Vendors

2022 ◽  
pp. 245-261
Author(s):  
Emmanuel Wusuhon Yanibo Ayaburi ◽  
Michele Maasberg ◽  
Jaeung Lee
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Competitive Advantage ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Unique Contribution ◽  
Data Services ◽  
Business Environments ◽  
Digital Era ◽  
Service Oriented

Organizations face both opportunities and risks with big data analytics vendors, and the risks are now profound, as data has been likened to the oil of the digital era. The growing body of research at the nexus of big data analytics and cloud computing is examined from the economic perspective, based on agency theory (AT). A conceptual framework is developed for analyzing these opportunities and challenges regarding the use of big data analytics and cloud computing in e-business environments. This framework allows organizations to engage in contracts that target competitive parity with their service-oriented decision support system (SODSS) to achieve a competitive advantage related to their core business model. A unique contribution of this paper is its perspective on how to engage a vendor contractually to achieve this competitive advantage. The framework provides insights for a manager in selecting a vendor for cloud-based big data services.

Responding to market turbulence by big data analytics and mass customization capability

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hongyan Sheng ◽  
Taiwen Feng ◽  
Lucheng Chen ◽  
Dianhui Chu
Keyword(s):  
Big Data ◽  
Mass Customization ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Two Dimensions ◽  
Content Type ◽  
Managerial Skills ◽  
Service Oriented ◽  
Market Turbulence ◽  
The Impact

PurposeThis study aims to explore how to respond to market turbulence by big data analytics (BDA) capability and mass customization capability (MCC) from the perspective of organizational information processing theory (OIPT).Design/methodology/approachThis study examines the research hypotheses using hierarchical regression analysis by collecting data from 277 Chinese firms.FindingsThe results reveal that supply chain agility (SCA) completely mediates the impacts of technical skills on product-oriented and service-oriented MCC and the impact of data-driven decision-making culture (DDC) on service-oriented MCC. SCA also partially mediates the impacts of managerial skills on two dimensions of MCC and the impact of DDC on product-oriented MCC. In addition, market turbulence strengthens the impact of managerial skills on SCA.Originality/valueThis study provides insightful contributions and implications for enhancing MCC to cope with market turbulence.

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