Service-Oriented Big Data Analytics for Improving Buildings Energy Management in Smart Cities

Author(s):  
Nader Mohamed ◽  
Jameela Al-Jaroodi ◽  
Imad Jawhar
2021 ◽  
Vol 24 ◽  
pp. 100192
Author(s):  
Mariagrazia Fugini ◽  
Jacopo Finocchi ◽  
Paolo Locatelli

2017 ◽  
Vol 63 (4) ◽  
pp. 426-434 ◽  
Author(s):  
A.R. Al-Ali ◽  
Imran A. Zualkernan ◽  
Mohammed Rashid ◽  
Ragini Gupta ◽  
Mazin Alikarar

Author(s):  
V. Bassoo ◽  
V. Ramnarain-Seetohul ◽  
V. Hurbungs ◽  
T. P. Fowdur ◽  
Y. Beeharry

Author(s):  
Zhaohao Sun

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.


2021 ◽  
Author(s):  
Chun Sing Lai ◽  
Loi Lei Lai ◽  
Qi Hong Lai

2020 ◽  
Vol 22 (4) ◽  
pp. 60-74
Author(s):  
Emmanuel Wusuhon Yanibo Ayaburi ◽  
Michele Maasberg ◽  
Jaeung Lee

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.


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