Big Data Technologies using SVM (Case Study: Surface Water Classification on Regional Water Utility Company in Surabaya)

Author(s):  
Rizqi Putri Nourma Budiarti ◽  
Sritrusta Sukaridhoto ◽  
Mochamad Hariadi ◽  
Mauridhi Hery Purnomo
Author(s):  
León Darío Parra ◽  
Milenka Linneth Argote Cusi

Modern society generates about 7 Zetabytes each year, of which 75% comes from the connectivity of individuals to social networks. In this regard, the chapter presents a case study of the application of big data technologies for entrepreneurial analysis using global entrepreneurship monitor (GEM) data as a new tool of analysis. Therefore, the core of this chapter is to present the methodology that was used to develop and implement the big data app of GEM as well as the main results of project. On the other hand, the chapter remarks the advantages and disadvantages of this kind of technology for the case of GEM data. Finally, it presents the respective dashboards that interrelate the gem data with Word Bank indicators as a case study of the application of big data for entrepreneurship research.


Amicus Curiae ◽  
2020 ◽  
Vol 1 (3) ◽  
pp. 338-360
Author(s):  
Jamie Grace ◽  
Roxanne Bamford

Policymaking is increasingly being informed by ‘big data’ technologies of analytics, machine learning and artificial intelligence (AI). John Rawls used particular principles of reasoning in his 1971 book, A Theory of Justice, which might help explore known problems of data bias, unfairness, accountability and privacy, in relation to applications of machine learning and AI in government. This paper will investigate how the current assortment of UK governmental policy and regulatory developments around AI in the public sector could be said to meet, or not meet, these Rawlsian principles, and what we might do better by incorporating them when we respond legislatively to this ongoing challenge. This paper uses a case study of data analytics and machine-learning regulation as the central means of this exploration of Rawlsian thinking in relation to the redevelopment of algorithmic governance.


Author(s):  
Lyubomir Gotsev ◽  
Boyan Jekov ◽  
Eugenia Kovatcheva ◽  
Roumen Nikolov ◽  
Ilian Barzev ◽  
...  

2017 ◽  
Vol 9 (4) ◽  
pp. 639
Author(s):  
Padmavathi Vanka ◽  
T. Sudha

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