scholarly journals Big-data Analytics: Exploring the Well-being Trend in South Korea Through Inductive Reasoning

2021 ◽  
Vol 15 (6) ◽  
Keyword(s):  
Big Data ◽  
South Korea ◽  
Data Analytics ◽  
Inductive Reasoning ◽  
Big Data Analytics ◽  
Well Being

Economic evaluation of the trilateral FTA among China, Japan, and South Korea with big data analytics

2019 ◽  
Vol 128 ◽  
pp. 1040-1051 ◽  
Author(s):  
Lianbiao Cui ◽  
Malin Song ◽  
Lei Zhu
Keyword(s):  
Big Data ◽  
Economic Evaluation ◽  
South Korea ◽  
Data Analytics ◽  
Big Data Analytics

scholarly journals The Smart City Evolution in South Korea: Findings from Big Data Analytics

2020 ◽  
Vol 7 (1) ◽  
pp. 301-311 ◽  
Author(s):  
Choongik CHOI ◽  
◽  
Junho CHOI ◽  
Chulmin KIM ◽  
Dongkwan LEE
Keyword(s):  
Big Data ◽  
South Korea ◽  
Smart City ◽  
Data Analytics ◽  
Big Data Analytics

Innovation Policies and Big Data: Opportunities and Challenges

2021 ◽  
Author(s):  
Eva Arrilucea ◽  
Miren Nekane Bilbao ◽  
Javier Herrera ◽  
Javier Del Ser
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Innovation Policy ◽  
Dynamic Environment ◽  
Big Data Analytics ◽  
Well Being ◽  
Policy Cycle ◽  
The Public ◽  
Degree Of Maturity ◽  
Innovation Policies

Innovation policies are considered to be one of the main tools to turn innovation into wealth, well-being and competitiveness in territories worldwide. However, given the ever-growing data-centered ecosystem where such policies coexist nowadays there is a founded suspicion that traditional methods for policy analysis, design and evaluation begin to fail, particularly when faster and more effective answers to societal paradigms are requested in a context characterized by sharp technological changes and unprecedented economic, scientific, political and social scenario. This chapter addresses the question whether Big Data analytics can become a tool capable of overcoming the current obstacles and adapt the public policy cycle to the new reality as it seems to be happening in the case of the private sector. We also explore if Big Data analytics can be the definitive tool to develop best policy solutions in a subjective, uncertain and dynamic environment, underpinned by different interests, as well as the degree of maturity for its application. To this end this work explores and exposes the role played to date by data in the design of innovation policies, concluding with a reasoned insight on the practical issues and unsolved research challenges that should be surpassed before empowering innovation policy making processes with Big Data analytics.

A FOUNDATION FOR INDUCTIVE REASONING IN HARNESSING THE POTENTIAL OF BIG DATA

2020 ◽  
Vol 19 (1) ◽  
pp. 238-258
Author(s):  
ROSSI A. HASSAD
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Deductive Reasoning ◽  
Statistics Education ◽  
Inductive Reasoning ◽  
Big Data Analytics ◽  
Pedagogical Strategies ◽  
Research Journal ◽  
Effective Pedagogy

Training programs for statisticians and data scientists in healthcare should give greater importance to fostering inductive reasoning toward developing a mindset for optimizing Big Data. This can complement the current predominant focus on the hypothetico-deductive reasoning model, and is theoretically supported by the constructivist philosophy and Gestalt theory. Big-Data analytics is primarily exploratory in nature, aimed at discovery and innovation, and this requires fluid or inductive reasoning, which can be facilitated by epidemiological concepts (taxonomic and causal) as intuitive theories. Pedagogical strategies such as problem-based learning (PBL) and cooperative learning can be effective in this regard. Empirical research is required to ascertain instructors’ and practitioners’ perceptions about the role of inductive reasoning in Big-Data analytics, what constitutes effective pedagogy, and how core epidemiological concepts interact with the evidence from Big Data to produce outcomes. Together these can support the development of guidelines for an effective integrated curriculum for the training of statisticians and data scientists First published February 2020 at Statistics Education Research Journal Archives

scholarly journals Pushing the limits of accountability: big data analytics containing and controlling COVID-19 in South Korea

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Paul D. Ahn ◽  
Danture Wickramasinghe
Keyword(s):  
Big Data ◽  
South Korea ◽  
Financial Services ◽  
Media Coverage ◽  
Data Analytics ◽  
Central Government ◽  
Big Data Analytics ◽  
Content Type ◽  
The Government ◽  
State Form

PurposeThe purpose of this paper is to illustrate how big data analytics pushed the limits of individuals' accountability as South Korea tried to control and contain coronavirus disease 2019 (COVID-19).Design/methodology/approachThe authors draw upon Deleuzo-Guattarian framework elaborating how a surveillant assemblage was rhizomatically created and operated to monitor a segment of the population holding them accountable. Publicly available secondary data, such as press release from the government and media coverage, were used.FindingsA COVID-19 Smart Management System and a Self-Quarantine Safety Protection App constituted a surveillance assemblage operating in a “state-form”. This comprises the central government departments, local councils, policing systems, providers of telecommunication and financial services, and independent groups of people. This assemblage pushed the limits of accountability as individuals who tested positive or might bear possible future risks of the infection and transmission were held accountable for their locations and health conditions.Practical implicationsPolicymakers may consider constructing this type of state-form for containing and controlling pandemics, such as COVID-19, while dealing with the issue of undermined privacy.Social implicationsThe mass may consider to what extent individuals' personal information should be protected and how to hold the governments accountable for the legitimate use of such information.Originality/valueWhile accountability studies have largely focussed on formal organisations, the authors illustrated how a broader context of a state-form, harnessing big data analytics, pushes the limits of accountability.

Big Data Analytics: The Next Big Thing

2019 ◽  
Vol 54 (5) ◽  
pp. 20
Author(s):  
Dheeraj Kumar Pradhan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Strategy in Discovery Mode - Wie Big Data & Analytics strategisches Denken verdrängen kann

2020 ◽  
Vol 49 (5) ◽  
pp. 11-17
Author(s):  
Thomas Wrona ◽  
Pauline Reinecke
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Big Data & Analytics (BDA) ist zu einer kaum hinterfragten Institution für Effizienz und Wettbewerbsvorteil von Unternehmen geworden. Zu viele prominente Beispiele, wie der Erfolg von Google oder Amazon, scheinen die Bedeutung zu bestätigen, die Daten und Algorithmen zur Erlangung von langfristigen Wettbewerbsvorteilen zukommt. Sowohl die Praxis als auch die Wissenschaft scheinen geradezu euphorisch auf den „Datenzug“ aufzuspringen. Wenn Risiken thematisiert werden, dann handelt es sich meist um ethische Fragen. Dabei wird häufig übersehen, dass die diskutierten Vorteile sich primär aus einer operativen Effizienzperspektive ergeben. Strategische Wirkungen werden allenfalls in Bezug auf Geschäftsmodellinnovationen diskutiert, deren tatsächlicher Innovationsgrad noch zu beurteilen ist. Im Folgenden soll gezeigt werden, dass durch BDA zwar Wettbewerbsvorteile erzeugt werden können, dass aber hiermit auch große strategische Risiken verbunden sind, die derzeit kaum beachtet werden.

A Review on Big Data Analytics Tools in Context with Scalability

2019 ◽  
Vol 7 (2) ◽  
pp. 273-277
Author(s):  
Ajay Kumar Bharti ◽  
Neha Verma ◽  
Deepak Kumar Verma
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Big Data Analytics Solutions and Challenges

2017 ◽  
Vol 49 (004) ◽  
pp. 825--830
Author(s):  
A. AHMED ◽  
R.U. AMIN ◽  
M. R. ANJUM ◽  
I. ULLAH ◽  
I. S. BAJWA
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Talent Management with Big Data Analytics: A Review

2018 ◽  
Author(s):  
Indira Sangapu
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Talent Management
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