Using big data analytics to synthesize research domains and identify emerging fields in urban climatology

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Fei Huo ◽  
Li Xu ◽  
Yanping Li ◽  
James S. Famiglietti ◽  
Zhenhua Li ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Urban Climatology ◽  
Research Domains

Integration of Data Mining and Business Intelligence in Big Data Analytics

2021 ◽  
pp. 13-43
Author(s):  
Atik Kulakli
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Business Intelligence ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Impact Factors ◽  
Publication Trends ◽  
Author Productivity ◽  
Research Questions ◽  
Research Domains

The purpose of this chapter is to analyze and explore the research studies for scholarly publication trends and patterns related to the integration of data mining in particular business intelligence in big data analytics domains published in the period of 2010-2019. Research patterns explore in highly prestigious sources that have high impact factors and citations counted in the ISI Web of Science Core Collection database (indexes included SCI-Exp and SSCI). Bibliometric analysis methods applied for this study under the research limitations. Research questions formed based on bibliometric principles concentrating fields such as descriptive of publication, author productivity, country-regions distribution, keyword analysis with contribution among researchers, citation analysis, co-citation patterns searched. Findings showed strong relations and patterns on these important research domains. Besides this chapter would useful for researchers to obtain an overview of publication trends on research domains to be concerned for further studies and shows the potential gaps in those fields.

Integration of Data Mining and Business Intelligence in Big Data Analytics

2022 ◽  
pp. 1892-1922
Author(s):  
Atik Kulakli
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Business Intelligence ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Impact Factors ◽  
Publication Trends ◽  
Author Productivity ◽  
Research Questions ◽  
Research Domains

The purpose of this chapter is to analyze and explore the research studies for scholarly publication trends and patterns related to the integration of data mining in particular business intelligence in big data analytics domains published in the period of 2010-2019. Research patterns explore in highly prestigious sources that have high impact factors and citations counted in the ISI Web of Science Core Collection database (indexes included SCI-Exp and SSCI). Bibliometric analysis methods applied for this study under the research limitations. Research questions formed based on bibliometric principles concentrating fields such as descriptive of publication, author productivity, country-regions distribution, keyword analysis with contribution among researchers, citation analysis, co-citation patterns searched. Findings showed strong relations and patterns on these important research domains. Besides this chapter would useful for researchers to obtain an overview of publication trends on research domains to be concerned for further studies and shows the potential gaps in those fields.

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

Can 'Big Data' Analytics Predict Policing Practice?

2016 ◽  
Author(s):  
Janet Chan ◽  
Lyria Bennett Moses
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics
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