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.

Trends and Opportunities in Health Analytics as a Service and Implications for Use in Low Resource Settings: A Literature Review Abstract (Preprint)

2019 ◽  
Author(s):  
Meghana Bastwadkar ◽  
Carolyn McGregor ◽  
S Balaji
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Intensive Care ◽  
Literature Review ◽  
Health Monitoring ◽  
Data Analytics ◽  
Neonatal Intensive Care ◽  
Big Data Analytics ◽  
Healthcare Facilities

BACKGROUND This paper presents a systematic literature review of existing remote health monitoring systems with special reference to neonatal intensive care (NICU). Articles on NICU clinical decision support systems (CDSSs) which used cloud computing and big data analytics were surveyed. OBJECTIVE The aim of this study is to review technologies used to provide NICU CDSS. The literature review highlights the gaps within frameworks providing HAaaS paradigm for big data analytics METHODS Literature searches were performed in Google Scholar, IEEE Digital Library, JMIR Medical Informatics, JMIR Human Factors and JMIR mHealth and only English articles published on and after 2015 were included. The overall search strategy was to retrieve articles that included terms that were related to “health analytics” and “as a service” or “internet of things” / ”IoT” and “neonatal intensive care unit” / ”NICU”. Title and abstracts were reviewed to assess relevance. RESULTS In total, 17 full papers met all criteria and were selected for full review. Results showed that in most cases bedside medical devices like pulse oximeters have been used as the sensor device. Results revealed a great diversity in data acquisition techniques used however in most cases the same physiological data (heart rate, respiratory rate, blood pressure, blood oxygen saturation) was acquired. Results obtained have shown that in most cases data analytics involved data mining classification techniques, fuzzy logic-NICU decision support systems (DSS) etc where as big data analytics involving Artemis cloud data analysis have used CRISP-TDM and STDM temporal data mining technique to support clinical research studies. In most scenarios both real-time and retrospective analytics have been performed. Results reveal that most of the research study has been performed within small and medium sized urban hospitals so there is wide scope for research within rural and remote hospitals with NICU set ups. Results have shown creating a HAaaS approach where data acquisition and data analytics are not tightly coupled remains an open research area. Reviewed articles have described architecture and base technologies for neonatal health monitoring with an IoT approach. CONCLUSIONS The current work supports implementation of the expanded Artemis cloud as a commercial offering to healthcare facilities in Canada and worldwide to provide cloud computing services to critical care. However, no work till date has been completed for low resource setting environment within healthcare facilities in India which results in scope for research. It is observed that all the big data analytics frameworks which have been reviewed in this study have tight coupling of components within the framework, so there is a need for a framework with functional decoupling of components.

Big Data Predictive and Prescriptive Analytics

2014 ◽  
pp. 370-391 ◽  
Author(s):  
Ganesh Chandra Deka
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Data Analytics ◽  
Action Plan ◽  
Big Data Analytics ◽  
Clear Understanding ◽  
Future Planning ◽  
Prescriptive Analytics ◽  
Hybrid Data ◽  
Recent Trends

The Analytics tools are capable of suggesting the most favourable future planning by analyzing “Why” and “How” blended with What, Who, Where, and When. Descriptive, Predictive, and Prescriptive analytics are the analytics currently in use. Clear understanding of these three analytics will enable an organization to chalk out the most suitable action plan taking various probable outcomes into account. Currently, corporate are flooded with structured, semi-structured, unstructured, and hybrid data. Hence, the existing Business Intelligence (BI) practices are not sufficient to harness potentials of this sea of data. This change in requirements has made the cloud-based “Analytics as a Service (AaaS)” the ultimate choice. In this chapter, the recent trends in Predictive, Prescriptive, Big Data analytics, and some AaaS solutions are discussed.

Big Data Predictive and Prescriptive Analytics

Big Data ◽  
2016 ◽  
pp. 30-55 ◽  
Author(s):  
Ganesh Chandra Deka
Keyword(s):  
Big Data ◽  
Business Intelligence ◽  
Data Analytics ◽  
Action Plan ◽  
Big Data Analytics ◽  
Clear Understanding ◽  
Future Planning ◽  
Prescriptive Analytics ◽  
Hybrid Data ◽  
Recent Trends

The Analytics tools are capable of suggesting the most favourable future planning by analyzing “Why” and “How” blended with What, Who, Where, and When. Descriptive, Predictive, and Prescriptive analytics are the analytics currently in use. Clear understanding of these three analytics will enable an organization to chalk out the most suitable action plan taking various probable outcomes into account. Currently, corporate are flooded with structured, semi-structured, unstructured, and hybrid data. Hence, the existing Business Intelligence (BI) practices are not sufficient to harness potentials of this sea of data. This change in requirements has made the cloud-based “Analytics as a Service (AaaS)” the ultimate choice. In this chapter, the recent trends in Predictive, Prescriptive, Big Data analytics, and some AaaS solutions are discussed.

Computational and Data Mining Perspectives on HIV/AIDS in Big Data Era

2022 ◽  
pp. 1477-1503
Author(s):  
Ali Al Mazari
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Sector ◽  
Future Research ◽  
Research Directions ◽  
Scientific Disciplines ◽  
Future Research Directions ◽  
Hiv Aids

HIV/AIDS big data analytics evolved as a potential initiative enabling the connection between three major scientific disciplines: (1) the HIV biology emergence and evolution; (2) the clinical and medical complex problems and practices associated with the infections and diseases; and (3) the computational methods for the mining of HIV/AIDS biological, medical, and clinical big data. This chapter provides a review on the computational and data mining perspectives on HIV/AIDS in big data era. The chapter focuses on the research opportunities in this domain, identifies the challenges facing the development of big data analytics in HIV/AIDS domain, and then highlights the future research directions of big data in the healthcare sector.

Big Data Analytics Adoption Factors in Improving Information Systems Security

2022 ◽  
pp. 1231-1248
Author(s):  
Marouane Balmakhtar ◽  
Scott E. Mensch
Keyword(s):  
Big Data ◽  
Information Systems ◽  
Information Security ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Review Of The Literature ◽  
Information Systems Security ◽  
Systems Security ◽  
Security Effectiveness ◽  
Research Questions

This research measured determinants that influence the willingness of IT/IA professionals to recommend Big Data analytics to improve information systems security in an organization. A review of the literature as well as the works of prior researchers provided the basis for formulation of research questions. Results of this study found that security effectiveness, organizational need, and reliability play a role in the decision to recommend big data analytics to improve information security. This research has implications for both consumers and providers of big data analytics services through the identification of factors that influence IT/IA professionals. These factors aim to improve information systems security, and therefore, which service offerings are likely to meet the needs of these professionals and their organizations.

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