scholarly journals A Mathematical Approach to Healthcare Insurance Data Analytics

2021 ◽  
pp. 82-93
Author(s):  
Terungwa Simon Yange ◽  
Ishaya Peni Gambo ◽  
Theresa Omodunbi ◽  
Hettie Abimbola Soriyan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Java Programming ◽  
Computer Scientists ◽  
Marketing Activities ◽  
Healthcare Insurance ◽  
Using Data ◽  
Insurance Data ◽  
Real Challenge

The emergence of big data analytics as a way of deriving insights from data has brought excitement to mathematicians, statisticians, computer scientists and other professionals. However, the near absence of a mathematical foundation for analytics has become a real challenge amidst the flock of big data marketing activities, especially in healthcare insurance. This paper developed a mathematical model for the analytics of healthcare insurance data using set theory. A prototype for the model was implemented using Java Programming Language, MapReduce Framework, Association Rule Mining and MongoDB. Also, it was tested for accuracy using data from the National Health Insurance Scheme in Nigeria with a view to reducing delays in the processes of the Scheme. The result showed that the accuracy level was 97.14% on average, which depicts a higher performance for the model. This result implies that delays affecting the processing of data submitted by the providers and enrollees to the HMOs reduced drastically leading to the improvement in the flow of resources.

A Mathematical Foundation of Big Data

2017 ◽  
Vol 13 (02) ◽  
pp. 83-99 ◽  
Author(s):  
Zhaohao Sun ◽  
Paul P. Wang
Keyword(s):  
Big Data ◽  
Data Science ◽  
Big Data Analytics ◽  
Business Analytics ◽  
Mathematical Foundation ◽  
Business Professionals ◽  
Computer Scientists ◽  
Marketing Activities ◽  
Real Challenge ◽  
Big Data Computing

The recent research evolution on big data has brought exciting aspiration to mathematicians, computer scientists and business professionals alike. However, the lack of a sound mathematical foundation presents itself as a real challenge amidst the swarm of big data marketing activities. This paper intends to propose a possible mathematical theory as a foundation for big data research. Specifically, we propose the concept of the adjective “big” as a mathematical operator, furthermore, the concept of so-called “big” logically and naturally fits the concept of being “linguistics variable” as per fuzzy logic research community for decades. The consequence of adopting such a mathematical modeling can be profoundly considered as an abstraction of the technologies, systems, tools for data management and processing that transforms data into big data. In addition, the concept of infinity of the big data is based on the theory of calculus and the set theory. Furthermore, the concept of relativity of the big data, as we find out, is based on the operations of the fuzzy subsets theory. The proposed approach in this paper, we hope, can facilitate and open up more opportunities for big data research and developments on big data analytics, business analytics, big data intelligence, big data computing as well as big data science.

Big Data and Its Role in Facilitating the Visualization of Financial Analytics

2018 ◽  
pp. 704-722
Author(s):  
Iman Raeesi Vanani ◽  
Maziar Shiraj Kheiri
Keyword(s):  
Big Data ◽  
Global Economy ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Accounting Measurement ◽  
Use Of Data ◽  
Accounting Environment ◽  
Using Data ◽  
And Function ◽  
Support Decision Making

The business use of data analytics is growing rapidly in the accounting environment. Similar to many new systems that involve accounting information, data analytics has fundamentally changed task based processes particularly those tasks that provide inference, prediction and assurance to decision makers. Big Data analytics is the process of inspecting, cleaning, transforming, and modeling Big Data to discover and communicate useful information and patterns, suggest conclusions, and support decision making. Big Data now pervades every sector and function of the global economy. These essays focus on the uses and challenges of Big Data in accounting (measurement) and auditing (assurance). The objective of this chapter is to examine how Big Data analytics will impact the accounting and auditing environment. This is important to practitioners as well as academics because they will be using data analytics in accounting and auditing tasks and will need to have an in-depth familiarity with financial analytics to effectively accomplish these tasks and make effective and efficient decisions.

Big Data Management

2017 ◽  
Vol 7 (3) ◽  
pp. 44-55 ◽  
Author(s):  
Kijpokin Kasemsap
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Data Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Care Industry ◽  
Business Leaders ◽  
Privacy And Security ◽  
Review Analysis ◽  
Using Data

The objective of this article is to provide the advanced issues and approaches of big data management. The literature review indicates the overview of big data management; the aspects of Big Data Analytics (BDA); the importance of big data management; the methods for big data management; the privacy and security concerns of big data management; and the big data management in the health care industry. Organizations that have been successful in working with effective big data management have accomplished this issue using data to help make sense of the information. The volume of data that companies are able to gather about customers and market conditions can provide business leaders with insights into new revenue and business opportunities, presuming they can spot the opportunities in vast amounts of data. The literature review analysis provides both practitioners and researchers an important understanding about big data management in modern organizations.

scholarly journals Big Data Analytics in Government: Improving Decision Making for R&D Investment in Korean SMEs

2019 ◽  
Vol 12 (1) ◽  
pp. 202
Author(s):  
Eun Sun Kim ◽  
Yunjeong Choi ◽  
Jeongeun Byun
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Driven ◽  
Medium Size ◽  
The Government ◽  
Using Data ◽  
High Level ◽  
D Investment

To expand the field of governmental applications of Big Data analytics, this study presents a case of data-driven decision-making using information on research and development (R&D) projects in Korea. The Korean government has continuously expanded the proportion of its R&D investment in small and medium-size enterprises to improve the commercialization performance of national R&D projects. However, the government has struggled with the so-called “Korea R&D Paradox”, which refers to how performance has lagged despite the high level of investment in R&D. Using data from 48,309 national R&D projects carried out by enterprises from 2013 to 2017, we perform a cluster analysis and decision tree analysis to derive the determinants of their commercialization performance. This study provides government entities with insights into how they might adjust their approach to Big Data analytics to improve the efficiency of R&D investment in small- and medium-sized enterprises.

scholarly journals Editorial: The personalisation of insurance: Data, behaviour and innovation

2020 ◽  
Vol 7 (2) ◽  
pp. 205395172097370
Author(s):  
Liz McFall ◽  
Gert Meyers ◽  
Ine Van Hoyweghen
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Disruptive Innovation ◽  
Black Boxes ◽  
Disruptive Innovations ◽  
Insurance Data

The adoption of Big Data analytics (BDA) in insurance has proved controversial but there has been little analysis specifying how insurance practices are changing. Is insurance passively subject to the forces of disruptive innovation, moving away from the pooling of risk towards its personalisation or individualisation, and what might that mean in practice? This special theme situates disruptive innovations, particularly the experimental practices of behaviour-based personalisation, in the context of the practice and regulation of contemporary insurance. Our contributors argue that behaviour-based personalisation in insurance has different and broader implications than have yet been appreciated. BDAs are changing how insurance governs risk; how it knows, classifies, manages, prices and sells it, in ways that are more opaque and more extensive than the black boxes of in-car telematics.

Big-data analytics for Arrhythmia Classification using data compression and kernel methods

2015 ◽  
Author(s):  
J.M. Lillo-Castellano ◽  
I. Mora-Jimenez ◽  
R. Moreno-Gonzalez ◽  
M. Montserrat-Garcia-de-Pablo ◽  
A. Garcia-Alberola ◽  
...  
Keyword(s):  
Big Data ◽  
Data Compression ◽  
Kernel Methods ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Using Data

scholarly journals Big Data Science and EXASOL as Big Data Analytics tool

2019 ◽  
Vol 8 (9S) ◽  
pp. 933-937
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
System Architectures ◽  
Huge Data ◽  
Exploratory Approach ◽  
Data Scientist ◽  
Using Data

Big data and Data science are the two top trends of recent years. Both can be combined together as big data science. This leads to the demand for new system architectures which facilitates the development of processes which can handle huge data volumes without deterring the agility, flexibility and the interactive feel which suits the exploratory approach of a data scientist. Businesses today have found ways of using data as the principal factor for value generation. These data-driven businesses apply a variety of data tools as data analysis is one of the chief elements in this process. In order to raise data science to the new computational level that is required to meet the challenges of big data and interactive advanced analytics, EXASOL has introduced a new technological approach. This tool enables us more effective and easy data analysis.

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