Building a Maturity Framework for Big Data Cybersecurity Analytics

2019 ◽  
pp. 164-183 ◽  
Author(s):  
Chi Minh Pham
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Data Management ◽  
Big Data Analytics ◽  
Successful Implementation ◽  
Maturity Model ◽  
The Novel ◽  
Model Concept ◽  
New Approach ◽  
Novel Approach

In recent years, big data analytics has become widely applied in cybersecurity, leading to the novel approach of big data cybersecurity analytics. While some organizations have been adopting this new approach in tackling cybercrime, there are limited guidelines to which companies can refer. Therefore, the renowned maturity model concept, which offers a systematic approach for an organization to measure and improve its maturity level, is applied in this study. On the basis of a comprehensive literature review, this chapter proposes a maturity framework for big data cybersecurity analytics. This synthesized comprehensive maturity framework comprises seven dimensions across five stage levels—namely organization, human, infrastructure, data management, analytics application, governance, and security dimensions. Knowing which dimensions need to be improved and which pathway to follow ensures the successful implementation of big data cybersecurity analytics within organizations.

Building a Maturity Framework for Big Data Cybersecurity Analytics

2021 ◽  
pp. 365-385
Author(s):  
Chi Minh Pham
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Data Management ◽  
Big Data Analytics ◽  
Successful Implementation ◽  
Maturity Model ◽  
The Novel ◽  
Model Concept ◽  
New Approach ◽  
Novel Approach

In recent years, big data analytics has become widely applied in cybersecurity, leading to the novel approach of big data cybersecurity analytics. While some organizations have been adopting this new approach in tackling cybercrime, there are limited guidelines to which companies can refer. Therefore, the renowned maturity model concept, which offers a systematic approach for an organization to measure and improve its maturity level, is applied in this study. On the basis of a comprehensive literature review, this chapter proposes a maturity framework for big data cybersecurity analytics. This synthesized comprehensive maturity framework comprises seven dimensions across five stage levels—namely organization, human, infrastructure, data management, analytics application, governance, and security dimensions. Knowing which dimensions need to be improved and which pathway to follow ensures the successful implementation of big data cybersecurity analytics within organizations.

scholarly journals Potential item set mining by using utility pattern growth model in big data

2017 ◽  
Vol 7 (1.3) ◽  
pp. 66
Author(s):  
S. Angel Latha Mary ◽  
R. Divya ◽  
K. Uma Maheswari
Keyword(s):  
Big Data ◽  
Time Complexity ◽  
The Novel ◽  
Test Results ◽  
Utility Mining ◽  
Complex Issue ◽  
New Approach ◽  
Novel Approach ◽  
Using Data ◽  
Candidate Item

Information extracted by using data mining in earlier days. Now a day’s, the most talked about technology is Big Data. Utility Mining is the most crucial task in the real time application where the customers prefer to choose the item set which can yield more profit. Handling of large volume of transactional patterns becomes the complex issue in every application which is resolved in the existing work introducing the parallel utility mining process which will process the candidate item sets in the paralyzed manner by dividing the entire tasks into sub partition. Each sub partition would be processed in individual mapper and then be resulted with the final output value. The time complexity would be more when processing an unnecessary candidate item sets. This problem is resolved in the proposed methodology by introducing the novel approach called UP-Growth and UP-Growth+ which will prune the candidate item sets to reduce the dimension of the candidate item sets. The time complexity is further reduced by representing the candidate item sets in the tree layout. The test results prove that the proposed new approach provides better result than the existing work in terms of accuracy.

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.

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.

FDM: Fuzzy-Optimized Data Management Technique for Improving Big Data Analytics

2021 ◽  
Vol 29 (1) ◽  
pp. 177-185
Author(s):  
Gunasekaran Manogaran ◽  
P. Mohamed Shakeel ◽  
S. Baskar ◽  
Ching-Hsien Hsu ◽  
Seifedine Nimer Kadry ◽  
...  
Keyword(s):  
Big Data ◽  
Data Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Management Technique

Strategic issues of big data analytics applications for managing health-care sector: a systematic literature review and future research agenda

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rajesh Kumar Singh ◽  
Saurabh Agrawal ◽  
Abhishek Sahu ◽  
Yigit Kazancoglu
Keyword(s):  
Health Care ◽  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Health Care Sector ◽  
Future Research ◽  
Research Gaps ◽  
Content Type

PurposeThe proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.Design/methodology/approachFora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.FindingsBD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.Research limitations/implicationsThe proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.Originality/valueThere are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.

scholarly journals A survey and analysis of big data management based on computational methodologies

2020 ◽  
Vol 6 (20) ◽  
pp. 48-53
Author(s):  
Shweta Kumari
Keyword(s):  
Big Data ◽  
Data Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Business Enterprise ◽  
Enormous Amount ◽  
Current Scenario ◽  
Data Points ◽  
The Impact ◽  
Day By Day

n a business enterprise there is an enormous amount of data generated or processed daily through different data points. It is increasing day by day. It is tough to handle it through traditional applications like excel or any other tools. So, big data analytics and environment may be helpful in the current scenario and the situation discussed above. This paper discussed the big data management ways with the impact of computational methodologies. It also covers the applicability domains and areas. It explores the computational methods applicability scenario and their conceptual design based on the previous literature. Machine learning, artificial intelligence and data mining techniques have been discussed for the same environment based on the related study.

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