Big Data Security Management

Big Data ◽  
2016 ◽  
pp. 247-260
Author(s):  
Zaiyong Tang ◽  
Youqin Pan
Keyword(s):  
Big Data ◽  
Data Security ◽  
Scale Up ◽  
Big Data Analytics ◽  
Security Management ◽  
Future Research ◽  
Maturity Model ◽  
Research Issues ◽  
Security Standards ◽  
Industry Trends

Big data is a buzzword today, and security of big data is a big concern. Traditional security standards and technologies cannot scale up to deliver reliable and effective security solutions in the big data environment. This chapter covers big data security management from concepts to real-world issues. By identifying and laying out the major challenges, industry trends, legal and regulatory environments, security principles, security management frameworks, security maturity model, big data analytics in solving security problems, current research results, and future research issues, this chapter provides researchers and practitioners with a timely reference and guidance in securing big data processing, management, and applications.

Big Data Security Management

2015 ◽  
pp. 53-66
Author(s):  
Zaiyong Tang ◽  
Youqin Pan
Keyword(s):  
Big Data ◽  
Data Security ◽  
Scale Up ◽  
Big Data Analytics ◽  
Security Management ◽  
Future Research ◽  
Maturity Model ◽  
Research Issues ◽  
Security Standards ◽  
Industry Trends

Big data is a buzzword today, and security of big data is a big concern. Traditional security standards and technologies cannot scale up to deliver reliable and effective security solutions in the big data environment. This chapter covers big data security management from concepts to real-world issues. By identifying and laying out the major challenges, industry trends, legal and regulatory environments, security principles, security management frameworks, security maturity model, big data analytics in solving security problems, current research results, and future research issues, this chapter provides researchers and practitioners with a timely reference and guidance in securing big data processing, management, and applications.

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.

An experimental investigation of the impact of using big data analytics on customers’ performance measurement

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Marwa Rabe Mohamed Elkmash ◽  
Magdy Gamal Abdel-Kader ◽  
Bassant Badr El Din
Keyword(s):  
Big Data ◽  
Performance Measurement ◽  
Data Analytics ◽  
Positive Impact ◽  
Big Data Analytics ◽  
Unstructured Data ◽  
Future Research ◽  
Web Based ◽  
Content Type ◽  
The Impact

Purpose This study aims to investigate and explore the impact of big data analytics (BDA) as a mechanism that could develop the ability to measure customers’ performance. To accomplish the research aim, the theoretical discussion was developed through the combination of the diffusion of innovation theory with the technology acceptance model (TAM) that is less developed for the research field of this study. Design/methodology/approach Empirical data was obtained using Web-based quasi-experiments with 104 Egyptian accounting professionals. Further, the Wilcoxon signed-rank test and the chi-square goodness-of-fit test were used to analyze data. Findings The empirical results indicate that measuring customers’ performance based on BDA increase the organizations’ ability to analyze the customers’ unstructured data, decrease the cost of customers’ unstructured data analysis, increase the ability to handle the customers’ problems quickly, minimize the time spent to analyze the customers’ data and obtaining the customers’ performance reports and control managers’ bias when they measure customer satisfaction. The study findings supported the accounting professionals’ acceptance of BDA through the TAM elements: the intention to use (R), perceived usefulness (U) and the perceived ease of use (E). Research limitations/implications This study has several limitations that could be addressed in future research. First, this study focuses on customers’ performance measurement (CPM) only and ignores other performance measurements such as employees’ performance measurement and financial performance measurement. Future research can examine these areas. Second, this study conducts a Web-based experiment with Master of Business Administration students as a study’s participants, researchers could conduct a laboratory experiment and report if there are differences. Third, owing to the novelty of the topic, there was a lack of theoretical evidence in developing the study’s hypotheses. Practical implications This study succeeds to provide the much-needed empirical evidence for BDA positive impact in improving CPM efficiency through the proposed framework (i.e. CPM and BDA framework). Furthermore, this study contributes to the improvement of the performance measurement process, thus, the decision-making process with meaningful and proper insights through the capability of collecting and analyzing the customers’ unstructured data. On a practical level, the company could eventually use this study’s results and the new insights to make better decisions and develop its policies. Originality/value This study holds significance as it provides the much-needed empirical evidence for BDA positive impact in improving CPM efficiency. The study findings will contribute to the enhancement of the performance measurement process through the ability of gathering and analyzing the customers’ unstructured data.

scholarly journals The Application of Downhole Vibration Factor in Drilling Tool Reliability Big Data Analytics—A Review

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Yali Ren ◽  
Ning Wang ◽  
Jinwei Jiang ◽  
Junxiao Zhu ◽  
Gangbing Song ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Maintenance Planning ◽  
Drilling Tool ◽  
Operation Conditions ◽  
Drilling Tools ◽  
Drilling Operations ◽  
Field Reliability

In the challenging downhole environment, drilling tools are normally subject to high temperature, severe vibration, and other harsh operation conditions. The drilling activities generate massive field data, namely field reliability big data (FRBD), which includes downhole operation, environment, failure, degradation, and dynamic data. Field reliability big data has large size, high variety, and extreme complexity. FRBD presents abundant opportunities and great challenges for drilling tool reliability analytics. Consequently, as one of the key factors to affect drilling tool reliability, the downhole vibration factor plays an essential role in the reliability analytics based on FRBD. This paper reviews the important parameters of downhole drilling operations, examines the mode, physical and reliability impact of downhole vibration, and presents the features of reliability big data analytics. Specifically, this paper explores the application of vibration factor in reliability big data analytics covering tool lifetime/failure prediction, prognostics/diagnostics, condition monitoring (CM), and maintenance planning and optimization. Furthermore, the authors highlight the future research about how to better apply the downhole vibration factor in reliability big data analytics to further improve tool reliability and optimize maintenance planning.

Applications of Big Data Analytics in Healthcare Management Systems

2021 ◽  
Vol 23 (06) ◽  
pp. 1167-1182
Author(s):  
Shreyas Nopany ◽  
◽  
Prof. Manonmani S ◽  
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Management ◽  
Healthcare Sector ◽  
Future Research ◽  
Healthcare Industry ◽  
Strategy Formulation ◽  
Data Governance ◽  
Number Of Patients

The healthcare industry has become increasingly demanding in recent years. The growing number of patients makes it difficult for doctors and staff to manage their work effectively. In order to achieve their objectives, data analysts collect a large amount of data, analyze it, and use it to derive valuable insights. Data analytics may become a promising solution as healthcare industry demands increase. The paper discusses the challenges of data analytics in the healthcare sector and the benefits of using big data for healthcare analytics. Aside from focusing on the opportunities that big data analytics has in the healthcare sector, the paper will also discuss data governance, strategy formulation, and improvements to IT infrastructure. Implementation techniques include Hadoop, HDFS, MapReduce, and Apache in Big Data Analytics. A Healthcare Management System can be categorized into five divisions, namely, Drug discovery, Disease prevention, diagnosis and treatment, Hospital operations, post-care, requiring comprehensive data management. Big Data analysis support transformation is identified as a required component in future research for the application of Big Data in HealthCare.

Big Data Analytics in Cloud Computing

2019 ◽  
pp. 325-341
Author(s):  
Rajganesh Nagarajan ◽  
Ramkumar Thirunavukarasu
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Processing ◽  
Data Visualization ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Big Data Processing ◽  
Investment Cost

In this chapter, the authors consider different categories of data, which are processed by the big data analytics tools. The challenges with respect to the big data processing are identified and a solution with the help of cloud computing is highlighted. Since the emergence of cloud computing is highly advocated because of its pay-per-use concept, the data processing tools can be effectively deployed within cloud computing and certainly reduce the investment cost. In addition, this chapter talks about the big data platforms, tools, and applications with data visualization concept. Finally, the applications of data analytics are discussed for future research.

Semantic Technologies and Big Data Analytics for Cyber Defence

Web Services ◽  
2019 ◽  
pp. 1430-1443
Author(s):  
Louise Leenen ◽  
Thomas Meyer
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Data Sets ◽  
Semantic Technologies ◽  
Data Types ◽  
Intelligent Decision Making ◽  
Big Data Technologies

The Governments, military forces and other organisations responsible for cybersecurity deal with vast amounts of data that has to be understood in order to lead to intelligent decision making. Due to the vast amounts of information pertinent to cybersecurity, automation is required for processing and decision making, specifically to present advance warning of possible threats. The ability to detect patterns in vast data sets, and being able to understanding the significance of detected patterns are essential in the cyber defence domain. Big data technologies supported by semantic technologies can improve cybersecurity, and thus cyber defence by providing support for the processing and understanding of the huge amounts of information in the cyber environment. The term big data analytics refers to advanced analytic techniques such as machine learning, predictive analysis, and other intelligent processing techniques applied to large data sets that contain different data types. The purpose is to detect patterns, correlations, trends and other useful information. Semantic technologies is a knowledge representation paradigm where the meaning of data is encoded separately from the data itself. The use of semantic technologies such as logic-based systems to support decision making is becoming increasingly popular. However, most automated systems are currently based on syntactic rules. These rules are generally not sophisticated enough to deal with the complexity of decisions required to be made. The incorporation of semantic information allows for increased understanding and sophistication in cyber defence systems. This paper argues that both big data analytics and semantic technologies are necessary to provide counter measures against cyber threats. An overview of the use of semantic technologies and big data technologies in cyber defence is provided, and important areas for future research in the combined domains are discussed.

Big Data Analytics Driven Supply Chain Transformation

2019 ◽  
pp. 106-124 ◽  
Author(s):  
Mondher Feki
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Research Directions ◽  
Digital Platforms ◽  
New Frontier ◽  
Future Research Directions ◽  
Chain Transformation

Big data has emerged as the new frontier in supply chain management; however, few firms know how to embrace big data and capitalize on its value. The non-stop production of massive amounts of data on various digital platforms has prompted academics and practitioners to focus on the data economy. Companies must rethink how to harness big data and take full advantage of its possibilities. Big data analytics can help them in giving valuable insights. This chapter provides an overview of big data analytics use in the supply chain field and underlines its potential role in the supply chain transformation. The results show that big data analytics techniques can be categorized into three types: descriptive, predictive, and prescriptive. These techniques influence supply chain processes and create business value. This study sets out future research directions.

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