scholarly journals Towards Big data Governance in Cybersecurity

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Longzhi Yang ◽  
Jie Li ◽  
Noe Elisa ◽  
Tom Prickett ◽  
Fei Chao
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Access ◽  
Quality Data ◽  
Data Sets ◽  
Data Governance ◽  
Business Decisions ◽  
Regulatory Frameworks ◽  
Big Data Technologies ◽  
Cooperative Decision Making

AbstractBig data refers to large complex structured or unstructured data sets. Big data technologies enable organisations to generate, collect, manage, analyse, and visualise big data sets, and provide insights to inform diagnosis, prediction, or other decision-making tasks. One of the critical concerns in handling big data is the adoption of appropriate big data governance frameworks to (1) curate big data in a required manner to support quality data access for effective machine learning and (2) ensure the framework regulates the storage and processing of the data from providers and users in a trustworthy way within the related regulatory frameworks (both legally and ethically). This paper proposes a framework of big data governance that guides organisations to make better data-informed business decisions within the related regularity framework, with close attention paid to data security, privacy, and accessibility. In order to demonstrate this process, the work also presents an example implementation of the framework based on the case study of big data governance in cybersecurity. This framework has the potential to guide the management of big data in different organisations for information sharing and cooperative decision-making.

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 in Business

2021 ◽  
pp. 235-249
Author(s):  
Farid Huseynov
Keyword(s):  
Decision Making ◽  
Big Data ◽  
New Products ◽  
Critical Role ◽  
Digital Data ◽  
Business Decisions ◽  
Business Operations ◽  
Operational Costs ◽  
Big Data Technologies ◽  
Different Sources

The term “big data” refers to the very large and diverse sets of structured, semi-structured, and unstructured digital data from different sources that accumulate and grow very rapidly on a continuous basis. Big data enables enhanced decision-making in various types of businesses. Through these technologies, businesses are able to cut operational costs, digitally transform business operations to be more efficient and effective, and make more informed business decisions. Big data technologies enable businesses to better understand their markets by uncovering hidden patterns behind consumer behaviors and introduce new products and services accordingly. This chapter shows the critical role that big data plays in businesses. Initially, in this chapter, big data and its underlying technologies are explained. Later, this chapter discusses how big data digitally transforms critical business operations for enhanced decision-making and superior customer experience. Finally, this chapter ends with the possible challenges of big data for businesses and possible solutions to these challenges.

scholarly journals How to use Big Data technologies to optimize operations in Upstream Petroleum Industry

2013 ◽  
Vol 1 (1) ◽  
pp. 19-25 ◽  
Author(s):  
Abdelkader Baaziz ◽  
Luc Quoniam
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Petroleum Industry ◽  
Data Sets ◽  
Big Data Technologies ◽  
World Economic ◽  
Disparate Data ◽  
Complex Forms ◽  
The Right ◽  
4D Seismic

“Big Data is the oil of the new economy” is the most famous citation during the three last years. It has even been adopted by the World Economic Forum in 2011. In fact, Big Data is like crude! It’s valuable, but if unrefined it cannot be used. It must be broken down, analyzed for it to have value. But what about Big Data generated by the Petroleum Industry and particularly its upstream segment? Upstream is no stranger to Big Data. Understanding and leveraging data in the upstream segment enables firms to remain competitive throughout planning, exploration, delineation, and field development.Oil Gas Companies conduct advanced geophysics modeling and simulation to support operations where 2D, 3D 4D Seismic generate significant data during exploration phases. They closely monitor the performance of their operational assets. To do this, they use tens of thousands of data-collecting sensors in subsurface wells and surface facilities to provide continuous and real-time monitoring of assets and environmental conditions. Unfortunately, this information comes in various and increasingly complex forms, making it a challenge to collect, interpret, and leverage the disparate data. As an example, Chevron’s internal IT traffic alone exceeds 1.5 terabytes a day.Big Data technologies integrate common and disparate data sets to deliver the right information at the appropriate time to the correct decision-maker. These capabilities help firms act on large volumes of data, transforming decision-making from reactive to proactive and optimizing all phases of exploration, development and production. Furthermore, Big Data offers multiple opportunities to ensure safer, more responsible operations. Another invaluable effect of that would be shared learning.The aim of this paper is to explain how to use Big Data technologies to optimize operations. How can Big Data help experts to decision-making leading the desired outcomes?Keywords:Big Data; Analytics; Upstream Petroleum Industry; Knowledge Management; KM; Business Intelligence; BI; Innovation; Decision-making under Uncertainty

Business Intelligence

2019 ◽  
pp. 578-588
Author(s):  
Richard T. Herschel
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Common Sense ◽  
Business Intelligence ◽  
Productivity Growth ◽  
Unstructured Data ◽  
Quality Data ◽  
Data Governance ◽  
Location Data ◽  
Multiple Dimensions

Big data is driving new forms of competition, new waves of productivity growth, innovation, and providing unique ways to interact with customers. Big data is comprised of a wealth of unstructured data that is being transmitted and captured by organizations to enhance business intelligence efforts. The forms that big data takes include text, video, voice, location data, social media, and its growth is exponential. Big data encompasses multiple dimensions, including volume, variety, velocity, veracity, variability, and complexity. Despite the opportunities that big data presents, organizations are having difficulty managing it, especially in the areas of data management and data governance. Executive involvement is critical to ensuring quality data for business intelligence as well as to ensure that their firms will be able to attract the necessary talent to effectively engage in BI analytics. It is recommended that management take a common-sense approach to dealing with big data to ensure that data-driven decision making is not compromised.

Semantic Technologies and Big Data Analytics for Cyber Defence

2018 ◽  
pp. 1375-1388
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.

Business Intelligence

2018 ◽  
pp. 951-960
Author(s):  
Richard T. Herschel
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Common Sense ◽  
Business Intelligence ◽  
Productivity Growth ◽  
Unstructured Data ◽  
Quality Data ◽  
Data Governance ◽  
Location Data ◽  
Multiple Dimensions

Big data is driving new forms of competition, new waves of productivity growth, innovation, and providing unique ways to interact with customers. Big data is comprised of a wealth of unstructured data that is being transmitted and captured by organizations to enhance business intelligence efforts. The forms that big data takes include text, video, voice, location data, social media, and its growth is exponential. Big data encompasses multiple dimensions, including volume, variety, velocity, veracity, variability, and complexity. Despite the opportunities that big data presents, organizations are having difficulty managing it, especially in the areas of data management and data governance. Executive involvement is critical to insuring quality data for business intelligence as well as to insure that their firms will be able to attract the necessary talent to effectively engage in BI analytics. It is recommended that management take a common sense approach to dealing with big data to insure that data-driven decision-making is not compromised.

Semantic Technologies and Big Data Analytics for Cyber Defence

2016 ◽  
Vol 6 (3) ◽  
pp. 53-64 ◽  
Author(s):  
Louise Leenen ◽  
Thomas Meyer
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Large Data ◽  
Future Research ◽  
Data Sets ◽  
Semantic Technologies ◽  
Data Types ◽  
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.

scholarly journals Improving the data access control using blockchain for healthcare domain

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 901
Author(s):  
Olaosebikan Tahir Yinka ◽  
Su-Cheng Haw ◽  
Timothy Tzen Vun Yap ◽  
Samini Subramaniam
Keyword(s):  
Big Data ◽  
Identity Management ◽  
Performance Metrics ◽  
Data Access ◽  
Data Access Control ◽  
Proposed Model ◽  
Big Data Technologies ◽  
Access To Data ◽  
Authorization Control ◽  
Privacy Issues

Introduction: Unauthorized access to data is one of the most significant privacy issues that hinder most industries from adopting big data technologies. Even though specific processes and structures have been put in place to deal with access authorization and identity management for large databases nonetheless, the scalability criteria are far beyond the capabilities of traditional databases. Hence, most researchers are looking into other solutions, such as big data management. Methods: In this paper, we firstly study the strengths and weaknesses of implementing cryptography and blockchain for identity management and authorization control in big data, focusing on the healthcare domain. Subsequently, we propose a decentralized data access and sharing system that preserves privacy to ensure adequate data access management under the blockchain. In addition, we designed a blockchain framework to resolve the decentralized data access and sharing system privacy issues, by implementing a public key infrastructure model, which utilizes a signature cryptography algorithm (elliptic curve and signcryption). Lastly, we compared the proposed blockchain model to previous techniques to see how well it performed. Results: We evaluated the blockchain on four performance metrics which include throughput, latency, scalability, and security. The proposed blockchain model was tested using a sample of 5000 patients and 500,000 observations. The performance evaluation results further showed that the proposed model achieves higher throughput and lower latency compared to existing approaches when the workload varies up to 10,000 transactions. Discussion: This research reviews the importance of blockchains as they provide infinite possibilities to individuals, companies, and governments.

scholarly journals Challenges Associated with Cross-Jurisdictional Analyses using Administrative Health Data and Primary Care Electronic Medical Records in Canada

2018 ◽  
Vol 3 (3) ◽  
Author(s):  
Alan Katz ◽  
Jennifer Enns ◽  
Sabrina T Wong ◽  
Tyler Williamson ◽  
Alexander Singer ◽  
...  
Keyword(s):  
British Columbia ◽  
Social Services ◽  
Administrative Data ◽  
Data Access ◽  
Health Data ◽  
Quality Data ◽  
Data Governance ◽  
Quality Health Care ◽  
Data Repositories ◽  
Administrative Health Data

Over the last 30 years, public investments in Canada and many other countries have created clinical and administrative health data repositories to support research on health and social services, population health and health policy. However, there is limited capacity to share and use data across jurisdictional boundaries, in part because of inefficient and cumbersome procedures to access these data and gain approval for their use in research. A lack of harmonization among variables and indicators makes it difficult to compare research among jurisdictions. These challenges affect the quality, scope, and impact of work that could be done. The purpose of this paper is to compare and contrast the data access procedures in three Canadian jurisdictions (Manitoba, Alberta and British Columbia), and to describe how we addressed the challenges presented by differences in data governance and architecture in a Canadian cross-jurisdictional research study. We characterize common stages in gaining access to administrative data among jurisdictions, including obtaining ethics approval, applying for data access from data custodians, and ensuring the extracted data is released to accredited individuals in secure data environments. We identify advantages of Manitoba’s flexible ‘stewardship’ model over the more restrictive ‘custodianship’ model in British Columbia, and highlight the importance of communication between analysts in each jurisdiction to compensate for differences in coding variables and poor quality data. Researchers and system planners must have access to and be able to make effective use of administrative health data to ensure that Canadians continue to have access to high-quality health care and benefit from effective health policies. The considerable benefits of collaborative population-based research that spans jurisdictional borders have been recognized by the Canadian Institutes for Health Research in their recent call for the creation of a National Data Platform to resolve many of the issues in harmonization and validation of administrative data elements.

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