scholarly journals Analytical Techniques for Decision Making on Information Security for Big Data Breaches

2018 ◽  
Vol 17 (02) ◽  
pp. 527-545 ◽  
Author(s):  
Aiiad Albeshri ◽  
Vijey Thayananthan
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Information Security ◽  
Access Control ◽  
Key Management ◽  
Analytical Techniques ◽  
Data Infrastructure ◽  
Online Systems ◽  
Data Breaches ◽  
Priority Model

In the big data processes, management and analytics are primary areas where we can introduce the decision making on information security to mitigate the big data breaches. According to the growing number of online systems and big data handling, mitigating the big data breaches is the serious problem during the processing period which needs to be monitored using appropriate technique. The goal of this research is to prevent the big data breaches using correct decision making based on information security concepts such as access control with authentication which depend on the management policies. The analytical approach of information security solution can also be useful for securing the big data infrastructure and key management that improve the big data breaches. As an analytical method, information security which focuses on detecting and securing the big data breaches is considered with access control. Here, we have introduced the multi-priority model influenced with the network calculus and access control which monitors the breaches during the big data processing. In the results and analysis, we can provide a graph which shows the monitoring improvement for decision making during the mitigation of big data breaches.

Innovation Platforms

2018 ◽  
pp. 72-95
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Business Model ◽  
Value Chain ◽  
Analytical Techniques ◽  
Human Interaction ◽  
Business Relationship ◽  
Potential Relationship ◽  
Relationship Of ◽  
Cross Fertilization

Data and analytics is the heart of a digital business platform. Today, big data (BD) becomes useful when it enriches decision making that is enhanced by application of analytical techniques and some element of human interaction. With the merging of data and information vs. knowledge and intelligence, this chapter investigates an opportunity for cross-fertilization between BD and the field of digital business with related disciplines. Primary BD and analytics platform is a set of business capabilities. This chapter aims to investigate the potential relationship of BD and analytics platform and digital business platform. In doing so, it develops a BD value chain framework, BD business model pattern (BDBMP) with related levels of BD maturity improvement. This framework could be used to find answers on the basic BD and digital business relationship questions.

Analytics based decision making

2014 ◽  
Vol 6 (4) ◽  
pp. 332-340 ◽  
Author(s):  
Deepak Agrawal
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Design Methodology ◽  
Historical Perspective ◽  
Big Data Analytics ◽  
Analytical Techniques ◽  
Content Type ◽  
Informed Decisions ◽  
Different Types

Purpose – This paper aims to trace the history, application areas and users of Classical Analytics and Big Data Analytics. Design/methodology/approach – The paper discusses different types of Classical and Big Data Analytical techniques and application areas from the early days to present day. Findings – Businesses can benefit from a deeper understanding of Classical and Big Data Analytics to make better and more informed decisions. Originality/value – This is a historical perspective from the early days of analytics to present day use of analytics.

scholarly journals RELATIONSHIP BETWEEN BIG DATA AND DECISION SUPPORT SYSTEMS

2020 ◽  
Vol 11 (1) ◽  
pp. 82-93
Author(s):  
Gustavo Grander ◽  
Luciano Ferreira da Silva ◽  
Alan Tadeu Moraes Moraes ◽  
Paulo Sergio Gonçalves de Oliveira
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Analytical Techniques ◽  
Strong Relationship ◽  
Decision Making Processes ◽  
Scopus Database

This article aimed to identify relationships between Big Data and Decision Support Systems. For this, we conducted a search in the Scopus database and as a result, we identified a report according to the increased frequency of publications, frequency of publications in journals and, using the VOSviewer software, we performed an analysis of words co-citation. We identified 5 groups of keywords that suggest different areas of study (e.g., logistics, health and social media), as well as a more recent focus on studies aimed at sustainable development, machine learning, analytical techniques and decision-making processes decision. An important contribution that should also be highlighted was the strong relationship between the keywords Big Data, artificial intelligence and decision making, suggesting studies involving the three terms in a large number of works. 

scholarly journals Big data for small businesses: Abstracting security and decision-making tools

10.29007/6tpw ◽  
2019 ◽  
Author(s):  
Sara Salih ◽  
Kennedy Njenga
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Information Security ◽  
Small Businesses ◽  
Theoretical Underpinning ◽  
Substantive Theory ◽  
Business Decision ◽  
Data Usage ◽  
The Relationship ◽  
Financial Consideration

The study delineates the understanding of big data as an emergent phenomenon that has brought a notable shift in the relationship between technology and business decision- making. Using grounded theory techniques, the study espouses opportunities and alternative perceptions from small businesses regarding the value that big data may offer in contrast to usage experience by big businesses. Information security lies at the heart of these consideration. The study draws on concepts and tenets from the discipline of information security to support a theoretical underpinning for big data usage in small businesses. A substantive theory has been developed from this work with three distinct concepts emerging that show that financial consideration, management mindset and size consideration play a big part in influencing small business perceptions.

scholarly journals A Single-Label Model to Ensure Data Consistency in Information Security

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Cigdem Bakir
Keyword(s):  
Experimental Study ◽  
Big Data ◽  
Information Security ◽  
Access Control ◽  
Real Data ◽  
Data Integrity ◽  
Data Consistency ◽  
Flexible Structure ◽  
Data Confidentiality ◽  
Unauthorized Access

Information security is defined as preventing actions such as unauthorized access and use, modification, and removal of information. It consists of certain basic elements of confidentiality, integrity, and accessibility. There are numerous studies in published literature which have been conducted to ensure information security. However, there is no previous study that covers these three basic elements together. In the present study, a model that includes these three key elements of information security together for big data was proposed and implemented. With this proposed “single-label model,” a more practical and flexible structure was established for all operations (read, write, update, and delete) performed on a database on real data. In previous studies conducted with a label model, separate labels were used for read-only or write-only operations, and there was no structure that could ensure both confidentiality and integrity at the same time. The present study, however, shows what type of authorization and access control could be established between which processes and which users by looking at a single label for all the operations performed on the data. Thus, in contrast to the previous studies seen in published literature, data confidentiality, data integrity, and data consistency were all guaranteed for all transactions. The results of the proposed single-label model were also shown comparatively by conducting an experimental study of its application. The results obtained are promising for further studies.

Big Data and the Paradox of Diversity

2016 ◽  
Vol 2 (2) ◽  
pp. 39-54 ◽  
Author(s):  
Bernhard Rieder
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Analytical Techniques ◽  
Guiding Principle ◽  
Societal Context ◽  
Traditional Societies ◽  
The Many

Abstract This paper develops a critique of Big Data and associated analytical techniques by focusing not on errors - skewed or imperfect datasets, false positives, underrepresentation, and so forth - but on data mining that works. After a quick framing of these practices as interested readings of reality, I address the question of how data analytics and, in particular, machine learning reveal and operate on the structured and unequal character of contemporary societies, installing “economic morality” (Allen 2012) as the central guiding principle. Rather than critiquing the methods behind Big Data, I inquire into the way these methods make the many differences in decentred, non-traditional societies knowable and, as a consequence, ready for profitable distinction and decision-making. The objective, in short, is to add to our understanding of the “profound ideological role at the intersection of sociality, research, and commerce” (van Dijck 2014: 201) the collection and analysis of large quantities of multifarious data have come to play. Such an understanding needs to embed Big Data in a larger, more fundamental critique of the societal context it operates in.

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