Big Data and Strategy: Theoretical Foundations and New Opportunities

Editorial for IEEE Access Special Section on Theoretical Foundations for Big Data Applications: Challenges and Opportunities

IEEE Access ◽

10.1109/access.2016.2605338 ◽

2016 ◽

Vol 4 ◽

pp. 5730-5732 ◽

Cited By ~ 1

Author(s):

Shui Yu ◽

Chonggang Wang ◽

Ke Liu ◽

Albert Y. Zomaya

Keyword(s):

Big Data ◽

Special Section ◽

Big Data Applications ◽

Challenges And Opportunities ◽

Theoretical Foundations

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Big data technology in education

Bulletin of the Karaganda University. Pedagogy series ◽

10.31489/2020ped4/8-15 ◽

2020 ◽

Vol 100 (4) ◽

pp. 8-15

Author(s):

M Serik ◽

◽

G Nurbekova ◽

J Kultan ◽

◽

...

Keyword(s):

Big Data ◽

Computer Science ◽

Intelligent Systems ◽

Professional Training ◽

Educational Process ◽

Educational Institutions ◽

National University ◽

The Internet Of Things ◽

Theoretical Foundations ◽

Big Data Technology

The article discusses the implementation of big data in the educational process of higher education. The authors, analyzing a large amount of data, referring to the types of services provided by e-government, indicate that there are many pressing problems, many services are not yet automated. In order to improve the professional training of teachers of Computer Science of the L.N. Gumilyov Eurasian National University, educational programs and courses have been developed 7M01514 — «Smart City technologies», «Big Data and cloud computing» and 7М01525 — «STEM-Education», «The Internet of Things and Intelligent Systems «on the theoretical and practical foundations of big data and introduced into the educational process. The arti-cle discusses several types of programs for teaching big data and analyzes data on the implementation of big data in some educational institutions. For the introduction and implementation of special courses in the educational process in the areas of magistracy in the educational program Computer Science, the curriculum, educational and methodological complex, digital educational resources are considered, as well as hardware and software that collects, stores, sorts big data, well as the introduction into the educational process of theoretical foundations and methods of using the developed technical and technological equipment.

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ABOUT SOFTWARE ENVIRONMENTS USING BIG DATA IN THE LEARNING PROCESS

Bulletin of Kazakh National Women's Teacher Training University ◽

10.52512/2306-5079-2021-85-1-76-83 ◽

2021 ◽

pp. 76-83

Author(s):

M. Serik ◽

G. F. Nurbekova ◽

B. B. Akhmetova

Keyword(s):

Big Data ◽

Educational Program ◽

Information Technologies ◽

Educational Process ◽

Educational Institutions ◽

Technological Equipment ◽

Network Controller ◽

National University ◽

Senior Students ◽

Theoretical Foundations

The article discusses several types of programs for teaching big data and analyzes data on the implementation of big data in some educational institutions. For the introduction and implementation of special courses in the educational process in the areas of magistracy in the educational program Computer Science, the curriculum, educational and methodological complex, digital educational resources are considered. As well as hardware and software that collects, stores, sorts big data, and the introduction into the educational process of theoretical foundations and methods of using the developed technical and technological equipment. This article reflects information on the conduct of a special course on the use of big data in the educational, educational process of senior students and undergraduates of the specialty "Informatics", the Faculty of Information Technologies, LN Gumilyov Eurasian National University. The first module of the course examines the ERA 500 network controller as an example of a turnstile network controller.

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Theoretical Foundations of Deep Resonance Interference Network

Security, Privacy, and Forensics Issues in Big Data - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-5225-9742-1.ch015 ◽

2020 ◽

pp. 340-362

Author(s):

Christophe Thovex

Keyword(s):

Machine Learning ◽

Big Data ◽

Deep Learning ◽

Reverse Engineer ◽

Finite Space ◽

Systemic Nature ◽

Hidden Data ◽

Probabilistic Machine Learning ◽

Digital Processes ◽

Theoretical Foundations

Digital processes for banks, insurances, or public services generate big data. Hidden networks and weak signals from frauds activities are sometimes statistically undetectable in the endogenous data respective to processes. The organic intelligence of human experts is able to reverse-engineer new fraud scenarios without statistically significant characteristics, but machine learning usually needs to be taught about them or fails to this task. Deep resonance interference network is a multidisciplinary attempt in probabilistic machine learning inspired from waves temporal reversal in finite space, introduced for big data analysis and hidden data mining. It proposes a theoretical alternative to artificial neural networks for deep learning. It is presented along with experimental outcomes related to fraudulent processes generating data statistically similar to legal endogenous data. Results show particular findings probably due to the systemic nature of the model, which appears closer to reasoning and intuition processes than to the perception processes mainly simulated in deep learning.

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Datafication: threat or opportunity for communication in the public sphere?

Journal of Communication Management ◽

10.1108/jcom-12-2014-0082 ◽

2016 ◽

Vol 20 (1) ◽

pp. 21-36 ◽

Cited By ~ 16

Author(s):

Derina Holtzhausen

Keyword(s):

Social Justice ◽

Big Data ◽

Discourse Analysis ◽

Public Sphere ◽

Strategic Communication ◽

Content Type ◽

Communication Practice ◽

The Public ◽

The Public Sphere ◽

Theoretical Foundations

Purpose – The purpose of this paper is to consider the threats and potential of Big Data for strategic communication. It explains the concepts of datafication and Big Data and establishes the social and cultural context of Big Data from the way those constructing algorithms superimpose their value systems and cultural references onto the data. It links Big Data and strategic communication through the segmentation devices and strategies both use and propose discourse analysis as a valid method for the critique of Big Data. The importance of strategic communication for the public sphere suggests that Big Data can pose a serious threat to public discourse. Design/methodology/approach – This is a conceptual and theoretical paper that first explains and interprets various new terms and concepts and then uses established theoretical approaches to analyze these phenomena. Findings – The use of Big Data for the micro-segmentation of audiences establishes its relationship with strategic communication. Big Data analyses and algorithms are not neutral. Treating algorithms as language and communication allow them to be subjected to discourse analysis to expose underlying power relations for resistance strategies to emerge. Strategic communicators should guard the public sphere and take an activist stance against the potential harm of Big Data. That requires a seat at the institutional technology table and speaking out against discriminatory practices. However, Big Data can also greatly benefit society and improve discourse in the public sphere. Research limitations/implications – There is not yet empirical data available on the impact of datafication on communication practice, which might be a problem well into the future. It also might be hard to do empirical research on its impact on practice and the public sphere. The heuristic value of this piece is that it laid down the theoretical foundations of the phenomena to be studied, which can in future be used for ethnographic research or qualitative studies. It might eventually be possible to follow personalized messages generated through datafication to study if they actually lead to behavior change in specific audience members. Practical/implications – As guardians of the public sphere strategic communication practitioners have to educate themselves on the realities of Big Data and should consciously acquire a seat at the institutional technology table. Practitioners will need to be involved in decisions on how algorithms are formulated and who they target. This will require them to serve as activists to ensure social justice. They also will need to contribute to organizational transparency by making organizational information widely available and accessible through media bought, owned, and earned. Strategic communicators need to create a binary partnership with journalists of all kinds to secure the public sphere. Social/implications – The paper exposes the role of algorithms in the construction of data and the extent to which algorithms are products of people who impose their own values and belief systems on them. Algorithms and the data they generate are subjective and value-laden. The concept of algorithms as language and communication and the use of Big Data for the segmentation of society for purposes of communication establish the connection between Big Data and strategic communication. The paper also exposes the potential for harm in the use of Big Data, as well as its potential for improving society and bringing about social justice. Originality/value – The value of this paper is that it introduces the concept of datafication to communication studies and proposes theoretical foundations for the study of Big Data in the context of strategic communications. It provides a theoretical and social foundation for the inclusion of the public sphere in a definition of strategic communication and emphasizes strategic communicators’ commitment to the public sphere as more important than ever before. It highlights how communication practice and society can impact each other positively and negatively and that Big Data should not be the future of strategic communication but only a part of it.

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On the Cognitive and Theoretical Foundations of Big Data Science and Engineering

New Mathematics and Natural Computation ◽

10.1142/s1793005717400026 ◽

2017 ◽

Vol 13 (02) ◽

pp. 101-117 ◽

Cited By ~ 2

Author(s):

Yingxu Wang

Keyword(s):

Big Data ◽

Data Science ◽

Recursive Formula ◽

Significant Finding ◽

Data Systems ◽

Basic Study ◽

Wide Range ◽

Big Data Systems ◽

Cognitive Foundations ◽

Theoretical Foundations

Big data play an indispensable role not only in the cognitive mechanisms of human sensation, quantification, qualification, estimation, memory, and reasoning, but also in a wide range of engineering applications. A basic study on the theoretical foundations of big data science is presented with a coherent set of general principles and analytic methodologies for big data systems. Cognitive foundations of big data are explored in order to formally explain the origination and nature of big data. A set of mathematical models of big data are created that rigorously elicit the general essences and patterns of big data across pervasive domains in sciences, engineering, and societies. A significant finding towards big data science is that big data systems in nature are a recursive [Formula: see text]-dimensional-typed hyperstructure (RNTHS) rather than pure numbers. The fundamental topological property of big data reveals a set of denotational mathematical solutions for dealing with inherited complexities and unprecedented challenges in big data engineering.

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An Adaptable Big Data Value Chain Framework for End-to-End Big Data Monetization

Big Data and Cognitive Computing ◽

10.3390/bdcc4040034 ◽

2020 ◽

Vol 4 (4) ◽

pp. 34

Author(s):

Abou Zakaria Faroukhi ◽

Imane El Alaoui ◽

Youssef Gahi ◽

Aouatif Amine

Keyword(s):

Big Data ◽

Value Chain ◽

Business Models ◽

Data Driven ◽

Business Operations ◽

Data Value ◽

End To End ◽

Almost All ◽

Theoretical Foundations ◽

Support Decision Making

Today, almost all active organizations manage a large amount of data from their business operations with partners, customers, and even competitors. They rely on Data Value Chain (DVC) models to handle data processes and extract hidden values to obtain reliable insights. With the advent of Big Data, operations have become increasingly more data-driven, facing new challenges related to volume, variety, and velocity, and giving birth to another type of value chain called Big Data Value Chain (BDVC). Organizations have become increasingly interested in this kind of value chain to extract confined knowledge and monetize their data assets efficiently. However, few contributions to this field have addressed the BDVC in a synoptic way by considering Big Data monetization. This paper aims to provide an exhaustive and expanded BDVC framework. This end-to-end framework allows us to handle Big Data monetization to make organizations’ processes entirely data-driven, support decision-making, and facilitate value co-creation. For this, we present a comprehensive review of existing BDVC models relying on some definitions and theoretical foundations of data monetization. Next, we expose research carried out on data monetization strategies and business models. Then, we offer a global and generic BDVC framework that supports most of the required phases to achieve data valorization. Furthermore, we present both a reduced and full monetization model to support many co-creation contexts along the BDVC.

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