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Subtle Challenges of Big Data

Subtle Challenges of Big Data PDF Author: Abdallah Bari
Publisher: Independently Published
ISBN: 9781521580929
Category :
Languages : en
Pages : 183

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Book Description
Big Data has created radical shifts, in less than a decade, with implications that are more subtle than they appear. While the technology is taking far major leaps ahead creating an unprecedented amount of data there is an urgent need to address Big Data' subtle implications and challenges related for instance to the establishment of mathematical theoretical frameworks to scale inferences and machine learning algorithms. Big Data has shown its tremendous potential to transform industries, such as healthcare and insurance industries, and to empower artificial intelligence and machine learning at an unequivocal scale, today. However, there are concerns that Big Data may lose much of its usefulness, potentially generating new unintended consequences if epistemological (knowledge generation) challenges are not addressed. Big Data has grown tremendously rapidly leading to data to outpace concepts. Conceptual investigations and mathematical frameworks are to theory formulation what methodology is to Big Data gathering and Big Data analytics. A lack of conceptual frameworks to address epistemological challenges of Big Data may slow progress in innovations and delay the development of Big Data's prospective applications according to recent reports and publications on Big Data. There is an urgent need to address Big Data's epistemological challenges along with technological challenges, in both public and private sectors, and to catch up with both shortages in skills and concepts to better leverage Big Data for our increasingly data-driven society.

Subtle Challenges of Big Data

Subtle Challenges of Big Data PDF Author: Abdallah Bari
Publisher: Independently Published
ISBN: 9781521580929
Category :
Languages : en
Pages : 183

View

Book Description
Big Data has created radical shifts, in less than a decade, with implications that are more subtle than they appear. While the technology is taking far major leaps ahead creating an unprecedented amount of data there is an urgent need to address Big Data' subtle implications and challenges related for instance to the establishment of mathematical theoretical frameworks to scale inferences and machine learning algorithms. Big Data has shown its tremendous potential to transform industries, such as healthcare and insurance industries, and to empower artificial intelligence and machine learning at an unequivocal scale, today. However, there are concerns that Big Data may lose much of its usefulness, potentially generating new unintended consequences if epistemological (knowledge generation) challenges are not addressed. Big Data has grown tremendously rapidly leading to data to outpace concepts. Conceptual investigations and mathematical frameworks are to theory formulation what methodology is to Big Data gathering and Big Data analytics. A lack of conceptual frameworks to address epistemological challenges of Big Data may slow progress in innovations and delay the development of Big Data's prospective applications according to recent reports and publications on Big Data. There is an urgent need to address Big Data's epistemological challenges along with technological challenges, in both public and private sectors, and to catch up with both shortages in skills and concepts to better leverage Big Data for our increasingly data-driven society.

Data Intensive Computing Applications for Big Data

Data Intensive Computing Applications for Big Data PDF Author: M. Mittal
Publisher: IOS Press
ISBN: 1614998140
Category : Computers
Languages : en
Pages : 620

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Book Description
The book ‘Data Intensive Computing Applications for Big Data’ discusses the technical concepts of big data, data intensive computing through machine learning, soft computing and parallel computing paradigms. It brings together researchers to report their latest results or progress in the development of the above mentioned areas. Since there are few books on this specific subject, the editors aim to provide a common platform for researchers working in this area to exhibit their novel findings. The book is intended as a reference work for advanced undergraduates and graduate students, as well as multidisciplinary, interdisciplinary and transdisciplinary research workers and scientists on the subjects of big data and cloud/parallel and distributed computing, and explains didactically many of the core concepts of these approaches for practical applications. It is organized into 24 chapters providing a comprehensive overview of big data analysis using parallel computing and addresses the complete data science workflow in the cloud, as well as dealing with privacy issues and the challenges faced in a data-intensive cloud computing environment. The book explores both fundamental and high-level concepts, and will serve as a manual for those in the industry, while also helping beginners to understand the basic and advanced aspects of big data and cloud computing.

Data Mining and Knowledge Discovery for Big Data

Data Mining and Knowledge Discovery for Big Data PDF Author: Wesley W. Chu
Publisher: Springer Science & Business Media
ISBN: 3642408370
Category : Technology & Engineering
Languages : en
Pages : 311

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Book Description
The field of data mining has made significant and far-reaching advances over the past three decades. Because of its potential power for solving complex problems, data mining has been successfully applied to diverse areas such as business, engineering, social media, and biological science. Many of these applications search for patterns in complex structural information. In biomedicine for example, modeling complex biological systems requires linking knowledge across many levels of science, from genes to disease. Further, the data characteristics of the problems have also grown from static to dynamic and spatiotemporal, complete to incomplete, and centralized to distributed, and grow in their scope and size (this is known as big data). The effective integration of big data for decision-making also requires privacy preservation. The contributions to this monograph summarize the advances of data mining in the respective fields. This volume consists of nine chapters that address subjects ranging from mining data from opinion, spatiotemporal databases, discriminative subgraph patterns, path knowledge discovery, social media, and privacy issues to the subject of computation reduction via binary matrix factorization.

Big Data at Work

Big Data at Work PDF Author: Scott Tonidandel
Publisher: Routledge
ISBN: 1317702700
Category : Psychology
Languages : en
Pages : 382

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Book Description
The amount of data in our world has been exploding, and analyzing large data sets—so called big data—will become a key basis of competition in business. Statisticians and researchers will be updating their analytic approaches, methods and research to meet the demands created by the availability of big data. The goal of this book is to show how advances in data science have the ability to fundamentally influence and improve organizational science and practice. This book is primarily designed for researchers and advanced undergraduate and graduate students in psychology, management and statistics.

Big Data Analytics

Big Data Analytics PDF Author: V. B. Aggarwal
Publisher: Springer
ISBN: 9811066205
Category : Computers
Languages : en
Pages : 766

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Book Description
This volume comprises the select proceedings of the annual convention of the Computer Society of India. Divided into 10 topical volumes, the proceedings present papers on state-of-the-art research, surveys, and succinct reviews. The volumes cover diverse topics ranging from communications networks to big data analytics, and from system architecture to cyber security. This volume focuses on Big Data Analytics. The contents of this book will be useful to researchers and students alike.

Combating Security Challenges in the Age of Big Data

Combating Security Challenges in the Age of Big Data PDF Author: Zubair Md. Fadlullah
Publisher: Springer Nature
ISBN: 3030356426
Category : Computers
Languages : en
Pages : 266

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Book Description
This book addresses the key security challenges in the big data centric computing and network systems, and discusses how to tackle them using a mix of conventional and state-of-the-art techniques. The incentive for joining big data and advanced analytics is no longer in doubt for businesses and ordinary users alike. Technology giants like Google, Microsoft, Amazon, Facebook, Apple, and companies like Uber, Airbnb, NVIDIA, Expedia, and so forth are continuing to explore new ways to collect and analyze big data to provide their customers with interactive services and new experiences. With any discussion of big data, security is not, however, far behind. Large scale data breaches and privacy leaks at governmental and financial institutions, social platforms, power grids, and so forth, are on the rise that cost billions of dollars. The book explains how the security needs and implementations are inherently different at different stages of the big data centric system, namely at the point of big data sensing and collection, delivery over existing networks, and analytics at the data centers. Thus, the book sheds light on how conventional security provisioning techniques like authentication and encryption need to scale well with all the stages of the big data centric system to effectively combat security threats and vulnerabilities. The book also uncovers the state-of-the-art technologies like deep learning and blockchain which can dramatically change the security landscape in the big data era.

Big Data Challenges

Big Data Challenges PDF Author: Anno Bunnik
Publisher: Springer
ISBN: 1349948853
Category : Political Science
Languages : en
Pages : 140

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Book Description
This book brings together an impressive range of academic and intelligence professional perspectives to interrogate the social, ethical and security upheavals in a world increasingly driven by data. Written in a clear and accessible style, it offers fresh insights to the deep reaching implications of Big Data for communication, privacy and organisational decision-making. It seeks to demystify developments around Big Data before evaluating their current and likely future implications for areas as diverse as corporate innovation, law enforcement, data science, journalism, and food security. The contributors call for a rethinking of the legal, ethical and philosophical frameworks that inform the responsibilities and behaviours of state, corporate, institutional and individual actors in a more networked, data-centric society. In doing so, the book addresses the real world risks, opportunities and potentialities of Big Data.

Applied Data Science

Applied Data Science PDF Author: Martin Braschler
Publisher: Springer
ISBN: 3030118215
Category : Computers
Languages : en
Pages : 465

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Book Description
This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry.

Big Data in Cognitive Science

Big Data in Cognitive Science PDF Author: Michael N. Jones
Publisher: Psychology Press
ISBN: 1315413558
Category : Psychology
Languages : en
Pages : 374

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Book Description
While laboratory research is the backbone of collecting experimental data in cognitive science, a rapidly increasing amount of research is now capitalizing on large-scale and real-world digital data. Each piece of data is a trace of human behavior and offers us a potential clue to understanding basic cognitive principles. However, we have to be able to put the pieces together in a reasonable way, which necessitates both advances in our theoretical models and development of new methodological techniques. The primary goal of this volume is to present cutting-edge examples of mining large-scale and naturalistic data to discover important principles of cognition and evaluate theories that would not be possible without such a scale. This book also has a mission to stimulate cognitive scientists to consider new ways to harness big data in order to enhance our understanding of fundamental cognitive processes. Finally, this book aims to warn of the potential pitfalls of using, or being over-reliant on, big data and to show how big data can work alongside traditional, rigorously gathered experimental data rather than simply supersede it. In sum, this groundbreaking volume presents cognitive scientists and those in related fields with an exciting, detailed, stimulating, and realistic introduction to big data – and to show how it may greatly advance our understanding of the principles of human memory, perception, categorization, decision-making, language, problem-solving, and representation.

Security, Privacy, and Forensics Issues in Big Data

Security, Privacy, and Forensics Issues in Big Data PDF Author: Joshi, Ramesh C.
Publisher: IGI Global
ISBN: 1522597441
Category : Computers
Languages : en
Pages : 456

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Book Description
With the proliferation of devices connected to the internet and connected to each other, the volume of data collected, stored, and processed is increasing every day, which brings new challenges in terms of information security. As big data expands with the help of public clouds, traditional security solutions tailored to private computing infrastructures and confined to a well-defined security perimeter, such as firewalls and demilitarized zones (DMZs), are no longer effective. New security functions are required to work over the heterogenous composition of diverse hardware, operating systems, and network domains. Security, Privacy, and Forensics Issues in Big Data is an essential research book that examines recent advancements in big data and the impact that these advancements have on information security and privacy measures needed for these networks. Highlighting a range of topics including cryptography, data analytics, and threat detection, this is an excellent reference source for students, software developers and engineers, security analysts, IT consultants, academicians, researchers, and professionals.