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Big Data Challenges
Author: Anno Bunnik, Anthony Cawley, Michael Mulqueen, Andrej Zwitter
Publisher: Springer
ISBN: 1349948853
Pages: 140
Year: 2016-05-13
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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.
Big Data Challenges
Author: Anno Bunnik, Anthony Cawley, Michael Mulqueen, Andrej Zwitter
Publisher: Palgrave
ISBN: 1349948845
Pages: 140
Year: 2016-05-13
View: 764
Read: 305
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.
Applications of Big Data Analytics
Author: Mohammed M. Alani, Hissam Tawfik, Mohammed Saeed, Obinna Anya
Publisher: Springer
ISBN: 3319764721
Pages: 214
Year: 2018-07-23
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This timely text/reference reviews the state of the art of big data analytics, with a particular focus on practical applications. An authoritative selection of leading international researchers present detailed analyses of existing trends for storing and analyzing big data, together with valuable insights into the challenges inherent in current approaches and systems. This is further supported by real-world examples drawn from a broad range of application areas, including healthcare, education, and disaster management. The text also covers, typically from an application-oriented perspective, advances in data science in such areas as big data collection, searching, analysis, and knowledge discovery. Topics and features: Discusses a model for data traffic aggregation in 5G cellular networks, and a novel scheme for resource allocation in 5G networks with network slicing Explores methods that use big data in the assessment of flood risks, and apply neural networks techniques to monitor the safety of nuclear power plants Describes a system which leverages big data analytics and the Internet of Things in the application of drones to aid victims in disaster scenarios Proposes a novel deep learning-based health data analytics application for sleep apnea detection, and a novel pathway for diagnostic models of headache disorders Reviews techniques for educational data mining and learning analytics, and introduces a scalable MapReduce graph partitioning approach for high degree vertices Presents a multivariate and dynamic data representation model for the visualization of healthcare data, and big data analytics methods for software reliability assessment This practically-focused volume is an invaluable resource for all researchers, academics, data scientists and business professionals involved in the planning, designing, and implementation of big data analytics projects. Dr. Mohammed M. Alani is an Associate Professor in Computer Engineering and currently is the Provost at Al Khawarizmi International College, Abu Dhabi, UAE. Dr. Hissam Tawfik is a Professor of Computer Science in the School of Computing, Creative Technologies & Engineering at Leeds Beckett University, UK. Dr. Mohammed Saeed is a Professor in Computing and currently is the Vice President for Academic Affairs and Research at the University of Modern Sciences, Dubai, UAE. Dr. Obinna Anya is a Research Staff Member at IBM Research – Almaden, San Jose, CA, USA.
Big Data in Complex Systems
Author: Aboul-Ella Hassanien, Ahmad Taher Azar, Vaclav Snasel, Janusz Kacprzyk, Jemal H. Abawajy
Publisher: Springer
ISBN: 331911056X
Pages: 499
Year: 2015-01-02
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This volume provides challenges and Opportunities with updated, in-depth material on the application of Big data to complex systems in order to find solutions for the challenges and problems facing big data sets applications. Much data today is not natively in structured format; for example, tweets and blogs are weakly structured pieces of text, while images and video are structured for storage and display, but not for semantic content and search. Therefore transforming such content into a structured format for later analysis is a major challenge. Data analysis, organization, retrieval, and modeling are other foundational challenges treated in this book. The material of this book will be useful for researchers and practitioners in the field of big data as well as advanced undergraduate and graduate students. Each of the 17 chapters in the book opens with a chapter abstract and key terms list. The chapters are organized along the lines of problem description, related works, and analysis of the results and comparisons are provided whenever feasible.
Big Data, Big Challenges in Evidence-Based Policy Making
Author: H. Kumar Jayasuriya, Kathryn Ritcheske
Publisher: West Academic Publishing
ISBN: 1634594525
Pages: 262
Year: 2015-09-25
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Big Data, Big Challenges in Evidence-Based Policy Making is a multi-disciplinary study of how to glean insights from massive data sets to make better public policy decisions. Using a combination of explanatory material, specific examples, and practical suggestions, the book teaches readers how to preserve, use, and publish big data. Each chapter provides real-life examples of how big data can be used in policy making. The book also provides practical insights from archivists and librarians who are on the forefront of preserving data and helping researchers find needed data. To complete the discussion of big data, the book provides a frank and nuanced discussion of privacy risks involved with big data. It also examines the political constraints on how to regulate privacy. In addition, the book offers a comparative review of privacy by examining the different privacy protections in the US and the EU, as well as the delicate system of trading private data between nations. This book can be used to supplement upper level law school courses as well as courses on public health, economics, political science, environmental studies, and information science. The contributors are: Margaret O'Neill Adams, Judith Amsalem, Paula Avila-Guillen, Ana Ayala, Tanya Baytor, Josh Blackman, Linda K. Breggin, Dianne Callan, Christin Cave, Kristofer A. Ekdahl, Francine E. Friedman, Aliza Glasner, Carole Roan Gresenz, James Grimmelmann, Mark D. Johnson, Leslie Johnston, Susan C. Kim, John D. Kraemer, William G. LeFurgy, Jared Lyle, Kathryn Mengerink, Elizabeth Moss, Catherine Powell, Jason S. Roffenbender, Joshua C. Teitelbaum, Matthew C. Thomas, and Zachary Turk.
Big Data
Author: Min Chen, Shiwen Mao, Yin Zhang, Victor CM Leung
Publisher: Springer
ISBN: 331906245X
Pages: 89
Year: 2014-05-05
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This Springer Brief provides a comprehensive overview of the background and recent developments of big data. The value chain of big data is divided into four phases: data generation, data acquisition, data storage and data analysis. For each phase, the book introduces the general background, discusses technical challenges and reviews the latest advances. Technologies under discussion include cloud computing, Internet of Things, data centers, Hadoop and more. The authors also explore several representative applications of big data such as enterprise management, online social networks, healthcare and medical applications, collective intelligence and smart grids. This book concludes with a thoughtful discussion of possible research directions and development trends in the field. Big Data: Related Technologies, Challenges and Future Prospects is a concise yet thorough examination of this exciting area. It is designed for researchers and professionals interested in big data or related research. Advanced-level students in computer science and electrical engineering will also find this book useful.
Big Data - Challenges for the Hospitality Industry
Author: Michael Toedt
Publisher: epubli
ISBN: 3844275932
Pages:
Year: 2013-12-12
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Hotel companies who are able to deal with Big Data will create a sustainable competitive advantage. But what is Big Data and how can we use it in the Hospitality Industry? What is the distinctive value and what are key areas for a successful implementation? Michael Toedt explains in this book the biggest hurdle – the lack of knowledge within the senior management and the willingness to implement the necessary changes – and gives hoteliers recommendations to avoid the main failures.
Big Data Optimization: Recent Developments and Challenges
Author: Ali Emrouznejad
Publisher: Springer
ISBN: 3319302655
Pages: 487
Year: 2016-05-26
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The main objective of this book is to provide the necessary background to work with big data by introducing some novel optimization algorithms and codes capable of working in the big data setting as well as introducing some applications in big data optimization for both academics and practitioners interested, and to benefit society, industry, academia, and government. Presenting applications in a variety of industries, this book will be useful for the researchers aiming to analyses large scale data. Several optimization algorithms for big data including convergent parallel algorithms, limited memory bundle algorithm, diagonal bundle method, convergent parallel algorithms, network analytics, and many more have been explored in this book.
Big Data
Author: Cornelia Hammer, Ms.Diane C Kostroch, Mr.Gabriel Quiros
Publisher: International Monetary Fund
ISBN: 1484318994
Pages: 41
Year: 2017-09-13
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Big data are part of a paradigm shift that is significantly transforming statistical agencies, processes, and data analysis. While administrative and satellite data are already well established, the statistical community is now experimenting with structured and unstructured human-sourced, process-mediated, and machine-generated big data. The proposed SDN sets out a typology of big data for statistics and highlights that opportunities to exploit big data for official statistics will vary across countries and statistical domains. To illustrate the former, examples from a diverse set of countries are presented. To provide a balanced assessment on big data, the proposed SDN also discusses the key challenges that come with proprietary data from the private sector with regard to accessibility, representativeness, and sustainability. It concludes by discussing the implications for the statistical community going forward.
Big Data Challenges and Opportunities
Author: Ceren Budak
Publisher:
ISBN: 1267933631
Pages: 257
Year: 2012
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While identifying influentials in social networks, we leverage data-driven methods. When modeling diffusion of information and user behavior, we rely on statistical methods and theories from social science literature. Given a solid understanding of information diffusion in social networks, we can focus on various applications. Discrete math optimization techniques provide us an optimal direction to limiting the spread of misinformation in social networks. And finally, we rely on data streams solutions for building an informational trend detection framework in social networks. Throughout our studies, we focus on various networks such as Twitter, Digg, Facebook and the Blogosphere.
Hadoop Essentials
Author: Shiva Achari
Publisher: Packt Publishing Ltd
ISBN: 1784390461
Pages: 194
Year: 2015-04-29
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If you are a system or application developer interested in learning how to solve practical problems using the Hadoop framework, then this book is ideal for you. This book is also meant for Hadoop professionals who want to find solutions to the different challenges they come across in their Hadoop projects.
HCI Challenges and Privacy Preservation in Big Data Security
Author: Lopez, Daphne, Durai, M.A. Saleem
Publisher: IGI Global
ISBN: 1522528644
Pages: 275
Year: 2017-08-10
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Privacy protection within large databases can be a challenge. By examining the current problems and challenges this domain is facing, more efficient strategies can be established to safeguard personal information against invasive pressures. HCI Challenges and Privacy Preservation in Big Data Security is an informative scholarly publication that discusses how human-computer interaction impacts privacy and security in almost all sectors of modern life. Featuring relevant topics such as large scale security data, threat detection, big data encryption, and identity management, this reference source is ideal for academicians, researchers, advanced-level students, and engineers that are interested in staying current on the advancements and drawbacks of human-computer interaction within the world of big data.
Practical Enterprise Data Lake Insights
Author: Saurabh Gupta, Venkata Giri
Publisher: Apress
ISBN: 1484235223
Pages: 327
Year: 2018-07-29
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Use this practical guide to successfully handle the challenges encountered when designing an enterprise data lake and learn industry best practices to resolve issues. When designing an enterprise data lake you often hit a roadblock when you must leave the comfort of the relational world and learn the nuances of handling non-relational data. Starting from sourcing data into the Hadoop ecosystem, you will go through stages that can bring up tough questions such as data processing, data querying, and security. Concepts such as change data capture and data streaming are covered. The book takes an end-to-end solution approach in a data lake environment that includes data security, high availability, data processing, data streaming, and more. Each chapter includes application of a concept, code snippets, and use case demonstrations to provide you with a practical approach. You will learn the concept, scope, application, and starting point. What You'll Learn Get to know data lake architecture and design principles Implement data capture and streaming strategies Implement data processing strategies in Hadoop Understand the data lake security framework and availability model Who This Book Is For Big data architects and solution architects
Application of Big Data for National Security
Author: Babak Akhgar, Gregory B. Saathoff, Hamid R Arabnia, Richard Hill, Andrew Staniforth, Petra Saskia Bayerl
Publisher: Butterworth-Heinemann
ISBN: 0128019735
Pages: 316
Year: 2015-02-19
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Application of Big Data for National Security provides users with state-of-the-art concepts, methods, and technologies for Big Data analytics in the fight against terrorism and crime, including a wide range of case studies and application scenarios. This book combines expertise from an international team of experts in law enforcement, national security, and law, as well as computer sciences, criminology, linguistics, and psychology, creating a unique cross-disciplinary collection of knowledge and insights into this increasingly global issue. The strategic frameworks and critical factors presented in Application of Big Data for National Security consider technical, legal, ethical, and societal impacts, but also practical considerations of Big Data system design and deployment, illustrating how data and security concerns intersect. In identifying current and future technical and operational challenges it supports law enforcement and government agencies in their operational, tactical and strategic decisions when employing Big Data for national security Contextualizes the Big Data concept and how it relates to national security and crime detection and prevention Presents strategic approaches for the design, adoption, and deployment of Big Data technologies in preventing terrorism and reducing crime Includes a series of case studies and scenarios to demonstrate the application of Big Data in a national security context Indicates future directions for Big Data as an enabler of advanced crime prevention and detection
Internet of Things and Big Data Analysis: Recent Trends and Challenges
Author: Ali Al-Sabbagh
Publisher:
ISBN: 0692809929
Pages: 304
Year: 2016-11-13
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Chapter One: Overview of Internet of Things (IoT): This chapter describes the definition of IoT. The term Internet of Things (IoT) or Internet of Everything (IoE) is still ambiguous. There is no single unified definition of what it really is. However, it can be defined by elaborating what it can provide. The Internet of Things is thought to be the next evolution of the Internet as it provides a networking infrastructure allowing for trillions of devices to collect data and communicate with each other to make processed smart decisions. Chapter Two: Challenges and Applications of (IoT): This chapter discusses some of the major IoT applications that have the potential to bring transformation in our future trends. The rapid development of these applications is expected to face numerous challenges. This chapter brings key challenges into focus and discusses potential barriers that could impede the rapid adoption of IoT. Chapter Three: Explosion of Data (Big Data): This chapter elaborates on data management, which requires the process of transferring data in an efficient way, upon the user's demand. Therefore, the right data must reach the right user at the right time, in order to be valuable. Data comes from many different sources, i.e. web apps, sensor networks and many others. This data needs to be collected, categorized, stored, and analyzed in order to get an insight of its content and hence present it in an efficient way. Chapter Four: Boosted Prediction Analysis for Big Data: Prediction techniques represent a useful tool for knowledge discovery in a massive and complex healthcare dataset. In this chapter, a prediction model has been designed and implemented which analyzes medical records of patients and provides information for decision making in health institutes. The proposed model consists of three primary stages, the first being preprocessing data that focuses on preparing the information for the mining process. Chapter Five: IoT Security: This chapter outlines existing security approaches being used for IoT, together with the weaknesses they inherit. Since the security of IoT communications could be addressed in the context of the communication protocol itself, we focus on existing protocols and mechanisms used to secure communications involved in this vital subject. Chapter Six: Threat Taxonomy for Cloud of Things: In this chapter we present a comprehensive threat model which is then utilized to create a first-ever threat taxonomy for the Cloud of Things. This taxonomy outlines different security and privacy threats faced by this nascent technology and can be used as the basis for further research on security and privacy in the Cloud of Things. Chapter Seven: Smart Homes Based On Smart Cities' Design Patterns: This chapter reviews smart cities for the Internet of Things. It discusses maximizing the efficiency of distribution and consumption of energy from one point of view and a vision for smart cities in the future from another point of view. Moreover, it presents the design for a smart home and ends up with a proposed system, as a case study. Chapter Eight: Social-Sensor Networks: This chapter deals with the integration of Wireless Sensor Networks (WSNs) and Social Networks. Nowadays, WSNs have caused a paradigm shift in our society. They have become a popular mean of communication among people. Many aspects of our lives are significantly related to the WSNs, such as communication, transportation, military, and agriculture. Chapter Nine: 5G Driving Global IoT: This chapter explores the IoT literature in terms of the communication technology involved: 4G-LTE-A. Additionally, an outline for improving the IoT of future keys in current cellular systems is discussed. This work exhibits how the current 4G LTE-A frameworks can contribute to the design of smart cities. Furthermore, an overview of 4G and 5G is presented.

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