Big Data Challenges Book PDF, EPUB Download & Read Online Free

Big Data Challenges
Author: Anno Bunnik, Anthony Cawley, Michael Mulqueen, Andrej Zwitter
Publisher: Springer
ISBN: 1349948853
Pages: 140
Year: 2016-05-13
View: 414
Read: 500
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: Society, Security, Innovation and Ethics
Author: Anno Bunnik, Anthony Cawley, Michael Mulqueen
Publisher: Palgrave MacMillan
ISBN: 1349956678
Pages: 156
Year: 2018-05-27
View: 699
Read: 1325

Big Data Challenges
Author: Anno Bunnik, Anthony Cawley, Michael Mulqueen, Andrej Zwitter
Publisher: Palgrave
ISBN: 1349948845
Pages: 140
Year: 2016-05-13
View: 1256
Read: 1305
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
View: 609
Read: 787
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
View: 718
Read: 509
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 - Challenges for the Hospitality Industry
Author: Michael Toedt
Publisher: epubli
ISBN: 3844275932
Pages:
Year: 2013-12-12
View: 1104
Read: 279
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.
Practical Enterprise Data Lake Insights
Author: Saurabh Gupta, Venkata Giri
Publisher: Apress
ISBN: 1484235223
Pages: 327
Year: 2018-07-29
View: 1021
Read: 1230
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
Big Data
Author: Min Chen, Shiwen Mao, Yin Zhang, Victor CM Leung
Publisher: Springer
ISBN: 331906245X
Pages: 89
Year: 2014-05-05
View: 869
Read: 1025
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.
Medical Big Data and Internet of Medical Things
Author: Aboul Ella Hassanien, Nilanjan Dey, Surekha Borra
Publisher: CRC Press
ISBN: 1138492477
Pages: 340
Year: 2018-10-11
View: 378
Read: 613
This book addresses recent advances in mining, learning, and analysis of big volume of medical images. The book presents taxonomies, trends and issues such as veracity in distributive, dynamic, and diverse data collection, data management, data models, hypotheses testing, training, validation, model-building, optimization techniques and governance of medical big data collected from multiple, heterogeneous IoT devises, networks, platforms and systems such as private vs. public cloud. The book includes privacy, trust, and security issues related to medical Big Data and related IoT and presents case studies in healthcare analytics as well.
Big Data
Author: Cornelia Hammer, Ms.Diane C Kostroch, Mr.Gabriel Quiros
Publisher: International Monetary Fund
ISBN: 1484318994
Pages: 41
Year: 2017-09-13
View: 718
Read: 348
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.
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
View: 957
Read: 774
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.
The Big Data Challenges of Connectomics
Author:
Publisher:
ISBN:
Pages: 7
Year: 2014
View: 401
Read: 809
The structure of the nervous system is extraordinarily complicated because individual neurons are interconnected to hundreds or even thousands of other cells in networks that can extend over large volumes. Mapping such networks at the level of synaptic connections, a field called connectomics, began in the 1970s with a the study of the small nervous system of a worm and has recently garnered general interest thanks to technical and computational advances that automate the collection of electron-microscopy data and offer the possibility of mapping even large mammalian brains. However, modern connectomics produces 'big data', unprecedented quantities of digital information at unprecedented rates, and will require, as with genomics at the time, breakthrough algorithmic and computational solutions. Here in this paper we describe some of the key difficulties that may arise and provide suggestions for managing them.
Big Data Optimization: Recent Developments and Challenges
Author: Ali Emrouznejad
Publisher: Springer
ISBN: 3319302655
Pages: 487
Year: 2016-05-26
View: 398
Read: 374
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.
Hadoop Essentials
Author: Shiva Achari
Publisher: Packt Publishing Ltd
ISBN: 1784390461
Pages: 194
Year: 2015-04-29
View: 1214
Read: 577
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.
Challenges and Opportunity with Big Data
Author: Lin Zhang, Lei Ren, Fabrice Kordon
Publisher: Springer
ISBN: 3319619942
Pages: 209
Year: 2017-09-13
View: 1327
Read: 861
This book presents the thoroughly refereed and revised post-workshop proceedings of the 19th Monterey Workshop, held in Beijing, China, in Ocotber 2016. The workshop explored the challenges associated with the Development, Operation and Management of Large-Scale complex IT Systems. The 18 revised full papers presented were significantly extended and improved by the insights gained from the productive and lively discussions at the workshop, and the feedback from the post-workshop peer reviews. 2016 marks the 23rd anniversary for the Monterey Workshop series which started in 1993. For nearly a quarter of century, the Monterey Workshops have established themselves as an important international forum to foster, among academia, industry, and government agencies, discussion and exchange of ideas, research results and experience in developing software intensive systems, and have significantly advanced the field. The community of the workshop participants has grown to become an influential source of ideas and innovations and its impact on the knowledge economy has been felt worldwide.

Recently Visited