Beyond Big Data

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Beyond Big Data

Author : Martin Oberhofer
Publisher : Pearson Education
ISBN 13 : 013350980X
Total Pages : 261 pages
Book Rating : 4.1/5 (335 download)

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Book Synopsis Beyond Big Data by : Martin Oberhofer

Download or read book Beyond Big Data written by Martin Oberhofer and published by Pearson Education. This book was released on 2015 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: Drive Powerful Business Value by Extending MDM to Social, Mobile, Local, and Transactional Data Enterprises have long relied on Master Data Management (MDM) to improve customer-related processes. But MDM was designed primarily for structured data. Today, crucial information is increasingly captured in unstructured, transactional, and social formats: from tweets and Facebook posts to call center transcripts. Even with tools like Hadoop, extracting usable insight is difficult--often, because it's so difficult to integrate new and legacy data sources. In Beyond Big Data, five of IBM's leading data management experts introduce powerful new ways to integrate social, mobile, location, and traditional data. Drawing on pioneering experience with IBM's enterprise customers, they show how Social MDM can help you deepen relationships, improve prospect targeting, and fully engage customers through mobile channels. Business leaders and practitioners will discover powerful new ways to combine social and master data to improve performance and uncover new opportunities. Architects and other technical leaders will find a complete reference architecture, in-depth coverage of relevant technologies and use cases, and domain-specific best practices for their own projects. Coverage Includes How Social MDM extends fundamental MDM concepts and techniques Architecting Social MDM: components, functions, layers, and interactions Identifying high value relationships: person to product and person to organization Mapping Social MDM architecture to specific products and technologies Using Social MDM to create more compelling customer experiences Accelerating your transition to highly-targeted, contextual marketing Incorporating mobile data to improve employee productivity Avoiding privacy and ethical pitfalls throughout your ecosystem Previewing Semantic MDM and other emerging trends

Intelligence in Big Data Technologies—Beyond the Hype

Author : J. Dinesh Peter
Publisher : Springer Nature
ISBN 13 : 9811552851
Total Pages : 625 pages
Book Rating : 4.8/5 (115 download)

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Book Synopsis Intelligence in Big Data Technologies—Beyond the Hype by : J. Dinesh Peter

Download or read book Intelligence in Big Data Technologies—Beyond the Hype written by J. Dinesh Peter and published by Springer Nature. This book was released on 2020-07-25 with total page 625 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a compendium of the proceedings of the International Conference on Big-Data and Cloud Computing. The papers discuss the recent advances in the areas of big data analytics, data analytics in cloud, smart cities and grid, etc. This volume primarily focuses on the application of knowledge which promotes ideas for solving problems of the society through cutting-edge big-data technologies. The essays featured in this proceeding provide novel ideas that contribute for the growth of world class research and development. It will be useful to researchers in the area of advanced engineering sciences.

Composition and Big Data

Author : Amanda Licastro
Publisher : University of Pittsburgh Press
ISBN 13 : 0822988194
Total Pages : 277 pages
Book Rating : 4.8/5 (229 download)

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Book Synopsis Composition and Big Data by : Amanda Licastro

Download or read book Composition and Big Data written by Amanda Licastro and published by University of Pittsburgh Press. This book was released on 2021-11-02 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a data-driven world, anything can be data. As the techniques and scale of data analysis advance, the need for a response from rhetoric and composition grows ever more pronounced. It is increasingly possible to examine thousands of documents and peer-review comments, labor-hours, and citation networks in composition courses and beyond. Composition and Big Data brings together a range of scholars, teachers, and administrators already working with big-data methods and datasets to kickstart a collective reckoning with the role that algorithmic and computational approaches can, or should, play in research and teaching in the field. Their work takes place in various contexts, including programmatic assessment, first-year pedagogy, stylistics, and learning transfer across the curriculum. From ethical reflections to database design, from corpus linguistics to quantitative autoethnography, these chapters implement and interpret the drive toward data in diverse ways.

Big Data

Author : Viktor Mayer-Schönberger
Publisher : Houghton Mifflin Harcourt
ISBN 13 : 0544002695
Total Pages : 257 pages
Book Rating : 4.5/5 (44 download)

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Book Synopsis Big Data by : Viktor Mayer-Schönberger

Download or read book Big Data written by Viktor Mayer-Schönberger and published by Houghton Mifflin Harcourt. This book was released on 2013 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: A exploration of the latest trend in technology and the impact it will have on the economy, science, and society at large.

Big Data Analytics Beyond Hadoop

Author : Vijay Srinivas Agneeswaran
Publisher : FT Press
ISBN 13 : 0133838250
Total Pages : 235 pages
Book Rating : 4.1/5 (338 download)

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Book Synopsis Big Data Analytics Beyond Hadoop by : Vijay Srinivas Agneeswaran

Download or read book Big Data Analytics Beyond Hadoop written by Vijay Srinivas Agneeswaran and published by FT Press. This book was released on 2014-05-15 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master alternative Big Data technologies that can do what Hadoop can't: real-time analytics and iterative machine learning. When most technical professionals think of Big Data analytics today, they think of Hadoop. But there are many cutting-edge applications that Hadoop isn't well suited for, especially real-time analytics and contexts requiring the use of iterative machine learning algorithms. Fortunately, several powerful new technologies have been developed specifically for use cases such as these. Big Data Analytics Beyond Hadoop is the first guide specifically designed to help you take the next steps beyond Hadoop. Dr. Vijay Srinivas Agneeswaran introduces the breakthrough Berkeley Data Analysis Stack (BDAS) in detail, including its motivation, design, architecture, Mesos cluster management, performance, and more. He presents realistic use cases and up-to-date example code for: Spark, the next generation in-memory computing technology from UC Berkeley Storm, the parallel real-time Big Data analytics technology from Twitter GraphLab, the next-generation graph processing paradigm from CMU and the University of Washington (with comparisons to alternatives such as Pregel and Piccolo) Halo also offers architectural and design guidance and code sketches for scaling machine learning algorithms to Big Data, and then realizing them in real-time. He concludes by previewing emerging trends, including real-time video analytics, SDNs, and even Big Data governance, security, and privacy issues. He identifies intriguing startups and new research possibilities, including BDAS extensions and cutting-edge model-driven analytics. Big Data Analytics Beyond Hadoop is an indispensable resource for everyone who wants to reach the cutting edge of Big Data analytics, and stay there: practitioners, architects, programmers, data scientists, researchers, startup entrepreneurs, and advanced students.

Big Data at Work

Author : Thomas Davenport
Publisher : Harvard Business Review Press
ISBN 13 : 1422168174
Total Pages : 241 pages
Book Rating : 4.4/5 (221 download)

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Book Synopsis Big Data at Work by : Thomas Davenport

Download or read book Big Data at Work written by Thomas Davenport and published by Harvard Business Review Press. This book was released on 2014-02-04 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: Go ahead, be skeptical about big data. The author was—at first. When the term “big data” first came on the scene, bestselling author Tom Davenport (Competing on Analytics, Analytics at Work) thought it was just another example of technology hype. But his research in the years that followed changed his mind. Now, in clear, conversational language, Davenport explains what big data means—and why everyone in business needs to know about it. Big Data at Work covers all the bases: what big data means from a technical, consumer, and management perspective; what its opportunities and costs are; where it can have real business impact; and which aspects of this hot topic have been oversold. This book will help you understand: • Why big data is important to you and your organization • What technology you need to manage it • How big data could change your job, your company, and your industry • How to hire, rent, or develop the kinds of people who make big data work • The key success factors in implementing any big data project • How big data is leading to a new approach to managing analytics With dozens of company examples, including UPS, GE, Amazon, United Healthcare, Citigroup, and many others, this book will help you seize all opportunities—from improving decisions, products, and services to strengthening customer relationships. It will show you how to put big data to work in your own organization so that you too can harness the power of this ever-evolving new resource.

The Rise of Big Data Policing

Author : Andrew Guthrie Ferguson
Publisher : NYU Press
ISBN 13 : 147986997X
Total Pages : 267 pages
Book Rating : 4.4/5 (798 download)

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Book Synopsis The Rise of Big Data Policing by : Andrew Guthrie Ferguson

Download or read book The Rise of Big Data Policing written by Andrew Guthrie Ferguson and published by NYU Press. This book was released on 2019-11-15 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: Winner, 2018 Law & Legal Studies PROSE Award The consequences of big data and algorithm-driven policing and its impact on law enforcement In a high-tech command center in downtown Los Angeles, a digital map lights up with 911 calls, television monitors track breaking news stories, surveillance cameras sweep the streets, and rows of networked computers link analysts and police officers to a wealth of law enforcement intelligence. This is just a glimpse into a future where software predicts future crimes, algorithms generate virtual “most-wanted” lists, and databanks collect personal and biometric information. The Rise of Big Data Policing introduces the cutting-edge technology that is changing how the police do their jobs and shows why it is more important than ever that citizens understand the far-reaching consequences of big data surveillance as a law enforcement tool. Andrew Guthrie Ferguson reveals how these new technologies —viewed as race-neutral and objective—have been eagerly adopted by police departments hoping to distance themselves from claims of racial bias and unconstitutional practices. After a series of high-profile police shootings and federal investigations into systemic police misconduct, and in an era of law enforcement budget cutbacks, data-driven policing has been billed as a way to “turn the page” on racial bias. But behind the data are real people, and difficult questions remain about racial discrimination and the potential to distort constitutional protections. In this first book on big data policing, Ferguson offers an examination of how new technologies will alter the who, where, when and how we police. These new technologies also offer data-driven methods to improve police accountability and to remedy the underlying socio-economic risk factors that encourage crime. The Rise of Big Data Policing is a must read for anyone concerned with how technology will revolutionize law enforcement and its potential threat to the security, privacy, and constitutional rights of citizens. Read an excerpt and interview with Andrew Guthrie Ferguson in The Economist.

Blockchain, Internet of Things, and Artificial Intelligence

Author : Naveen Chilamkurti
Publisher : CRC Press
ISBN 13 : 1000359557
Total Pages : 400 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Blockchain, Internet of Things, and Artificial Intelligence by : Naveen Chilamkurti

Download or read book Blockchain, Internet of Things, and Artificial Intelligence written by Naveen Chilamkurti and published by CRC Press. This book was released on 2021-04-02 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Blockchain, Internet of Things, and Artificial Intelligence provides an integrated overview and technical description of the fundamental concepts of blockchain, IoT, and AI technologies. State-of-the-art techniques are explored in depth to discuss the challenges in each domain. The convergence of these revolutionized technologies has leveraged several areas that receive attention from academicians and industry professionals, which in turn promotes the book's accessibility more extensively. Discussions about an integrated perspective on the influence of blockchain, IoT, and AI for smart cities, healthcare, and other business sectors illuminate the benefits and opportunities in the ecosystems worldwide. The contributors have focused on real-world examples and applications and highlighted the significance of the strengths of blockchain to transform the readers’ thinking toward finding potential solutions. The faster maturity and stability of blockchain is the key differentiator in artificial intelligence and the Internet of Things. This book discusses their potent combination in realizing intelligent systems, services, and environments. The contributors present their technical evaluations and comparisons with existing technologies. Theoretical explanations and experimental case studies related to real-time scenarios are also discussed. FEATURES Discusses the potential of blockchain to significantly increase data while boosting accuracy and integrity in IoT-generated data and AI-processed information Elucidates definitions, concepts, theories, and assumptions involved in smart contracts and distributed ledgers related to IoT systems and AI approaches Offers real-world uses of blockchain technologies in different IoT systems and further studies its influence in supply chains and logistics, the automotive industry, smart homes, the pharmaceutical industry, agriculture, and other areas Presents readers with ways of employing blockchain in IoT and AI, helping them to understand what they can and cannot do with blockchain Provides readers with an awareness of how industry can avoid some of the pitfalls of traditional data-sharing strategies This book is suitable for graduates, academics, researchers, IT professionals, and industry experts.

AI for Data Science

Author : Zacharias Voulgaris
Publisher :
ISBN 13 : 9781634624091
Total Pages : 0 pages
Book Rating : 4.6/5 (24 download)

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Book Synopsis AI for Data Science by : Zacharias Voulgaris

Download or read book AI for Data Science written by Zacharias Voulgaris and published by . This book was released on 2018 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master the approaches and principles of Artificial Intelligence (AI) algorithms, and apply them to Data Science projects with Python and Julia code. Aspiring and practicing Data Science and AI professionals, along with Python and Julia programmers, will practice numerous AI algorithms and develop a more holistic understanding of the field of AI, and will learn when to use each framework to tackle projects in our increasingly complex world. The first two chapters introduce the field, with Chapter 1 surveying Deep Learning models and Chapter 2 providing an overview of algorithms beyond Deep Learning, including Optimization, Fuzzy Logic, and Artificial Creativity. The next chapters focus on AI frameworks; they contain data and Python and Julia code in a provided Docker, so you can practice. Chapter 3 covers Apache's MXNet, Chapter 4 covers TensorFlow, and Chapter 5 investigates Keras. After covering these Deep Learning frameworks, we explore a series of optimization frameworks, with Chapter 6 covering Particle Swarm Optimization (PSO), Chapter 7 on Genetic Algorithms (GAs), and Chapter 8 discussing Simulated Annealing (SA). Chapter 9 begins our exploration of advanced AI methods, by covering Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Chapter 10 discusses optimization ensembles and how they can add value to the Data Science pipeline. Chapter 11 contains several alternative AI frameworks including Extreme Learning Machines (ELMs), Capsule Networks (CapsNets), and Fuzzy Inference Systems (FIS). Chapter 12 covers other considerations complementary to the AI topics covered, including Big Data concepts, Data Science specialization areas, and useful data resources to experiment on. A comprehensive glossary is included, as well as a series of appendices covering Transfer Learning, Reinforcement Learning, Autoencoder Systems, and Generative Adversarial Networks. There is also an appendix on the business aspects of AI in data science projects, and an appendix on how to use the Docker image to access the book's data and code. The field of AI is vast, and can be overwhelming for the newcomer to approach. This book will arm you with a solid understanding of the field, plus inspire you to explore further.

Beyond the Big Bang

Author : Paul A. LaViolette
Publisher : Inner Traditions
ISBN 13 : 9780892814572
Total Pages : 374 pages
Book Rating : 4.8/5 (145 download)

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Book Synopsis Beyond the Big Bang by : Paul A. LaViolette

Download or read book Beyond the Big Bang written by Paul A. LaViolette and published by Inner Traditions. This book was released on 1995 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Big Data and Learning Analytics in Higher Education

Author : Ben Kei Daniel
Publisher : Springer
ISBN 13 : 3319065203
Total Pages : 272 pages
Book Rating : 4.3/5 (19 download)

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Book Synopsis Big Data and Learning Analytics in Higher Education by : Ben Kei Daniel

Download or read book Big Data and Learning Analytics in Higher Education written by Ben Kei Daniel and published by Springer. This book was released on 2016-08-27 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the uses of big data in the context of higher education. The book describes a wide range of administrative and operational data gathering processes aimed at assessing institutional performance and progress in order to predict future performance, and identifies potential issues related to academic programming, research, teaching and learning. Big data refers to data which is fundamentally too big and complex and moves too fast for the processing capacity of conventional database systems. The value of big data is the ability to identify useful data and turn it into useable information by identifying patterns and deviations from patterns.

Modern Big Data Architectures

Author : Dominik Ryzko
Publisher : John Wiley & Sons
ISBN 13 : 1119597846
Total Pages : 208 pages
Book Rating : 4.1/5 (195 download)

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Book Synopsis Modern Big Data Architectures by : Dominik Ryzko

Download or read book Modern Big Data Architectures written by Dominik Ryzko and published by John Wiley & Sons. This book was released on 2020-03-31 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides an up-to-date analysis of big data and multi-agent systems The term Big Data refers to the cases, where data sets are too large or too complex for traditional data-processing software. With the spread of new concepts such as Edge Computing or the Internet of Things, production, processing and consumption of this data becomes more and more distributed. As a result, applications increasingly require multiple agents that can work together. A multi-agent system (MAS) is a self-organized computer system that comprises multiple intelligent agents interacting to solve problems that are beyond the capacities of individual agents. Modern Big Data Architectures examines modern concepts and architecture for Big Data processing and analytics. This unique, up-to-date volume provides joint analysis of big data and multi-agent systems, with emphasis on distributed, intelligent processing of very large data sets. Each chapter contains practical examples and detailed solutions suitable for a wide variety of applications. The author, an internationally-recognized expert in Big Data and distributed Artificial Intelligence, demonstrates how base concepts such as agent, actor, and micro-service have reached a point of convergence—enabling next generation systems to be built by incorporating the best aspects of the field. This book: Illustrates how data sets are produced and how they can be utilized in various areas of industry and science Explains how to apply common computational models and state-of-the-art architectures to process Big Data tasks Discusses current and emerging Big Data applications of Artificial Intelligence Modern Big Data Architectures: A Multi-Agent Systems Perspective is a timely and important resource for data science professionals and students involved in Big Data analytics, and machine and artificial learning.

Big Data Analytics Beyond Hadoop

Author : Vijay Srinivas Agneeswaran
Publisher : Pearson Education
ISBN 13 : 0133837947
Total Pages : 235 pages
Book Rating : 4.1/5 (338 download)

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Book Synopsis Big Data Analytics Beyond Hadoop by : Vijay Srinivas Agneeswaran

Download or read book Big Data Analytics Beyond Hadoop written by Vijay Srinivas Agneeswaran and published by Pearson Education. This book was released on 2014 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master alternative Big Data technologies that can do what Hadoop can't: real-time analytics and iterative machine learning. When most technical professionals think of Big Data analytics today, they think of Hadoop. But there are many cutting-edge applications that Hadoop isn't well suited for, especially real-time analytics and contexts requiring the use of iterative machine learning algorithms. Fortunately, several powerful new technologies have been developed specifically for use cases such as these. Big Data Analytics Beyond Hadoop is the first guide specifically designed to help you take the next steps beyond Hadoop. Dr. Vijay Srinivas Agneeswaran introduces the breakthrough Berkeley Data Analysis Stack (BDAS) in detail, including its motivation, design, architecture, Mesos cluster management, performance, and more. He presents realistic use cases and up-to-date example code for: Spark, the next generation in-memory computing technology from UC Berkeley Storm, the parallel real-time Big Data analytics technology from Twitter GraphLab, the next-generation graph processing paradigm from CMU and the University of Washington (with comparisons to alternatives such as Pregel and Piccolo) Halo also offers architectural and design guidance and code sketches for scaling machine learning algorithms to Big Data, and then realizing them in real-time. He concludes by previewing emerging trends, including real-time video analytics, SDNs, and even Big Data governance, security, and privacy issues. He identifies intriguing startups and new research possibilities, including BDAS extensions and cutting-edge model-driven analytics. Big Data Analytics Beyond Hadoop is an indispensable resource for everyone who wants to reach the cutting edge of Big Data analytics, and stay there: practitioners, architects, programmers, data scientists, researchers, startup entrepreneurs, and advanced students.

Beyond Digital

Author : Paul Leinwand
Publisher : Harvard Business Press
ISBN 13 : 1647822335
Total Pages : 142 pages
Book Rating : 4.6/5 (478 download)

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Book Synopsis Beyond Digital by : Paul Leinwand

Download or read book Beyond Digital written by Paul Leinwand and published by Harvard Business Press. This book was released on 2022-01-04 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: Two world-renowned strategists detail the seven leadership imperatives for transforming companies in the new digital era. Digital transformation is critical. But winning in today's world requires more than digitization. It requires understanding that the nature of competitive advantage has shifted—and that being digital is not enough. In Beyond Digital, Paul Leinwand and Matt Mani from Strategy&, PwC's global strategy consulting business, take readers inside twelve companies and how they have navigated through this monumental shift: from Philips's reinvention from a broad conglomerate to a focused health technology player, to Cleveland Clinic's engagement with its broader ecosystem to improve and expand its leading patient care to more locations around the world, to Microsoft's overhaul of its global commercial business to drive customer outcomes. Other case studies include Adobe, Citigroup, Eli Lilly, Hitachi, Honeywell, Inditex, Komatsu, STC Pay, and Titan. Building on a major new body of research, the authors identify the seven imperatives that leaders must follow as the digital age continues to evolve: Reimagine your company's place in the world Embrace and create value via ecosystems Build a system of privileged insights with your customers Make your organization outcome-oriented Invert the focus of your leadership team Reinvent the social contract with your people Disrupt your own leadership approach Together, these seven imperatives comprise a playbook for how leaders can define a bolder purpose and transform their organizations.

Big Data Applications and Use Cases

Author : Patrick C. K. Hung
Publisher : Springer
ISBN 13 : 3319301462
Total Pages : 214 pages
Book Rating : 4.3/5 (193 download)

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Book Synopsis Big Data Applications and Use Cases by : Patrick C. K. Hung

Download or read book Big Data Applications and Use Cases written by Patrick C. K. Hung and published by Springer. This book was released on 2016-05-18 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents different use cases in big data applications and related practical experiences. Many businesses today are increasingly interested in utilizing big data technologies for supporting their business intelligence so that it is becoming more and more important to understand the various practical issues from different practical use cases. This book provides clear proof that big data technologies are playing an ever increasing important and critical role in a new cross-discipline research between computer science and business.

Big Data and Artificial Intelligence in Digital Finance

Author : John Soldatos
Publisher : Springer Nature
ISBN 13 : 3030945901
Total Pages : 371 pages
Book Rating : 4.0/5 (39 download)

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Book Synopsis Big Data and Artificial Intelligence in Digital Finance by : John Soldatos

Download or read book Big Data and Artificial Intelligence in Digital Finance written by John Soldatos and published by Springer Nature. This book was released on 2022 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents how cutting-edge digital technologies like Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTechs, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also introduces some of the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance. Introduces the latest advances in Big Data and AI in Digital Finance that enable scalable, effective, and real-time analytics; Explains the merits of Blockchain technology in digital finance, including applications beyond the blockbuster cryptocurrencies; Presents practical applications of cutting edge digital technologies in the digital finance sector; Illustrates the regulatory environment of the financial sector and presents technical solutions that boost compliance to applicable regulations; This book is open access, which means that you have free and unlimited access.

Big Data Analytics Beyond Hadoop

Author : Vijay Srinivas Agneeswaram
Publisher :
ISBN 13 : 9780133838268
Total Pages : pages
Book Rating : 4.8/5 (382 download)

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Book Synopsis Big Data Analytics Beyond Hadoop by : Vijay Srinivas Agneeswaram

Download or read book Big Data Analytics Beyond Hadoop written by Vijay Srinivas Agneeswaram and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Master alternative Big Data technologies that can do what Hadoop can't: real-time analytics and iterative machine learning. When most technical professionals think of Big Data analytics today, they think of Hadoop. But there are many cutting-edge applications that Hadoop isn't well suited for, especially real-time analytics and contexts requiring the use of iterative machine learning algorithms. Fortunately, several powerful new technologies have been developed specifically for use cases such as these. Big Data Analytics Beyond Hadoop is the first guide specifically designed to help you take the next steps beyond Hadoop. Dr. Vijay Srinivas Agneeswaran introduces the breakthrough Berkeley Data Analysis Stack (BDAS) in detail, including its motivation, design, architecture, Mesos cluster management, performance, and more. He presents realistic use cases and up-to-date example code for:Spark, the next generation in-memory computing technology from UC BerkeleyStorm, the parallel real-time Big Data analytics technology from TwitterGraphLab, the next-generation graph processing paradigm from CMU and the University of Washington (with comparisons to alternatives such as Pregel and Piccolo)Halo also offers architectural and design guidance and code sketches for scaling machine learning algorithms to Big Data, and then realizing them in real-time. He concludes by previewing emerging trends, including real-time video analytics, SDNs, and even Big Data governance, security, and privacy issues. He identifies intriguing startups and new research possibilities, including BDAS extensions and cutting-edge model-driven analytics. Big Data Analytics Beyond Hadoop is an indispensable resource for everyone who wants to reach the cutting edge of Big Data analytics, and stay there: practitioners, architects, programmers, data scientists, researchers, startup entrepreneurs, and advanced students.