Cognitive Computing and Big Data Analytics

Author: Judith S. Hurwitz,Marcia Kaufman,Adrian Bowles

Publisher: John Wiley & Sons

ISBN: 1118896785

Category: Computers

Page: 288

View: 9142

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A comprehensive guide to learning technologies that unlock thevalue in big data Cognitive Computing provides detailed guidance towardbuilding a new class of systems that learn from experience andderive insights to unlock the value of big data. This book helpstechnologists understand cognitive computing's underlyingtechnologies, from knowledge representation techniques and naturallanguage processing algorithms to dynamic learning approaches basedon accumulated evidence, rather than reprogramming. Detailed caseexamples from the financial, healthcare, and manufacturing walkreaders step-by-step through the design and testing of cognitivesystems, and expert perspectives from organizations such asCleveland Clinic, Memorial Sloan-Kettering, as well as commercialvendors that are creating solutions. These organizations provideinsight into the real-world implementation of cognitive computingsystems. The IBM Watson cognitive computing platform is describedin a detailed chapter because of its significance in helping todefine this emerging market. In addition, the book includesimplementations of emerging projects from Qualcomm, Hitachi, Googleand Amazon. Today's cognitive computing solutions build on establishedconcepts from artificial intelligence, natural language processing,ontologies, and leverage advances in big data management andanalytics. They foreshadow an intelligent infrastructure thatenables a new generation of customer and context-aware smartapplications in all industries. Cognitive Computing is a comprehensive guide to thesubject, providing both the theoretical and practical guidancetechnologists need. Discover how cognitive computing evolved from promise toreality Learn the elements that make up a cognitive computingsystem Understand the groundbreaking hardware and softwaretechnologies behind cognitive computing Learn to evaluate your own application portfolio to find thebest candidates for pilot projects Leverage cognitive computing capabilities to transform theorganization Cognitive systems are rightly being hailed as the new era ofcomputing. Learn how these technologies enable emerging firms tocompete with entrenched giants, and forward-thinking establishedfirms to disrupt their industries. Professionals who currently workwith big data and analytics will see how cognitive computing buildson their foundation, and creates new opportunities. CognitiveComputing provides complete guidance to this new level ofhuman-machine interaction.
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Big-Data Analytics for Cloud, IoT and Cognitive Computing

Author: Kai Hwang,Min Chen

Publisher: John Wiley & Sons

ISBN: 1119247020

Category: Computers

Page: 432

View: 6363

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The definitive guide to successfully integrating social, mobile, Big-Data analytics, cloud and IoT principles and technologies The main goal of this book is to spur the development of effective big-data computing operations on smart clouds that are fully supported by IoT sensing, machine learning and analytics systems. To that end, the authors draw upon their original research and proven track record in the field to describe a practical approach integrating big-data theories, cloud design principles, Internet of Things (IoT) sensing, machine learning, data analytics and Hadoop and Spark programming. Part 1 focuses on data science, the roles of clouds and IoT devices and frameworks for big-data computing. Big data analytics and cognitive machine learning, as well as cloud architecture, IoT and cognitive systems are explored, and mobile cloud-IoT-interaction frameworks are illustrated with concrete system design examples. Part 2 is devoted to the principles of and algorithms for machine learning, data analytics and deep learning in big data applications. Part 3 concentrates on cloud programming software libraries from MapReduce to Hadoop, Spark and TensorFlow and describes business, educational, healthcare and social media applications for those tools. The first book describing a practical approach to integrating social, mobile, analytics, cloud and IoT (SMACT) principles and technologies Covers theory and computing techniques and technologies, making it suitable for use in both computer science and electrical engineering programs Offers an extremely well-informed vision of future intelligent and cognitive computing environments integrating SMACT technologies Fully illustrated throughout with examples, figures and approximately 150 problems to support and reinforce learning Features a companion website with an instructor manual and PowerPoint slides www.wiley.com/go/hwangIOT Big-Data Analytics for Cloud, IoT and Cognitive Computing satisfies the demand among university faculty and students for cutting-edge information on emerging intelligent and cognitive computing systems and technologies. Professionals working in data science, cloud computing and IoT applications will also find this book to be an extremely useful working resource.
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Cognitive Computing for Big Data Systems Over IoT

Frameworks, Tools and Applications

Author: Arun Kumar Sangaiah,Arunkumar Thangavelu,Venkatesan Meenakshi Sundaram

Publisher: Springer

ISBN: 3319706888

Category: Computers

Page: 375

View: 6254

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This book brings a high level of fluidity to analytics and addresses recent trends, innovative ideas, challenges and cognitive computing solutions in big data and the Internet of Things (IoT). It explores domain knowledge, data science reasoning and cognitive methods in the context of the IoT, extending current data science approaches by incorporating insights from experts as well as a notion of artificial intelligence, and performing inferences on the knowledge The book provides a comprehensive overview of the constituent paradigms underlying cognitive computing methods, which illustrate the increased focus on big data in IoT problems as they evolve. It includes novel, in-depth fundamental research contributions from a methodological/application in data science accomplishing sustainable solution for the future perspective. Mainly focusing on the design of the best cognitive embedded data science technologies to process and analyze the large amount of data collected through the IoT, and aid better decision making, the book discusses adapting decision-making approaches under cognitive computing paradigms to demonstrate how the proposed procedures as well as big data and IoT problems can be handled in practice. This book is a valuable resource for scientists, professionals, researchers, and academicians dealing with the new challenges and advances in the specific areas of cognitive computing and data science approaches.
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Big Data Analytics for Sustainable Computing

Author: Haldorai, Anandakumar,Ramu, Arulmurugan

Publisher: IGI Global

ISBN: 1522597522

Category: Computers

Page: 263

View: 4161

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Big data consists of data sets that are too large and complex for traditional data processing and data management applications. Therefore, to obtain the valuable information within the data, one must use a variety of innovative analytical methods, such as web analytics, machine learning, and network analytics. As the study of big data becomes more popular, there is an urgent demand for studies on high-level computational intelligence and computing services for analyzing this significant area of information science. Big Data Analytics for Sustainable Computing is a collection of innovative research that focuses on new computing and system development issues in emerging sustainable applications. Featuring coverage on a wide range of topics such as data filtering, knowledge engineering, and cognitive analytics, this publication is ideally designed for data scientists, IT specialists, computer science practitioners, computer engineers, academicians, professionals, and students seeking current research on emerging analytical techniques and data processing software.
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Cognitive Computing in Technology-Enhanced Learning

Author: Lytras, Miltiadis D.,Aljohani, Naif,Daniela, Linda,Visvizi, Anna

Publisher: IGI Global

ISBN: 1522590323

Category: Education

Page: 345

View: 7454

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Various technologies and applications such as cognitive computing, artificial intelligence, and learning analytics have received increased attention in recent years. The growing demand behind their adoption and exploitation in different application contexts has captured the attention of learning technology specialists, computer engineers, and business researchers who are attempting to decipher the phenomenon of personalized e-learning, its relation to already conducted research, and its implications for new research opportunities that effect innovations in teaching. Cognitive Computing in Technology-Enhanced Learning is a critical resource publication that aims to demonstrate state-of-the-art approaches of advanced data mining systems in e-learning, such as MOOCs and other innovative technologies, to improve learning analytics, as well as to show how new and advanced user interaction designs, educational models, and adoptive strategies can expand sustainability in applied learning technologies. Highlighting a range of topics such as augmented reality, ethics, and online learning environments, this book is ideal for educators, instructional designers, higher education faculty, school administrators, academicians, researchers, and students.
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Big Data Analytics and Computing for Digital Forensic Investigations

Author: Suneeta Satpathy,Sachi Nandan Mohanty

Publisher: CRC Press

ISBN: 1000045056

Category: Computers

Page: 214

View: 9193

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Digital forensics has recently gained a notable development and become the most demanding area in today’s information security requirement. This book investigates the areas of digital forensics, digital investigation and data analysis procedures as they apply to computer fraud and cybercrime, with the main objective of describing a variety of digital crimes and retrieving potential digital evidence. Big Data Analytics and Computing for Digital Forensic Investigations gives a contemporary view on the problems of information security. It presents the idea that protective mechanisms and software must be integrated along with forensic capabilities into existing forensic software using big data computing tools and techniques. Features Describes trends of digital forensics served for big data and the challenges of evidence acquisition Enables digital forensic investigators and law enforcement agencies to enhance their digital investigation capabilities with the application of data science analytics, algorithms and fusion technique This book is focused on helping professionals as well as researchers to get ready with next-generation security systems to mount the rising challenges of computer fraud and cybercrimes as well as with digital forensic investigations. Dr Suneeta Satpathy has more than ten years of teaching experience in different subjects of the Computer Science and Engineering discipline. She is currently working as an associate professor in the Department of Computer Science and Engineering, College of Bhubaneswar, affiliated with Biju Patnaik University and Technology, Odisha. Her research interests include computer forensics, cybersecurity, data fusion, data mining, big data analysis and decision mining. Dr Sachi Nandan Mohanty is an associate professor in the Department of Computer Science and Engineering at ICFAI Tech, ICFAI Foundation for Higher Education, Hyderabad, India. His research interests include data mining, big data analysis, cognitive science, fuzzy decision-making, brain–computer interface, cognition and computational intelligence.
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Big Data Analytics in Cybersecurity

Author: Onur Savas,Julia Deng

Publisher: CRC Press

ISBN: 1351650416

Category: Business & Economics

Page: 336

View: 8683

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Big data is presenting challenges to cybersecurity. For an example, the Internet of Things (IoT) will reportedly soon generate a staggering 400 zettabytes (ZB) of data a year. Self-driving cars are predicted to churn out 4000 GB of data per hour of driving. Big data analytics, as an emerging analytical technology, offers the capability to collect, store, process, and visualize these vast amounts of data. Big Data Analytics in Cybersecurity examines security challenges surrounding big data and provides actionable insights that can be used to improve the current practices of network operators and administrators. Applying big data analytics in cybersecurity is critical. By exploiting data from the networks and computers, analysts can discover useful network information from data. Decision makers can make more informative decisions by using this analysis, including what actions need to be performed, and improvement recommendations to policies, guidelines, procedures, tools, and other aspects of the network processes. Bringing together experts from academia, government laboratories, and industry, the book provides insight to both new and more experienced security professionals, as well as data analytics professionals who have varying levels of cybersecurity expertise. It covers a wide range of topics in cybersecurity, which include: Network forensics Threat analysis Vulnerability assessment Visualization Cyber training. In addition, emerging security domains such as the IoT, cloud computing, fog computing, mobile computing, and cyber-social networks are examined. The book first focuses on how big data analytics can be used in different aspects of cybersecurity including network forensics, root-cause analysis, and security training. Next it discusses big data challenges and solutions in such emerging cybersecurity domains as fog computing, IoT, and mobile app security. The book concludes by presenting the tools and datasets for future cybersecurity research.
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Cognitive Computing: Theory and Applications

Author: Vijay V Raghavan,Venkat N. Gudivada,Venu Govindaraju,C.R. Rao

Publisher: Elsevier

ISBN: 0444637516

Category: Mathematics

Page: 404

View: 4013

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Cognitive Computing: Theory and Applications, written by internationally renowned experts, focuses on cognitive computing and its theory and applications, including the use of cognitive computing to manage renewable energy, the environment, and other scarce resources, machine learning models and algorithms, biometrics, Kernel Based Models for transductive learning, neural networks, graph analytics in cyber security, neural networks, data driven speech recognition, and analytical platforms to study the brain-computer interface. Comprehensively presents the various aspects of statistical methodology Discusses a wide variety of diverse applications and recent developments Contributors are internationally renowned experts in their respective areas
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