| Description |
1 online resource (422 pages) |
| Contents |
Intro; Foreword; Preface; Acknowledgements; About This Book; Contents; About the Authors; 1 Big Data Analytics; 1.1 Introduction; 1.2 What Is Big Data?; 1.3 Disruptive Change and Paradigm Shift in the Business Meaning of Big Data; 1.4 Hadoop; 1.5 Silos; 1.5.1 Big Bang of Big Data; 1.5.2 Possibilities; 1.5.3 Future; 1.5.4 Parallel Processing for Problem Solving; 1.5.5 Why Hadoop?; 1.5.6 Hadoop and HDFS; 1.5.7 Hadoop Versions 1.0 and 2.0; 1.5.8 Hadoop 2.0; 1.6 HDFS Overview; 1.6.1 MapReduce Framework; 1.6.2 Job Tracker and Task Tracker; 1.6.3 YARN; 1.7 Hadoop Ecosystem |
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1.7.1 Cloud-Based Hadoop Solutions1.7.2 Spark and Data Stream Processing; 1.8 Decision Making and Data Analysis in the Context of Big Data Environment; 1.8.1 Present-Day Data Analytics Techniques; 1.9 Machine Learning Algorithms; 1.10 Evolutionary Computing (EC); 1.11 Conclusion; 1.12 Review Questions; References and Bibliography; 2 Intelligent Systems; 2.1 Introduction; 2.1.1 Open-Source Data Science; 2.1.2 Machine Intelligence and Computational Intelligence; 2.1.3 Data Engineering and Data Sciences; 2.2 Big Data Computing; 2.2.1 Distributed Systems and Database Systems |
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2.2.2 Data Stream Systems and Stream Mining2.2.3 Ubiquitous Computing Infrastructures; 2.3 Conclusion; 2.4 Review Questions; References; 3 Analytics Models for Data Science; 3.1 Introduction; 3.2 Data Models; 3.2.1 Data Products; 3.2.2 Data Munging; 3.2.3 Descriptive Analytics; 3.2.4 Predictive Analytics; 3.2.5 Data Science; 3.2.6 Network Science; 3.3 Computing Models; 3.3.1 Data Structures for Big Data; 3.3.2 Feature Engineering for Structured Data; 3.3.3 Computational Algorithm; 3.3.4 Programming Models; 3.3.5 Parallel Programming; 3.3.6 Functional Programming; 3.3.7 Distributed Programming |
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3.4 Conclusion3.5 Review Questions; References; 4 Big Data Tools-Hadoop Ecosystem, Spark and NoSQL Databases; 4.1 Introduction; 4.1.1 Hadoop Ecosystem; 4.1.2 HDFS Commands [1]; 4.2 MapReduce; 4.3 Pig; 4.4 Flume; 4.5 Sqoop; 4.6 Mahout, The Machine Learning Platform from Apache; 4.7 GANGLIA, The Monitoring Tool; 4.8 Kafka, The Stream Processing Platform (http://kafka.apache.org); 4.9 Spark; 4.10 NoSQL Databases; 4.11 Conclusion; References; 5 Predictive Modeling for Unstructured Data; 5.1 Introduction; 5.2 Applications of Predictive Modeling; 5.2.1 Natural Language Processing |
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5.2.2 Computer Vision5.2.3 Information Retrieval; 5.2.4 Speech Recognition; 5.3 Feature Engineering; 5.3.1 Feature Extraction and Weighing; 5.3.2 Feature Selection; 5.4 Pattern Mining for Predictive Modeling; 5.4.1 Probabilistic Graphical Models; 5.4.2 Deep Learning; 5.4.3 Convolutional Neural Networks (CNN); 5.4.4 Recurrent Neural Networks (RNNs); 5.4.5 Deep Boltzmann Machines (DBM); 5.4.6 Autoencoders; 5.5 Conclusion; 5.6 Review Questions; References; 6 Machine Learning Algorithms for Big Data; 6.1 Introduction; 6.2 Generative Versus Discriminative Algorithms |
| Summary |
This book provides a comprehensive survey of techniques, technologies and applications of Big Data and its analysis. The Big Data phenomenon is increasingly impacting all sectors of business and industry, producing an emerging new information ecosystem. On the applications front, the book offers detailed descriptions of various application areas for Big Data Analytics in the important domains of Social Semantic Web Mining, Banking and Financial Services, Capital Markets, Insurance, Advertisement, Recommendation Systems, Bio-Informatics, the IoT and Fog Computing, before delving into issues of security and privacy. With regard to machine learning techniques, the book presents all the standard algorithms for learning - including supervised, semi-supervised and unsupervised techniques such as clustering and reinforcement learning techniques to perform collective Deep Learning. Multi-layered and nonlinear learning for Big Data are also covered. In turn, the book highlights real-life case studies on successful implementations of Big Data Analytics at large IT companies such as Google, Facebook, LinkedIn and Microsoft. Multi-sectorial case studies on domain-based companies such as Deutsche Bank, the power provider Opower, Delta Airlines and a Chinese City Transportation application represent a valuable addition. Given its comprehensive coverage of Big Data Analytics, the book offers a unique resource for undergraduate and graduate students, researchers, educators and IT professionals alike |
| Notes |
6.3 Supervised Learning for Big Data |
| Bibliography |
Includes bibliographical references |
| Notes |
Print version record |
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Online resource; title from digital title page (viewed on November 06, 2019) |
| Subject |
Big data.
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Big data
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| Form |
Electronic book
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| Author |
Chivukula, Aneesh Sreevallabh, author
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Mogadala, Aditya, author
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Ghosh, Rohit, author
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Livingston, L. M. Jenila, author
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| ISBN |
9789811500947 |
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9811500940 |
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