Big data analytics : systems, algorithms, applications /: systems, algorithms, applications. ([2019])

• Record Type: Book
• Further Information: Note: C.S.R. Prabhu, Aneesh Sreevallabh Chivukula, Aditya Mogadala, Rohit Ghoh, L.M. Jenila Livingston.
• Authors: Prabhu, C. S. R; Chivukula, Aneesh Sreevallabh; Mogadala, Aditya; Ghosh, Rohit; Livingston, L. M. Jenila
• 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 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 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 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 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
• Publisher Details: Singapore : Springer Singapore Pte. Limited
• Publication Date: 2019
• Extent: 1 online resource
• Subjects: 005.7; Big data; Big data; Electronic books
• Languages: English
• ISBNs: 9789811500947; 9811500940
• Related ISBNs: 9789811500930
• Notes: Note: Description based on online resource; title from digital title page (viewed on November 06, 2019).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.