Large Scale and Big Data: Processing and Management

Large Scale and Big Data: Processing and Management provides readers with a central source of reference on the data management techniques currently available for large-scale data processing. Presenting chapters written by leading researchers, academics, and practitioners, it addresses the fundamental challenges associated with Big Data processing tools and techniques across a range of computing environments.

The book begins by discussing the basic concepts and tools of large-scale Big Data processing and cloud computing. It also provides an overview of different programming models and cloud-based deployment models. The book’s second section examines the usage of advanced Big Data processing techniques in different domains, including semantic web, graph processing, and stream processing. The third section discusses advanced topics of Big Data processing such as consistency management, privacy, and security.

Supplying a comprehensive summary from both the research and applied perspectives, the book covers recent research discoveries and applications, making it an ideal reference for a wide range of audiences, including researchers and academics working on databases, data mining, and web scale data processing.

After reading this book, you will gain a fundamental understanding of how to use Big Data-processing tools and techniques effectively across application domains. Coverage includes cloud data management architectures, big data analytics visualization, data management, analytics for vast amounts of unstructured data, clustering, classification, link analysis of big data, scalable data mining, and machine learning techniques.

Table of Contents

Chapter 1: Distributed Programming for the Cloud : Models, Challenges, and Analytics Engines
Chapter 2: MapReduce Family of Large-Scale Data-Processing Systems
Chapter 3: iMapReduce : Extending MapReduce for Iterative Processing
Chapter 4: Incremental MapReduce Computations
Chapter 5: Large-Scale RDF Processing with MapReduce
Chapter 6: Algebraic Optimization of RDF Graph Pattern Queries on MapReduce
Chapter 7: Network Performance Aware Graph Partitioning for Large Graph Processing Systems in the Cloud
Chapter 8: PEGASUS : A System for Large-Scale Graph Processing
Chapter 9: An Overview of the NoSQL World
Chapter 10: Consistency Management in Cloud Storage Systems
Chapter 11: CloudDB AutoAdmin : A Consumer-Centric Framework for SLA Management of Virtualized Database Servers
Chapter 12: An Overview of Large-Scale Stream Processing Engines
Chapter 13: Advanced Algorithms for Efficient Approximate Duplicate Detection in Data Streams Using Bloom Filters
Chapter 14: Large-Scale Network Traffic Analysis for Estimating the Size of IP Addresses and Detecting Traffic Anomalies
Chapter 15: Recommending Environmental Big Data Using Semantically Guided Machine Learning
Chapter 16: Virtualizing Resources for the Cloud
Chapter 17: Toward Optimal Resource Provisioning for Economical and Green MapReduce Computing in the Cloud
Chapter 18: Performance Analysis for Large IaaS Clouds
Chapter 19: Security in Big Data and Cloud Computing : Challenges, Solutions, and Open Problems