This book provides a central source of reference on the various data management techniques of large scale data processing and its technology application. This book presents chapters written by leading researchers, academics, and practitioners in the field, all of which have been reviewed by independent reviewers. The book covers the latest research discoveries and applications. 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.

Distributed Programming for the Cloud. MapReduce Family of Large-Scale Data-Processing Systems. Extending MapReduce for Iterative Processing. Incremental MapReduce Computations. Large-Scale RDF Processing with MapReduce. Algebraic Optimization of RDF Graph Pattern Queries on MapReduce. Network Performance Aware Graph Partitioning for Large Graph Processing Systems in the Cloud. PEGASUS. An Overview of the NoSQL World. Consistency Management in Cloud Storage Systems. CloudDB AutoAdmin. Overview of Large-Scale Stream Processing Engines. Advanced Algorithms for Efficient Approximate Duplicate Detection in Data Streams Using Bloom Filters. Large-Scale Network Traffic Analysis for Estimating the Size of IP Addresses and Detecting Traffic Anomalies. Recommending Environmental Big Data Using Semantically Guided Machine Learning. Virtualizing Resources for the Cloud. Toward Optimal Resource Provisioning for Economical and Green MapReduce. Computing in the Cloud. Performance Analysis for Large IaaS Clouds. Security in Big Data and Cloud Computing.