Large Scale and Big Data: Processing and Management [Paperback]

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.

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 books 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 Dal£J