Large Scale and Big Data

"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"--

Auteur

Dr. Sherif Sakr is a Senior Researcher at National ICT Australia (NICTA), Sydney, Australia. He is also a Conjoint Senior Lecturer at the University of New South Wales (UNSW). He received his PhD degree in Computer and Information Science from Konstanz University, Germany in 2007. He received his BSc and MSc degrees in Computer Science from Cairo University, Egypt, in 2000 and 2003 respectively. In 2011, Sherif held a Visiting Researcher position at the eXtreme Computing Group, Microsoft Research, USA. In 2012, he held a Research MTS position in Alcatel-Lucent Bell Labs. Dr. Sakr has published more than 60 refereed research publications in international journals and conferences such as the IEEE TSC, ACM CSUR, JCSS, IEEE COMST, VLDB, SIGMOD, ICDE, WWW, and CIKM. He has served in the organizing and program committees of numerous conferences and workshops.

Dr. Mohamed Medhat Gaber is a reader in the School of Computing Science and Digital Media of Robert Gordon University, UK. Mohamed received his PhD from Monash University, Australia, in 2006. He then held appointments with the University of Sydney, CSIRO, Monash University, and the University of Portsmouth. Dr. Gaber has published over 100 papers, coauthored one monograph-style book, and edited/coedited four books on data mining, and knowledge discovery. He has served in the program committees of major conferences related to data mining, including ICDM, PAKDD, ECML/PKDD, and ICML. He has also been a member of the organizing committees of numerous conferences and workshops.

Résumé

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.

Contenu

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.