Large Scale and Big Data (eBook, PDF)

Processing and Management
Redaktion: Sakr, Sherif; Gaber, Mohamed

• Format: PDF

Produktbeschreibung

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 t

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 636
• Erscheinungstermin: 25. Juni 2014
• Englisch
• ISBN-13: 9781466581517
• Artikelnr.: 41212720

Autorenporträt

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.

Inhaltsangabe

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.