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Author Talia, Domenico, author.

Title Data Analysis in the Cloud : Models, Techniques and Applications / Domenico Talia, Paolo Trunfio, Fabrizio Marozzo.

Publication Info. Amsterdam, Netherlands : Elsevier Ltd., [2016]
©2016

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Location Call No. OPAC Message Status
 Axe Elsevier ScienceDirect Ebook  Electronic Book    ---  Available
Description 1 online resource
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
Series Computer Science Reviews and Trends
Computer science reviews and trends.
Note Vendor-supplied metadata.
Bibliography Includes bibliographical references.
Summary Data Analysis in the Cloud introduces and discusses models, methods, techniques, and systems to analyze the large number of digital data sources available on the Internet using the computing and storage facilities of the cloud. Coverage includes scalable data mining and knowledge discovery techniques together with cloud computing concepts, models, and systems. Specific sections focus on map-reduce and NoSQL models. The book also includes techniques for conducting high-performance distributed analysis of large data on clouds. Finally, the book examines research trends such as Big Data pervasive computing, data-intensive exascale computing, and massive social network analysis.
Contents Cover; Title Page; Copyright Page; Dedication; Contents; Preface; Chapter 1 -- Introduction to Data Mining; 1.1 -- Data mining concepts ; 1.1.1 -- Classification ; 1.1.1.1 -- Decision Trees ; 1.1.1.2 -- Classification with kNN ; 1.1.2 -- Clustering ; 1.1.2.1 -- Bayesian Classification ; 1.1.2.2 -- The K-Means Algorithm ; 1.1.3 -- Association Rules ; 1.2 -- Parallel and distributed data mining ; 1.2.1 -- Parallel Classification ; 1.2.2 -- Parallel Clustering ; 1.2.3 -- Parallelism in Association Rules ; 1.2.4 -- Distributed Data Mining ; 1.2.4.1 -- Meta-Learning.
1.2.4.2 -- Collective Data Mining 1.2.4.3 -- Ensemble Learning ; 1.3 -- Summary ; References; Chapter 2 -- Introduction to Cloud Computing; 2.1 -- Cloud computing: definition, models, and architectures ; 2.1.1 -- Service Models ; 2.1.2 -- Deployment Models ; 2.1.3 -- Cloud Environments ; 2.1.3.1 -- Microsoft Azure ; 2.1.3.2 -- Amazon Web Services ; 2.1.3.3 -- OpenNebula ; 2.1.3.4 -- OpenStack ; 2.2 -- Cloud computing systems for data-intensive applications ; 2.2.1 -- Functional Requirements ; 2.2.1.1 -- Resource Management ; 2.2.1.2 -- Application Management.
2.2.2 -- Nonfunctional Requirements 2.2.2.1 -- User Requirements ; 2.2.2.2 -- Architecture Requirements ; 2.2.2.3 -- Infrastructure Requirements ; 2.2.3 -- Cloud Models for Distributed Data Analysis ; 2.3 -- Summary ; References ; Chapter 3 -- Models and Techniques for Cloud-Based Data Analysis; 3.1 -- MapReduce for data analysis ; 3.1.1 -- MapReduce Paradigm ; 3.1.2 -- MapReduce Frameworks ; 3.1.3 -- MapReduce Algorithms and Applications ; 3.2 -- Data analysis workflows ; 3.2.1 -- Workflow Programming ; 3.2.2 -- Workflow Management Systems ; 3.2.3 -- Workflow Management Systems for Clouds.
3.3 -- NoSQL models for data analytics 3.3.1 -- Key Features of NoSQL ; 3.3.2 -- Classification of NoSQL Databases ; 3.3.3 -- NoSQL Systems ; 3.3.3.1 -- Dynamo ; 3.3.3.2 -- MongoDB ; 3.3.3.3 -- Bigtable ; 3.3.4 -- Use Cases ; 3.4 -- Summary ; References ; Chapter 4 -- Designing and Supporting Scalable Data Analytics ; 4.1 -- Data analysis systems for clouds ; 4.1.1 -- Pegasus ; 4.1.2 -- Swift ; 4.1.3 -- Hunk ; 4.1.4 -- Sector/Sphere ; 4.1.5 -- BigML ; 4.1.6 -- Kognitio Analytical Platform ; 4.1.7 -- Mahout ; 4.1.8 -- Spark ; 4.1.9 -- Microsoft Azure Machine Learning ; 4.1.10 -- ClowdFlows.
4.2 -- How to design a scalable data analysis framework in clouds 4.2.1 -- Architecture and Execution Mechanisms ; 4.2.2 -- Implementation on Microsoft Azure ; 4.3 -- Programming workflow-based data analysis ; 4.3.1 -- VL4Cloud ; 4.3.2 -- JS4Cloud ; 4.3.3 -- Workflow Patterns in DMCF ; 4.3.3.1 -- Single Task ; 4.3.3.2 -- Pipeline ; 4.3.3.3 -- Data Partitioning ; 4.3.3.4 -- Data Aggregation ; 4.3.3.5 -- Parameter Sweeping ; 4.3.3.6 -- Input Sweeping ; 4.3.3.7 -- Tool Sweeping ; 4.3.3.8 -- Combination of Sweeping Patterns ; 4.4 -- Data analysis case studies.
4.4.1 -- Trajectory Mining Workflow Using VL4Cloud.
Subject Quantitative research.
Data mining.
Cloud computing.
Recherche quantitative.
Exploration de données (Informatique)
Infonuagique.
COMPUTERS -- General.
Cloud computing
Data mining
Quantitative research
Added Author Trunfio, Paolo, author.
Marozzo, Fabrizio, author.
Other Form: Print version: Talia, Domenico. Data Analysis in the Cloud : Models, Techniques and Applications. : Elsevier Science, ©2015 9780128028810
ISBN 9780128029145 (electronic bk.)
0128029145 (electronic bk.)
9780128028810
0128028815
Standard No. AU@ 000060935079
CHNEW 001013126
DEBSZ 451529340
DEBSZ 461171864
GBVCP 856732516
GBVCP 897159365
AU@ 000055527763

 
    
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