Data Analysis in the Cloud

Description

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.

Introduces data analysis techniques and cloud computing concepts
Describes cloud-based models and systems for Big Data analytics
Provides examples of the state-of-the-art in cloud data analysis
Explains how to develop large-scale data mining applications on clouds
Outlines the main research trends in the area of scalable Big Data analysis

Cover 2
Title Page 5
Copyright Page 6
Dedication 7
Contents 9
Preface 13
Chapter 1 - Introduction to Data Mining 15
- 1.1 - Data mining concepts 15
  - 1.1.1 - Classification 18
    - 1.1.1.1 - Decision Trees 19
    - 1.1.1.2 - Classification with kNN 21
  - 1.1.2 - Clustering 22
    - 1.1.2.1 - Bayesian Classification 23
    - 1.1.2.2 - The K-Means Algorithm 26
  - 1.1.3 - Association Rules 27

Cover 2
Title Page 5
Copyright Page 6
Dedication 7
Contents 9
Preface 13
Chapter 1 - Introduction to Data Mining 15
- 1.1 - Data mining concepts 15
  - 1.1.1 - Classification 18
    - 1.1.1.1 - Decision Trees 19
    - 1.1.1.2 - Classification with kNN 21
  - 1.1.2 - Clustering 22
    - 1.1.2.1 - Bayesian Classification 23
    - 1.1.2.2 - The K-Means Algorithm 26
  - 1.1.3 - Association Rules 27
- 1.2 - Parallel and distributed data mining 30
  - 1.2.1 - Parallel Classification 31
  - 1.2.2 - Parallel Clustering 32
  - 1.2.3 - Parallelism in Association Rules 33
  - 1.2.4 - Distributed Data Mining 34
    - 1.2.4.2 - Collective Data Mining 35
    - 1.2.4.1 - Meta-Learning 35
    - 1.2.4.3 - Ensemble Learning 36
  - 1.3 - Summary 37
  - References 38
- Chapter 2 - Introduction to Cloud Computing 41
  - 2.1 - Cloud computing: definition, models, and architectures 41
    - 2.1.1 - Service Models 42
    - 2.1.2 - Deployment Models 43
    - 2.1.3 - Cloud Environments 46
      - 2.1.3.1 - Microsoft Azure 46
      - 2.1.3.2 - Amazon Web Services 48
      - 2.1.3.3 - OpenNebula 49
      - 2.1.3.4 - OpenStack 50
    - 2.2 - Cloud computing systems for data-intensive applications 51
      - 2.2.1 - Functional Requirements 52
        
        2.2.1.1 - Resource Management 52
        
        2.2.1.2 - Application Management 53
      - 2.2.2 - Nonfunctional Requirements 53
        
        2.2.2.2 - Architecture Requirements 54
        
        2.2.2.3 - Infrastructure Requirements 54
        
        2.2.2.1 - User Requirements 54
      - 2.2.3 - Cloud Models for Distributed Data Analysis 55
    - 2.3 - Summary 57
    - References 57
  - Chapter 3 - Models and Techniques for Cloud-Based Data Analysis 59
    - 3.1 - MapReduce for data analysis 59
      - 3.1.1 - MapReduce Paradigm 61
      - 3.1.2 - MapReduce Frameworks 63
      - 3.1.3 - MapReduce Algorithms and Applications 65
    - 3.2 - Data analysis workflows 67
      - 3.2.1 - Workflow Programming 69
      - 3.2.2 - Workflow Management Systems 73
      - 3.2.3 - Workflow Management Systems for Clouds 75
    - 3.3 - NoSQL models for data analytics 78
      - 3.3.1 - Key Features of NoSQL 78
      - 3.3.2 - Classification of NoSQL Databases 80
      - 3.3.3 - NoSQL Systems 81
        
        3.3.3.1 - Dynamo 81
        
        3.3.3.2 - MongoDB 82
        
        3.3.3.3 - Bigtable 83
      - 3.3.4 - Use Cases 85
    - 3.4 - Summary 86
    - References 87
  - Chapter 4 - Designing and Supporting Scalable Data Analytics 91
    - 4.1 - Data analysis systems for clouds 91
      - 4.1.1 - Pegasus 92
      - 4.1.2 - Swift 93
      - 4.1.3 - Hunk 94
      - 4.1.4 - Sector/Sphere 95
      - 4.1.5 - BigML 96
      - 4.1.6 - Kognitio Analytical Platform 97
      - 4.1.7 - Mahout 98
      - 4.1.8 - Spark 99
      - 4.1.9 - Microsoft Azure Machine Learning 100
      - 4.1.10 - ClowdFlows 101
    - 4.2 - How to design a scalable data analysis framework in clouds 102
      - 4.2.1 - Architecture and Execution Mechanisms 103
      - 4.2.2 - Implementation on Microsoft Azure 105
    - 4.3 - Programming workflow-based data analysis 106
      - 4.3.1 - VL4Cloud 108
      - 4.3.2 - JS4Cloud 112
      - 4.3.3 - Workflow Patterns in DMCF 115
        
        4.3.3.1 - Single Task 115
        
        4.3.3.3 - Data Partitioning 116
        
        4.3.3.2 - Pipeline 116
        
        4.3.3.4 - Data Aggregation 118
        
        4.3.3.5 - Parameter Sweeping 119
        
        4.3.3.6 - Input Sweeping 120
        
        4.3.3.8 - Combination of Sweeping Patterns 122
        
        4.3.3.7 - Tool Sweeping 122
      - 4.4 - Data analysis case studies 124
        
        4.4.1 - Trajectory Mining Workflow Using VL4Cloud 124
        
        4.4.2 - Ensemble Learning Workflow Using JS4Cloud 127
        
        4.4.3 - Parallel Classification Using MapReduce in DMCF 129
        
        4.4.4 - Parallel Classification Using Swift 132
      - 4.5 - Summary 134
      - References 135
    - Chapter 5 - Research Trends in Big Data Analysis 137
      - 5.1 - Data-intensive exascale computing 137
        
        5.1.1 - Exascale Scalability in Data Analysis 138
        
        5.1.2 - Programming Issues for Exascale Data Analysis 140
      - 5.2 - Massive social network analysis 144
      - 5.3 - Key research areas 148
      - 5.4 - Summary 150
      - References 151
    - Back Cover 153

Author's affiliation

Domenico Talia: Professor of Computer Engineering, University
Paolo Trunfio: Assistant Professor in Information Processing
Fabrizio Marozzo: Department of Electronics, Computer Science a

Models, Techniques and Applications

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