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Big Data Analytics, 2ed

Radha Shankarmani

ISBN: 9788126565757

336 pages

INR 419

Description

The goal of this book is to cover foundational techniques and tools required for Big Data Analytics. It focuses on concepts, principles and techniques applicable to any technology environment and industry and establishes a baseline that can be enhanced further by additional real-world experience. This book aims to be a ready reckoner to either a novice or a professional working in the field.  Topics covered include Hadoop, MapReduce, Association Rules, Large-Scale Supervised Machine Learning, Data Streams, Clustering, NoSQL systems (Pig, Hive) and Applications including Recommendation Systems, Web and Security.

 

Preface

Acknowledgements

About the Authors

 

Chapter 1 Big Data Analytics

1.1 Introduction to Big Data

1.2 Big Data Characteristics

1.3 Types of Big Data

1.4 Traditional Versus Big Data Approach

1.5 Technologies Available for Big Data

1.6 Infrastructure for Big Data

1.7 Use of Data Analytics

1.8 Big Data Challenges

1.9 Desired Properties of a Big Data System

1.10 Case Study of Big Data Solutions

 

Chapter 2 Hadoop

2.1 Introduction

2.2 What is Hadoop?

2.3 Core Hadoop Components

2.4 Hadoop Ecosystem

2.5 Hive

2.6 Physical Architecture

2.7 Hadoop Limitations

 

Chapter 3 What is NoSQL?

3.1 What is NoSQL?

3.2 NoSQL Business Drivers

3.3 NoSQL Case Studies

3.4 NoSQL Data Architectural Patterns

3.5 Variations of NoSQL Architectural Patterns

3.6 Using NoSQL to Manage Big Data

 

Chapter 4 MapReduce

4.1 MapReduce and The New Software Stack

4.2 MapReduce

4.3 Algorithms Using MapReduce

 

Chapter 5 Finding Similar Items

5.1 Introduction

5.2 Nearest Neighbor Search

5.3 Applications of Nearest Neighbor Search

5.4 Similarity of Documents

5.5 Collaborative Filtering as a Similar-Sets Problem

5.6 Recommendation Based on User Ratings

5.7 Distance Measures

 

Chapter 6 Mining Data Streams

6.1 Introduction

6.2 Data Stream Management Systems

6.3 Data Stream Mining

6.4 Examples of Data Stream Applications

6.5 Stream Queries

6.6 Issues in Data Stream Query Processing

6.7 Sampling in Data Streams

6.8 Filtering Streams

6.10 Querying on Windows − Counting Ones in a Window

6.11 Decaying Windows

 

Chapter 7 Link Analysis

7.1 Introduction

7.2 History of Search Engines and Spam

7.3 PageRank

7.4 Efficient Computation of PageRank

7.5 Topic-Sensitive PageRank

7.6 Link Spam

7.7 Hubs and Authorities

 

Chapter 8 Frequent Itemset Mining

8.1 Introduction

8.2 Market-Basket Model

8.3 Algorithm for Finding Frequent Itemsets

8.4 Handling Larger Datasets in Main Memory

8.5 Limited Pass Algorithms

8.6 Counting Frequent Items in a Stream

 

Chapter 9 Clustering Approaches

9.1 Introduction

9.2 Overview of Clustering Techniques

9.3 Hierarchical Clustering

9.4 Partitioning Methods

9.5 The CURE Algorithm

9.6 Clustering Streams

 

Chapter 10 Recommendation Systems

10.1 Introduction

10.2 A Model for Recommendation Systems

10.3 Collaborative-Filtering System

10.4 Content-Based Recommendations

 

Chapter 11 Mining Social Network Graphs

11.1 Introduction

11.2 Applications of Social Network Mining

11.3 Social Networks as a Graph

11.4 Types of Social Networks

11.5 Clustering of Social Graphs

11.6 Direct Discovery of Communities in a Social Graph

11.7 SimRank

11.8 Counting Triangles in a Social Graph

 

Summary

Exercises

Programming Assignments

References

Appendix

Index