Big Data Analytics

Author : Radha Shankarmani, M. Vijayalakshmi
Price : Rs 379.00
ISBN 13 : 9788126558650
ISBN 10 : 8126558652
Pages : 328
Type : Paperbound

9788126558650

Details

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.

 

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 Case Study of Big Data Solutions

 

2 Hadoop

2.1 Introduction

2.2 What is Hadoop?

2.3 Core Hadoop Components

2.4 Hadoop Ecosystem

2.5 Physical Architecture

2.6 Hadoop Limitations

 

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

 

4 MapReduce

4.1 MapReduce and The New Software Stack

4.2 MapReduce

4.3 Algorithms Using MapReduce

 

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

 

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

6.9 Counting Distinct Elements in a Stream

6.10 Querying on Windows − Counting Ones in a Window

6.11 Decaying Windows

 

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

 

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

 

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

 

10 Recommendation Systems

10.1 Introduction

10.2 A Model for Recommendation Systems

10.3 Collaborative-Filtering System

10.4 Content-Based Recommendations

 

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

 

Primary Market

 

Undergraduate and graduate level.

 

Dr. Radha Shankarmani is currently working as Professor and Head at Department of Information Technology, Sardar Patel Institute of Technology, Mumbai. Her areas of interest include Business Intelligence, Software Engineering, Software Testing, Databases, Data Warehousing and Mining, Computer Simulation and Modeling, Management Information System and SOA.

 

Dr. M. Vijayalakshmi is Professor of Information Technology at VES Institute of Technology Mumbai. Currently she is also the Vice Principal of the college. She has more than 25 years of teaching experience both at undergraduate and postgraduate engineering level.