Big Data Analytics Tools and Technology for Effective Planning 1st Edition by Arun Somani, Ganesh Chandra Deka ISBN 9781315391243 1315391244

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ISBN 10: 1315391244
ISBN 13: 9781315391243
Author: Arun Somani, Ganesh Chandra Deka

Big Data Analytics Tools and Technology for Effective Planning 1st Edition Table of contents:

Chapter 1 Challenges in Big Data

Introduction

Background

Goals and Challenges of Analyzing Big Data

Paradigm Shifts

Organization of This Paper

Algorithms for Big Data Analytics

k-Means

Classification Algorithms: k-NN

Application of Big Data: A Case Study

Economics and Finance

Other Applications

Salient Features of Big Data

Heterogeneity

Noise Accumulation

Spurious Correlation

Coincidental Endogeneity

Impact on Statistical Thinking

Independence Screening

Dealing with Incidental Endogeneity

Impact on Computing Infrastructure

Literature Review

MapReduce

Cloud Computing

Impact on Computational Methods

First-Order Methods for Non-Smooth Optimization

Dimension Reduction and Random Projection

Future Perspectives and Conclusion

Existing Methods

Proposed Methods

Probabilistic Graphical Modeling

Mining Twitter Data: From Content to Connections

Late Work: Location-Specific Tweet Detection and Topic Summarization in Twitter

Tending to Big Data Challenges in Genome Sequencing and RNA Interaction Prediction

Single-Cell Genome Sequencing

RNA Structure and RNA–RNA Association Expectation

Identifying Qualitative Changes in Living Systems

Acknowledgments

References

Additional References for Researchers and Advanced Readers for Further Reading

Key Terminology and Definitions

Chapter 2 Challenges in Big Data Analytics

Introduction

Data Challenges

Storing the Data

Velocity of the Data

Data Variety

Computational Power

Understanding the Data

Data Quality

Data Visualization

Management Challenges

Leadership

Talent Management

Technology

Decision Making

Company Culture

Process Challenges

Introduction to Hadoop

Why Not a Data Warehouse for Big Data?

What Is Hadoop?

How Does Hadoop Tackle Big Data Challenges?

Storage Problem

Various Data Formats

Processing the Sheer Volume of Data

Cost Issues

Capturing the Data

Durability Problem

Scalability Issues

Issues in Analyzing Big Data

HDFS

Architecture

MapReduce

Hadoop: Pros and Cons

Other Big Data-Related Projects

Data Formats

Apache Avro

Apache Parquet

Data Ingestion

Apache Flume

Apache Sqoop

Data Processing

Apache Pig

Apache Hive

Apache Crunch

Apache Spark

Storage

HBase

Coordination

ZooKeeper

References

Chapter 3 Big Data Reference Model

Introduction into Big Data Management Reference Model

Information Visualization Based on the IVIS4BigData Reference Model

Interaction with Visual Data Views

Interaction with the Visualization Pipeline and Its Transformation Mappings

Introduction to the IVIS4BigData Reference Model

Introduction to Big Data Process Management Based on the CRISP4BigData Reference Model

The CRISP4BigData Reference Model

Data Collection, Management, and Curation

Analytics

Interaction and Perception

Deployment, Collaboration, and Visualization

Data Enrichment

Insight and Effectuation

Potentialities and Continuous Product and Service Improvement

Data Enrichment

Knowledge-Based Support

Knowledge Generation and Management

Retention and Archiving

Preparatory Operations for Evaluation of the CRISP4BigData Reference Model within a Cloud-Based Hadoop Ecosystem

Architecture Instantiation

Use Case 1: MetaMap Annotation of Biomedical Publications via Hadoop

Use Case 2: Emotion Recognition in Video Frames with Hadoop

Hadoop Cluster Installation in the EGI Federated Cloud

MetaMap Annotation Results

Conclusions and Outlook

References

Key Terminology and Definitions

Chapter 4 A Survey of Tools for Big Data Analytics

Survey on Commonly Used Big Data Tools

Potential Growth Versus Commitment for Big Data Analytics Options

Potential Growth

Commitment

Balance of Commitment and Potential Growth

Trends for Big Data Analytics Options

Group 1: Strong to Moderate Commitment, Strong Potential Growth

Advanced Analytics

Visualization

Real Time

In-Memory Databases

Unstructured Data

Group 2: Moderate Commitment, Good Potential Growth

Group 3: Weak Commitment, Good Growth

Hadoop Distributed File System (HDFS)

MapReduce

Complex Event Processing (CEP)

SQL

Clouds in TDWI Technology Surveys

Group 4: Strong Commitment, Flat or Declining Growth

Understanding Internet of Things Data

Challenges for Big Data Analytics Tools

Tools for Using Big Data

Jaspersoft BI Suite

Benefits

Pentaho Business Analytics

Karmasphere Studio and Analyst

Direct Access to Big Data for Analysis

Operationalization of the Results

Flexibility and Independence

Talend Open Studio

Skytree Server

Tableau Desktop and Server

Splunk

Splice Machine

Cost-Effective Scaling and Performance with Commodity Hardware

Real-Time Updates with Transactional Integrity

Conclusions

References

Chapter 5 Understanding the Data Science behind Business Analytics

Introduction

Types of Big Data Analytics

Descriptive Analytics

Diagnostic Analytics

Predictive Analytics

Prescriptive Analytics

Analytics Use Case: Customer Churn Prevention

Descriptive Analytics

Application of Descriptive Analytics in Customer Churn Prevention

Techniques Used for Descriptive Analytics

Diagnostic Analytics

Predictive Analytics

Techniques Used for Predictive Analytics

Machine Learning Techniques

Artificial Neural Networks

Supervised Learning

Artificial Neural Network Structure and Training

Back-Propagation Weight Adjustment Scheme

Prescriptive Analytics

Application of Prescriptive Analytics in the Customer Churn Prevention Use Case

Prescriptive Analytics Techniques

Big Data Analytics Architecture

Tools Used for Big Data Analytics

IBM InfoSphere

IBM SPSS

Apache Mahout

Azure Machine Learning Studio

Halo

Tableau

SAP Infinite Insight

@Risk

Oracle Advanced Analytics

TIBCO SpotFire

R

Wolfram Mathematica

Future Directions and Technologies

From Batch Processing to Real-Time Analytics

In-Memory Big Data Processing

Prescriptive Analytics

Conclusions

References

Online Sources

Chapter 6 Big Data Predictive Modeling and Analytics

Introduction

The Power of Business Planning with Precise Predictions

Predictive Modeling for Effective Business Planning: A Case Study

Effect of Big Data in Predictive Modeling

Predictive Modeling

Predictive Modeling Process

Selecting and Preparing Data

Fitting a Model

Feature Vectors

Estimating and Validating the Model

Types of Predictive Models

Linear and Nonlinear Regression

Types of Regression Algorithms

Decision Trees

Use of Decision Trees in Big Data Predictive Analytics

Inference

Random Forests

Use of Random Forests for Big Data

Support Vector Machines

Use of Support Vector Machines for Big Data Predictive Analytics

Unsupervised Models: Cluster Analysis

Cluster Analysis

Algorithms for Cluster Analysis

Use of Cluster Analysis for Big Data Predictions

Inference

Measuring Accuracy of Predictive Models

Target Shuffling

Lift Charts

ROC Curves

Bootstrap Sampling

Tools and Techniques Used for Predictive Modeling and Analytics

Data Mining Using CRISP-DM Technique

CRISP-DM Tool

Predictive Analytics Using R Open-Source Tool

Research Trends and Conclusion

References

Chapter 7 Deep Learning for Engineering Big Data Analytics

Introduction

Overview of Deep Learning as a Hierarchical Feature Extractor

Flow Physics for Inertial Flow Sculpting

Problem Definition

Design Challenges and the State of the Art

Broad Implications

Micropillar Sequence Design Using Deep Learning

Deep CNNs for Pillar Sequence Design

Action Sequence Learning for Flow Sculpting

Representative Training Data Generation

Summary, Conclusions, and Research Directions

References

Chapter 8 A Framework for Minimizing Data Leakage from Nonproduction Systems

Introduction

Nonproduction Environments

Legal, Business, and Human Factors

Existing Frameworks, Solutions, Products, and Guidelines

Limitations

Research for Framework Development

Research the Use and Protection of Data in Nonproduction Systems

Freedom of Information: An Organizational View

Questionnaire: Opinion

Simplified Business Model

Six Stages of the Framework, Detailing from Organization to Compliance

Know the Legal and Regulatory Standard

Know the Business Data

Know the System

Know the Environment

Data Treatment and Protection

Demonstrate Knowledge

Tabletop Case Study

Hypothetical Case Study Scenario

Discussions Using the Simplified Business Model and Framework

Know the Legal and Regulatory Standards

Know the Business Data

Know the System

Know the Environment

Data Treatment and Protection

Demonstrate Knowledge

Summary of the Impact of the Framework on the Case Study

Conclusions

Glossary

References

Chapter 9 Big Data Acquisition, Preparation, and Analysis Using Apache Software Foundation Tools

Introduction

Data Acquisition

Freely Available Sources of Data Sets

Data Collection through Application Programming Interfaces

Web Scraping

Create the Scraping Template

Explore Site Navigation

Automate Navigation and Extraction

Web Crawling

Data Preprocessing and Cleanup

Need for Hadoop MapReduce and Other Languages for Big Data Preprocessing

When to Choose Hadoop over Python or R

Comparison of Hadoop MapReduce, Python, and R

Cleansing Methods and Routines

Loading Data from Flat Files

Merging and Joining Data Sets

Query the Data

Data Analysis

Big Data Analysis Using Apache Foundation Tools

Various Language Support via Apache Hadoop

Apache Spark for Big Data Analytics

Case Study: Dimensionality Reduction Using Apache Spark, an Illustrative Example from Scientometric Big Data

Why SVD? The Solution May Be the Problem

Need for SVD

Relationship between Singular Values and the Input Matrix

SVD Using Hadoop Streaming with Numpy

Complexity of Using MapReduce

SVD Using Apache Mahout

Generating Sequence Files for Mahout

Lanczo’s Algorithm

Reading the Mahout-Generated Vectors

SVD Using Apache Spark

Data Analysis through Visualization

Why Big Data Visualization Is Challenging and Different from Traditional Data Visualization

Conclusions

References

Appendix 1

Spark Implementation and Configuration

Building Apache Spark Using Maven

Setting Up Maven’s Memory Usage

build/mvn

Required Cluster Configurations for Spark

Key Terminology and Definitions

Chapter 10 Storing and Analyzing Streaming Data: A Big Data Challenge

Introduction

Streaming Algorithms

Modeling Data Pipeline Problems

Vanilla Model

Sliding Window Model

Turnstile Model

Cash Register Model

Probablistic Data Structures and Algorithms

Bloom Filter

Count-Min Sketch

HyperLogLog

T Digest

Filtered Space Saving

Suitability of Data Models for Storing Stream Data

Key Value Stores Model

Document Stores Model

Wide-Column Stores Model

Other Models

Challenges and Open Research Problems

Industrial Challenges

Open Research Problems

Conclusions

References

Further Readings

Chapter 11 Big Data Cluster Analysis: A Study of Existing Techniques and Future Directions

Introduction

Overview of Cluster Analysis

An Illustrative Example

Similarity

Termination

Types of Clustering Techniques

Hierarchical Clustering

Overview

Agglomerative Hierarchical Clustering

Divisive Clustering

ROCK Clustering Technique

Expected Effects of the Three Vs on the ROCK Clustering Technique

Density-Based Clustering

Overview

DBSCAN Clustering Technique

Expected Effects of the Three Vs on the DBSCAN Clustering Technique

Partitioning-Based Clustering

Overview

k-Means Clustering Technique

Expected Effects of 3 Vs on the k-Means Clustering Technique

Grid-Based Clustering

Overview

CLIQUE Clustering Technique

Expected Effects of the Three Vs on the CLIQUE Clustering Technique

Miscellaneous Clustering Techniques

Future Directions for Big Data Cluster Analysis Research

Distributed Techniques

Density-Based Distributed Clustering

Graph Partitioning-Based Clustering

MapReduce-Based k-Means-Type Clustering

Robust Techniques

Clustering Engines

Conclusions

References

Key Terminology and Definitions

Chapter 12 Nonlinear Feature Extraction for Big Data Analytics

Introduction

Traditional Methods: Feature Extraction with PCA

Algorithm

PCA for Big Data

Kernel Methods

Problems Associated with Kernel Methods

Kernel Functions as New Features

Kernel Properties

The Most Common Kernel Functions

Selecting Basis Elements

Random Sampling

k-Means

Sparse Greedy Matrix Approximation

Example of Semiparametric SVMs

Stochastic Gradient Descent

Iterative Reweighted Least Squares

Deep Feature Learning

Auto-Encoders

Measuring Discrepancies in AE

Architecture of AEs

Regularizing AEs

Learning

Constructing Deep Structures

Benefits for Big Data

Probabilistic Models

Learning

Contrastive Divergence

Constructing Deep Structures

Deep Distributed Learning

Data Parallelism

Model Parallelism

Data and Model Parallelism

Updating Parameters in Large-Scale Networks

Synchronous Mode

Asynchronous Mode

Ensemble Methods

Nontrainable Aggregation

Trainable Aggregation

References

Chapter 13 Enhanced Feature Mining and Classifier Models to Predict Customer Churn for an e-Retailer

Introduction

Enterprise Data Pipeline (Hadoop Stack)

Stages of the Hadoop Pipeline

Source

Collection

Process

Store

Extract

Existing Models for Customer Churn

Customer Churn Model

Phase 1: Feature Mining

Phase 2: Data Science Model Building

Phase 3: Cross-Validation, Business Action, and Performance Tuning

Feature Engineering

Feature Mining

Demographic Features

Customer Information

Customer Sales

Product Sales

Frequency

Behavioral

Experience

Feature Selection

Baseline Methods

Imbalance in Class Label Outputs

F-ANOVA

Regularization

Binomial Classifier

SVM

Logistic Regression with L1 Norm

Gradient-Boost Ensemble

Cross-Validation

k-Fold Strategy for Cross-Validation

Receiver Operating Characteristics Curve

Conclusions and Future Work

References

Key Terminology and Definitions

Chapter 14 Large-Scale Entity Clustering Based on Structural Similarities within Knowledge Graphs

Introduction

Background: Big Data and Analytics

Definitions of Big Data

Characteristics of Big Data

Gartner’s Three Vs

IBM’s Four Vs

Microsoft’s Six Vs

Analytics

Background: Knowledge Graphs

Motivation: Entity Clustering in Large-Scale Knowledge Graphs

Data Description

Overview

Freebase Data Model

Knowledge Representation in Freebase

Computing Clusters

Data Preprocessing

Formulation of the Similarity Metric

Selection of Clustering Algorithm

Clustering Experiments

Results and Discussion

Evaluation of Cluster Quality

Cluster Analysis

Future Work: Exploitation of Clusters for Topic Exploration

Related Work

Methodology Limitations

Conclusions

References

Chapter 15 Big Data Analytics for Connected Intelligence with the Internet of Things

Introduction

Iot Paradigm

Main Elements of IoT

Popular Applications of IoT

Security Challenges in the IoT Network

A Brief Description of the Intelligence Definition

A Model of Systematic Intelligence Formation from Natural Intelligence

Foundations of Human–Computer Interactions

Intelligent Frameworks in IoT

Big Data Analytics for Intelligence in the IoT

Data Storage and Processing Modules

The Key Devices of Intelligence in IoT

The Fundamental Mechanisms for Big Data Analytics

Big Data Analytics Methods for Connected Intelligence with IoT

Evaluation Results

Conclusions and Future Work

References

Key Terminology and Definitions

Chapter 16 Big Data-Driven Value Chains and Digital Platforms: From Value Co-creation to Monetization

Introduction

Big Data-Driven Value Chains

Creation (Data Capture)

Storage (Data Warehousing)

Processing (Data Mining and Fusion)

Consumption (Sharing)

Assembling Value via Heterogeneous Data Fusion and Interoperability

Intermediating Digital Platforms

Linking Big Data with Network Theory

Big Data-Driven Business Planning

Monetization Strategies

Conclusions

References

Chapter 17 Distant and Close Reading of Dutch Drug Debates in Historical Newspapers: Possibilities and Challenges of Big Data Analysis in Historical Public Debate Research

Introduction

Big Data and Digital Humanities

Public Debate Research

Dutch Historical Newspapers as Big Data

Discourse Analysis and Governmentality

A Leveled Approach

Research Demonstration: Public Perception of Amphetamine in Dutch Newspapers

Regulation and Public Perception of Amphetamine

Modularity and the Cross-Media Challenge

Conclusions

References

Index

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