Data Driven Decision Making using Analytics (Computational Intelligence Techniques)

Cover
Half Title
Series Information
Title Page
Copyright Page
Table of Contents
Preface
List of Contributors
Editors’ Biography
1 Securing Big Data Using Big Data Mining
	1.1 Big Data
		1.1.1 Big Data V’s
			1.1.1.1 Volume
			1.1.1.2 Variety
			1.1.1.3 Velocity
			1.1.1.4 Veracity
			1.1.1.5 Validity
			1.1.1.6 Visualization of Big Data
			1.1.1.7 Value
			1.1.1.8 Big Data Hiding
		1.1.2 Challenges With Big Data
		1.1.3 Analytics of Big Data
			1.1.3.1 Use Cases Used in Big Data Analytics
		1.1.4 Social Media Analysis and Response
			1.1.4.1 IoT – Preventive Maintenance and Support
			1.1.4.2 Healthcare
			1.1.4.3 Insurance Fraud
		1.1.5 Big Data Analytics Tools
			1.1.5.1 Hadoop
			1.1.5.2 MapReduce Optimize
			1.1.5.3 HBase Hadoop Structure
			1.1.5.4 Hive Warehousing Tool
			1.1.5.5 Pig Programming
			1.1.5.6 Mahout Sub-Project Apache
			1.1.5.7 Non-Structured Query Language
			1.1.5.8 Bigtable
		1.1.6 Security Threats for Big Data
		1.1.7 Big Data Mining Algorithms
		1.1.8 Big Data Mining for Big Data Security
			1.1.8.1 Securing Big Data
			1.1.8.2 Real-Time Predictive and Active Intrusion Detection Systems
			1.1.8.3 Securing Valuable Information Using Data Science
			1.1.8.4 Pattern Discovery
			1.1.8.5 Automated Detection and Response Using Data Science
		1.1.9 Conclusions
	References
2 Analytical Theory: Frequent Pattern Mining
	2.1 Introduction
	2.2 Frequent Pattern Mining Algorithms
		2.2.1 Apriori Algorithm
		2.2.2 DHP Algorithm
		2.2.3 FP-Growth Algorithm
		2.2.4 EClaT Algorithm
		2.2.5 Tree Projection Algorithm
		2.2.6 TM Algorithm
		2.2.7 P-Mine Algorithm
		2.2.8 Can-Mining Algorithm
	2.3 Analysis of the Algorithms
	2.4 Privacy Issues
	2.5 Applications of FPM
		2.5.1 For Customer Analysis
		2.5.2 Frequent Patterns for Classification
		2.5.3 Frequent Patterns Aimed at Clustering
		2.5.4 Frequent Patterns for Outlier Analysis
		2.5.5 Frequent Patterns for Indexing
		2.5.6 Frequent Patterns for Text Mining
		2.5.7 Frequent Patterns for Spatial and Spatiotemporal Applications
		2.5.8 Applications in Chemical and Biological Fields
	2.6 Resources Available for Practitioner
	2.7 Future Works and Conclusion
	References
3 A Journey From Big Data to Data Mining in Quality Improvement
	3.1 Introduction
		3.1.1 Comparing Conventional Data Technique and Big Data Technique
	3.2 Big Data Technique Types
		3.2.1 Structured Big Data Type
		3.2.2 Unstructured Big Data Type
		3.2.3 Semi-Structured Big Data Type
	3.3 Essence of Big Data
		3.3.1 Volume
		3.3.2 Variety
		3.3.3 Velocity
		3.3.4 Variability
		3.3.5 Value
	3.4 Categorization of Data Mining Systems
		3.4.1 Classification On the Basis of the Type of Data Source That Is Mined
		3.4.2 Classification On the Basis of King of Knowledge Discovered
		3.4.3 Classification On the Basis of the Data Model On Which It Is Drawn
		3.4.4 Classification According to Different Mining Techniques That Are Used
	3.5 Data Mining Design
		3.5.1 Data Source
		3.5.2 Data Warehouse Server
		3.5.3 Data Mining Engine
		3.5.4 Pattern Assessment Module
		3.5.5 Graphical User Interface (GUI)
		3.5.6 Knowledge Base
	3.6 Data Mining Architecture
		3.6.1 Issues and Dilemma of Big Data Technique
			3.6.1.1 Dilemma
			3.6.1.2 Issues
			3.6.1.3 Solution of Big Data Technique
			3.6.1.4 Hadoop Distributed File System
			3.6.1.5 MapReduce
	3.7 Various Data Mining Techniques to Improve Data Quality
		3.7.1 Anomaly Detection
		3.7.2 Clustering
		3.7.3 Classification
		3.7.4 Regression
	3.8 Conclusion
	References
4 Significance of Data Mining in the Domain of Intrusion Detection
	4.1 Introduction
	4.2 Classification of Intrusion Detection Systems
		4.2.1 Network-Based IDS
		4.2.2 Host-Based IDS
		4.2.3 Application-Based IDS
		4.2.4 IDS Analysis
		4.2.5 Misuse Detection
		4.2.6 Response Options for IDS
		4.2.7 Collect Additional Information
		4.2.8 Take Action Against the Intruder
		4.2.9 Passive Responses
	4.3 Intrusion Detection Architecture
	4.4 IDS Products
		4.4.1 Research Products
			4.4.1.1 Emerald
			4.4.1.2 NetStat
			4.4.1.3 Bro
			4.4.1.4 NetProwler
			4.4.1.5 NetRanger
		4.4.2 Public Domain Tools
			4.4.2.1 Tripwire
			4.4.2.2 SNORT
			4.4.2.3 Network Flight Recorder
			4.4.2.4 Intrusion Detection Government Off-The-Shelf (GOTS) Products
	4.5 Types of Computer Attacks Commonly Detected By the IDS
		4.5.1 Scanning Attacks
		4.5.2 Denial of Service (DOS) Attacks
		4.5.3 Penetration Attacks
	4.6 Significant Gaps and Future Directions for IDS
	4.7 Data Mining for Intrusion Detection
		4.7.1 ADAM
		4.7.2 MADAM ID
		4.7.3 MINDS
		4.7.4 Clustering of Unlabeled ID
		4.7.5 Alert Correlation
	4.8 Conclusions
	References
5 Data Analytics and Mining: Platforms for Real-Time Applications
	5.1 Introduction
	5.2 Real-Time Analytics
		5.2.1 Stream Processing
		5.2.2 In-Memory Computing
	5.3 Applications of Real-Time Analytics
	5.4 A Brief Introduction to Real-Time Big Data Architectures
		5.4.1 Lambda Architecture
		5.4.2 Kappa Architecture
	5.5 Real-Time Analytics Platforms
		5.5.1 Open-Source Platforms
			5.5.1.1 Apache Storm
			5.5.1.2 Apache Spark Streaming
			5.5.1.3 Apache Samza
			5.5.1.4 Apache Flink
			5.5.1.5 Apache Kafka
			5.5.1.6 Apache SAMOA
		5.5.2 Commercial Platforms
			5.5.2.1 Amazon Kinesis
			5.5.2.2 Azure Stream Analytics
			5.5.2.3 IBM Streams
	5.6 Comparison of Open-Source Real-Time Analytics Platforms
	5.7 Conclusions
	References
6 Analysis of Government Policies to Control Pandemic and Its Effects On Climate Change to Improve Decision Making
	6.1 Introduction
	6.2 Methodology
		6.2.1 World Health Organization Criteria for Relaxing Physical Distancing Measures
		6.2.2 Data Mining
		6.2.3 Data Analytics
			6.2.3.1 Formulas for Calculating Metrics
			6.2.3.2 Analyzing Trends in Air Quality Index (AQI) Across Several Indian Cities
	6.3 Results and Discussions
		6.3.1 Countries With Maximum Readiness for Easing Response Policies
		6.3.2 AQI for Some of the Major Cities of India
		6.3.3 AQI Before and After Lock-Down
			6.3.3.1 Before Lock-Down
			6.3.3.2 After Lock-Down
	6.4 Conclusion
	References
7 Data Analytics and Data Mining Strategy to Improve Quality, Performance and Decision Making
	7.1 Introduction
	7.2 Database
	7.3 Data Quality
	7.4 Data Analytics
		7.4.1 Why Data Analytics?
		7.4.2 Role of Data Analytics
			7.4.2.1 Data Mining
			7.4.2.2 Data Cleaning
			7.4.2.3 Data Analysing
			7.4.2.4 Create Report With a Visualization Technique
			7.4.2.5 Database Maintaining
			7.4.2.6 Data Systems
		7.4.3 Sample Data Analysis
		7.4.4 Main Challenges Using Data Analytics
			7.4.4.1 Unstructured Storage of Data
			7.4.4.2 Data From Multiple Sources
			7.4.4.3 Huge Amount of Data
			7.4.4.4 Management Pressure
			7.4.4.5 Data Inadequacy
			7.4.4.6 Lack of Respective Business Knowledge
			7.4.4.7 Lack of Data Maintenance
		7.4.5 Analytics for Better Decision Making
			7.4.5.1 Exploiting Information to Drive Performance
			7.4.5.2 Utilizing More Concepts of Consumer Patterns
			7.4.5.3 Governing Risk Through Analytics
			7.4.5.4 Bottom Line Growth
		7.4.6 Statistical Analysis
		7.4.7 Analytical Tools
	7.5 Data Mining
		7.5.1 Techniques Involved in Data Mining
			7.5.1.1 Classification
			7.5.1.2 Clustering
			7.5.1.3 Regression
			7.5.1.4 Association Rules
			7.5.1.5 Sequential Patterns
			7.5.1.6 Prediction
		7.5.2 Functional Areas of Data Mining
		7.5.3 Data Mining Tools
		7.5.4 Business Intelligence in Data Mining
		7.5.5 Anomaly in Data Mining
		7.5.6 Data Mining Model
			7.5.6.1 Forecasting Method
			7.5.6.2 Risk and Probability
			7.5.6.3 Recommendations
			7.5.6.4 Sequence Discovery
			7.5.6.5 Grouping
	7.6 Decision Making
		7.6.1 Methodologies to Enhance the Decision Making
		7.6.2 Data-Driven Decision Making
		7.6.3 Recent Technologies
	7.7 Conclusions
	References
8 SMART Business Model: An Analytical Approach to Astute Data Mining for Successful Organization
	8.1 Introduction
		8.1.1 Big Data Approach?
		8.1.2 Who Is Using Big Data?
		8.1.3 Big Data Maturity Model
	8.2 Smart Integrated Business Model (SIBM)
		8.2.1 SMART Questions That Help You and Your Team
			8.2.1.1 The Purpose Panel
			8.2.1.2 The Customer Panel
			8.2.1.3 The Finance Panel
			8.2.1.4 The Operations Panel
			8.2.1.5 The Resource Panel
			8.2.1.6 The Competition and Risk Panel
	8.3 Conclusion
	References
9 AI and Healthcare: Praiseworthy Aspects and Shortcomings
	9.1 Introduction
	9.2 How AI Works in Healthcare?
	9.3 Medical Imaging and Diagnosis
	9.4 NLP: A Natural Solution to Healthcare Problems
	9.5 Deep Learning in Drug Discovery
	9.6 Health Monitoring Using AI
	9.7 Conclusion
	References
Index