Data Warehousing OLAP and Data Mining 1st Edition by S Nagabhushana ISBN 8122417647 9788122417647 by S. Nagabhushana 9788122417647, 8122417647 instant download after payment.

Data Warehousing OLAP and Data Mining 1st Edition by S Nagabhushana - Ebook PDF Instant Download/Delivery: 8122417647, 9788122417647
Full download Data Warehousing OLAP and Data Mining 1st Edition after payment

Product details:

ISBN 10: 8122417647 
ISBN 13: 9788122417647
Author:  S Nagabhushana

A "Table of Contents" for a book on "Data Warehousing, OLAP, and Data Mining" would typically be structured to guide the reader from foundational concepts through the practical implementation and application of these interconnected fields.

Data Warehousing OLAP and Data Mining 1st Table of contents:

Part I: Introduction and Foundations of Data Warehousing

• Chapter 1: Introduction to Data Warehousing
  • What is a Data Warehouse?
  • Evolution of Data Warehousing
  • Characteristics of a Data Warehouse
  • Differences between OLTP and OLAP Systems
  • Benefits and Challenges of Data Warehousing
  • Data Warehouse Architecture Overview
• Chapter 2: Data Warehouse Components and Architecture
  • Source Systems and Data Extraction
  • Data Staging Area (ETL/ELT Processes)
  • Data Warehouse Database (Star Schema, Snowflake Schema, Fact Constellation)
  • Metadata Repository
  • Data Marts (Dependent vs. Independent)
  • Operational Data Stores (ODS)
  • Data Warehouse Appliances
• Chapter 3: Data Modeling for Data Warehousing
  • Dimensional Modeling Concepts
  • Fact Tables (Additive, Semi-Additive, Non-Additive Measures)
  • Dimension Tables (Conformed Dimensions, Role-Playing Dimensions, Junk Dimensions)
  • Hierarchies and Attributes
  • Slowly Changing Dimensions (SCD Type 1, 2, 3, 4, 6)
  • Designing Star and Snowflake Schemas
• Chapter 4: Data Extraction, Transformation, and Loading (ETL/ELT)
  • Overview of the ETL/ELT Process
  • Data Extraction Techniques (Full vs. Incremental Load)
  • Data Transformation Techniques (Cleaning, Conforming, Deriving, Aggregating)
  • Data Loading Strategies (Initial Load, Incremental Load, Refresh)
  • ETL Tools and Technologies
  • Monitoring and Managing ETL Processes
• Chapter 5: Data Quality and Governance in Data Warehousing
  • Importance of Data Quality
  • Data Profiling and Cleansing
  • Data Conformance and Standardization
  • Data Governance Frameworks for Data Warehousing
  • Master Data Management (MDM) Principles

Part II: Online Analytical Processing (OLAP)

• Chapter 6: Introduction to OLAP
  • What is OLAP?
  • Key Characteristics of OLAP Systems
  • OLAP Operations (Slice, Dice, Roll-up, Drill-down, Pivot/Rotate)
  • Benefits of OLAP for Business Intelligence
• Chapter 7: Types of OLAP Systems
  • MOLAP (Multidimensional OLAP) - Cubes, Array-based storage
  • ROLAP (Relational OLAP) - Stored in relational databases
  • HOLAP (Hybrid OLAP)
  • DOLAP (Desktop OLAP)
  • Choosing the Right OLAP Architecture
• Chapter 8: OLAP Tools and Applications
  • Overview of Commercial OLAP Tools (e.g., Microsoft SSAS, Oracle OLAP, IBM Cognos, Tableau)
  • Building and Querying OLAP Cubes
  • MDX (Multidimensional Expressions) for Querying Cubes
  • Reporting and Dashboarding using OLAP data
  • Performance Considerations in OLAP

Part III: Data Mining

• Chapter 9: Introduction to Data Mining
  • What is Data Mining?
  • KDD (Knowledge Discovery in Databases) Process
  • Goals and Applications of Data Mining
  • Data Mining Tasks (Prediction, Classification, Clustering, Association Rule Mining)
  • Ethical Issues in Data Mining
• Chapter 10: Data Preprocessing for Data Mining
  • Data Cleaning (Missing Values, Noise)
  • Data Integration (Schema Integration, Redundancy)
  • Data Transformation (Normalization, Aggregation)
  • Data Reduction (Dimensionality Reduction, Numerosity Reduction)
  • Data Discretization and Concept Hierarchy Generation
• Chapter 11: Classification Techniques
  • Introduction to Classification
  • Decision Trees (ID3, C4.5, CART)
  • Naïve Bayes Classifiers
  • Support Vector Machines (SVMs)
  • k-Nearest Neighbors (k-NN)
  • Evaluation of Classifiers (Accuracy, Precision, Recall, F1-score, ROC Curves)
• Chapter 12: Clustering Techniques
  • Introduction to Clustering
  • Partitioning Methods (k-Means, k-Medoids)
  • Hierarchical Methods (Agglomerative, Divisive)
  • Density-Based Methods (DBSCAN)
  • Evaluation of Clustering Results
• Chapter 13: Association Rule Mining
  • Introduction to Association Rules
  • Apriori Algorithm
  • Frequent Itemset Generation (Support, Confidence, Lift)
  • FP-Growth Algorithm
  • Applications of Association Rules (Market Basket Analysis)
• Chapter 14: Other Data Mining Techniques
  • Regression Analysis
  • Outlier Detection
  • Time Series Analysis
  • Sequence Mining
  • Text Mining and Web Mining (brief overview)
• Chapter 15: Data Mining Tools and Applications
  • Overview of Data Mining Software (e.g., R, Python libraries, Weka, SAS, IBM SPSS Modeler)
  • Case Studies and Real-World Applications of Data Mining
  • Integrating Data Mining with Business Intelligence

Part IV: Integration and Future Trends

• Chapter 16: Integrating Data Warehousing, OLAP, and Data Mining
  • How the Components Work Together
  • The Role of the Data Warehouse in Data Mining
  • Leveraging OLAP for Data Mining Results
  • Building an End-to-End Business Intelligence Solution
• Chapter 17: Big Data and Advanced Analytics
  • Introduction to Big Data (Volume, Velocity, Variety, Veracity)
  • Hadoop and Spark Ecosystems
  • Cloud-Based Data Warehousing and Analytics Platforms
  • Real-Time Analytics
  • Machine Learning and Deep Learning in Business Intelligence
• Chapter 18: Emerging Trends and Future Directions
  • Data Lakehouses
  • Data Mesh Architectures
  • Automated Machine Learning (AutoML)
  • Ethical AI and Responsible Data Mining
  • The Future of Business Intelligence and Analytics

People also search for Data Warehousing OLAP and Data Mining 1st:

data warehousing olap and data mining
    
olap in data mining and data warehousing
    
olap data cube
    
hadoop data warehouse
    
data warehouse and olap technology in data mining

Tags: S Nagabhushana, Warehousing, OLAP