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Data Warehouse and Data Mining  
Concepts, techniques and real life applications (English Edition)
Author(s): Dr. Jugnesh Kumar
Published by BPB Publications
ISBN: 9789355517340
Pages: 214
https://doi.org/9789355517340

EBOOK (EPUB)

ISBN: 9789355517340   Price: INR 699.00
  
 

Description

Table of contents

Subject(s): Data warehouse, Data warehouse implementation, Data mining, Data mining query languages, Data mining techniques, Mining complex data objects
Data warehouse and data mining are essential technologies in the field of data analysis and business intelligence. Data warehouse provides a centralized repository of structured data and facilitates data storage and retrieval. Data mining, on the other hand, utilizes various algorithms and techniques to extract valuable patterns, trends, and insights from large datasets. The book explains the ins and outs of data warehousing by discussing its principles, benefits, and components, differentiating it from traditional databases. The readers will explore warehouse architecture, learn to navigate OLTP and OLAP systems, grasping the crux of the difference between ROLAP and MOLAP. The book is designed to help you discover data mining secrets with techniques like classification and clustering. You will be able to advance your skills by handling multimedia, time series, and text, staying ahead in the evolving data mining landscape. By the end of this book, you will be equipped with the skills and knowledge to confidently translate business questions into actionable strategies, extracting valuable insights for informed decisions.
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Description
Subject(s): Data warehouse, Data warehouse implementation, Data mining, Data mining query languages, Data mining techniques, Mining complex data objects
Data warehouse and data mining are essential technologies in the field of data analysis and business intelligence. Data warehouse provides a centralized repository of structured data and facilitates data storage and retrieval. Data mining, on the other hand, utilizes various algorithms and techniques to extract valuable patterns, trends, and insights from large datasets. The book explains the ins and outs of data warehousing by discussing its principles, benefits, and components, differentiating it from traditional databases. The readers will explore warehouse architecture, learn to navigate OLTP and OLAP systems, grasping the crux of the difference between ROLAP and MOLAP. The book is designed to help you discover data mining secrets with techniques like classification and clustering. You will be able to advance your skills by handling multimedia, time series, and text, staying ahead in the evolving data mining landscape. By the end of this book, you will be equipped with the skills and knowledge to confidently translate business questions into actionable strategies, extracting valuable insights for informed decisions.
Table of contents
Table of Contents
  1. Cover
  2. Title Page
  3. Copyright Page
  4. Dedication Page
  5. About the Author
  6. About the Reviewer
  7. Acknowledgement
  8. Preface
  9. Table of Contents
  10. 1. Introduction to Data Warehousing
    1. Introduction
    2. Structure
    3. Objectives
    4. Data warehousing
    5. History of data warehouse
      1. Decision support system development (1970s–1980s)
      2. Online analytical processing was first introduced in the 1980s
      3. Concepts for data warehousing in the 1980s and 1990s
      4. Ralph Kimball’s dimensional modelling first appeared in the 1990s
      5. ETL and data integration advancements between the 1990s and 2000s
      6. Columnar databases and in-memory computing adoption between 2000 and 2010
      7. Big Data and Cloud data warehousing (2010s–present)
    6. Data warehouse works
      1. Sources of data
        1. Extract, Transform, and Load
        2. Data organization and storage
    7. Types of data warehouses
      1. Enterprise data warehouse
      2. Data mart
      3. Operational data store
      4. General stages of data warehouse
        1. Analysis of requirements
        2. Modelling data
        3. Extraction of data
        4. Data loading
        5. Transformation of data
        6. Data management and storage
        7. Management of metadata
        8. Analysis and querying
        9. Upkeep and evolution
      5. Need of data warehouse
    8. Uses and trends of data warehouse
      1. Data warehouse applications in various industries
        1. Banking
        2. Retail
        3. Healthcare
        4. Marketing
        5. Manufacturing
        6. Telecommunications
      2. Trends of data warehouse
    9. Database management system versus data warehouse
    10. Metadata
      1. Types of metadata
        1. Descriptive metadata
        2. Administrative metadata
        3. Structural metadata
        4. Technical metadata
        5. Provenance metadata
        6. Rights metadata
        7. Preservation metadata
      2. Role of metadata
      3. Metadata repository
      4. Benefits of metadata
      5. Challenges for metadata management
    11. Multidimensional data model
    12. Data cubes
      1. Data cube classification
      2. Operations in data cubes
      3. Advantages of data cubes
      4. Data cube disadvantages
    13. Schemas for multidimensional database
      1. Star schema
      2. Snowflake schema
      3. Star Schema
        1. Fact table
      4. Snowflake schema
        1. Fact table
      5. Fact constellation in data warehouse
        1. General structure of fact constellation
        2. Fact constellation schema architecture
    14. Conclusion
    15. Exercises
  11. 2. Data Warehouse Process and Architecture
    1. Introduction
    2. Structure
    3. Objectives
    4. Objectives of data warehouse architecture
      1. Single-tier architecture
      2. Two-tier architecture
      3. Three-tier architecture
      4. Properties of data warehouse architecture
    5. Types of data warehouse architecture
      1. Single-tier architecture
      2. Two-tier architecture
      3. Three-tier architecture
    6. Advantages of data warehouse architecture
    7. Disadvantages of data warehouse architecture
    8. Data warehouse database
    9. Online transaction processing and online analytical processing
      1. Online transaction processing
      2. Online analytical processing
      3. Advantages and disadvantages of Online transaction processing and online analytical processing
      4. Types of online analytical processing
      5. Examples of all types, advantages and disadvantages of different OLAP systems
    10. Servers
    11. Data warehouse manager
    12. Conclusion
    13. Exercises
  12. 3. Data Warehouse Implementation
    1. Introduction
    2. Structure
    3. Objectives
    4. Introduction of data warehouse implementation
    5. Data warehouse implementation guidelines
    6. Components of data warehouse implementation
    7. Benefits of implementing a data warehouse
    8. Computation of data cubes
      1. Subtracting two cubes for differences
    9. Modeling Online Analytical Processing data
    10. Online Analytical Processing queries manager
    11. Data warehouse back-end tools
    12. Complex aggregation at multiple granularities
    13. Tuning and testing of data warehouse
      1. Tuning and testing of data warehouse: Enhancing performance and reliability
    14. Conclusion
    15. Exercises
  13. 4. Data Mining Definition and Task
    1. Introduction
    2. Structure
    3. Objectives
    4. Introduction to data mining
      1. Defining business objectives/problem definition
        1. Importance
        2. Stakeholder meetings
        3. Problem definition
        4. Prioritization
        5. Scope definition
        6. Deliverables
        7. Benefits
    5. Data mining tasks
      1. Data mining versus data analysis
    6. Data mining functionality
    7. Knowledge Discovery in Databases versus data mining
    8. Data mining techniques
      1. Classification
      2. Clustering
      3. Association rule mining
      4. Regression analysis
        1. Correlation versus causation
      5. Anomaly detection
        1. Global outliers
        2. Contextual outliers
        3. Collective outliers
      6. Sequence pattern mining
    9. Text mining
    10. Data mining tools and applications
    11. Conclusion
    12. Exercises
  14. 5. Data Mining Query Languages
    1. Introduction
    2. Structure
    3. Objectives
    4. Introduction to data mining query languages
    5. Syntax of Data Mining Query Languages
      1. Structured Query Language
        1. GROUP BY
        2. ORDER BY
        3. WINDOW operations (using OVER)
        4. Creating a view as part of data preprocessing
        5. Various clauses to explore the attributes or dimensions
      2. Data Mining Query Language
      3. Multidimensional Expressions
        1. MDX query clauses
        2. MDX functions
      4. Datalog
      5. XQuery
        1. XPath and XQuery
        2. CSS selectors
        3. Web scraping libraries
        4. JSONPath
        5. SPARQL
        6. RESTful APIs
        7. Web Scraping Frameworks
        8. Graph Query Languages
    6. Standardization of Data Mining Languages
    7. Data specification
      1. DMQL for characterization
      2. DMQL for discrimination
      3. Specifying knowledge
      4. Hierarchy specification
      5. Pattern presentation and visualization specification
      6. Data mining languages and standardization of data mining
    8. Conclusion
    9. Exercises
  15. 6. Data Mining Techniques
    1. Introduction
    2. Structure
    3. Objectives
    4. Data mining techniques
    5. Association rules
      1. Mathematical explanation of association rule and all of these components
      2. Types of association rules in data mining
      3. Algorithms for generating association rules in data mining
    6. Clustering techniques
      1. Types of clustering
      2. Clustering methods
        1. Partitioning method
        2. Hierarchical methods
        3. Density-based method
        4. Grid-based method
        5. Model-based methods
        6. Constraint-based method
    7. Decision tree
    8. Rough sets
    9. Support vector machines and fuzzy techniques
      1. Support vector machines
        1. Hyperplane
        2. Support vectors
        3. Margin
      2. Fuzzy techniques
    10. Conclusion
    11. Exercises
  16. 7. Mining Complex Data Objects
    1. Introduction
    2. Structure
    3. Objectives
    4. Mining complex data objects
      1. Time-series data mining
      2. Sequential pattern mining in symbolic sequences
      3. Data mining of biological sequences
      4. Graph pattern mining
      5. Statistical modeling of networks
      6. Mining spatial data
      7. Mining cyber-physical system data
      8. Mining multimedia data
      9. Mining web data
      10. Mining text data
      11. Mining spatiotemporal data
      12. Mining data streams
    5. Spatial databases
    6. Multimedia databases
      1. Multimedia databases and temporal data
        1. Difference between spatial and temporal data
      2. Different types of multimedia applications
      3. Challenges with multimedia database
      4. Architecture for multimedia data mining
        1. Applications of multimedia mining
    7. Time series and sequence data mining
      1. Components of time series
      2. Categories of time series movements
    8. Text databases and mining
    9. Word Wide Web
      1. Streaming data processing: An in-depth exploration
      2. Types of windows for aggregating streaming data
    10. Conclusion
    11. Exercises
  17. Index
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