Data Warehouse and Data Mining: Concepts, techniques and real life applications.

Unveiling insights, unleashing potential: Navigating the depths of data warehousing and mining for a data-driven tomorrow KEY FEATURES ● Explore concepts ranging from fundamentals to advanced techniques of data warehouses and data mining. ● Translate business questions into actionable strategies to make informed decisions. ● Gain practical implementation guidance for hands-on learning. DESCRIPTION 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. WHAT YOU WILL LEARN ● Designing and building efficient data warehouses. ● Handling diverse data types for comprehensive insights. ● Mastering various data mining techniques. ● Translating business questions into mining strategies. ● Techniques for pattern discovery and knowledge extraction. WHO THIS BOOK IS FOR From aspiring data analysts, data professionals, IT managers, to business intelligence practitioners, this book caters to a diverse audience. TABLE OF CONTENTS 1. Introduction to Data Warehousing 2. Data Warehouse Process and Architecture 3. Data Warehouse Implementation 4. Data Mining Definition and Task 5. Data Mining Query Languages 6. Data Mining Techniques 7. Mining Complex Data Objects Cover Title Page Copyright Page Dedication Page About the Author About the Reviewer Acknowledgement Preface Table of Contents 1. Introduction to Data Warehousing Introduction Structure Objectives Data warehousing History of data warehouse Decision support system development (1970s–1980s) Online analytical processing was first introduced in the 1980s Concepts for data warehousing in the 1980s and 1990s Ralph Kimball’s dimensional modelling first appeared in the 1990s ETL and data integration advancements between the 1990s and 2000s Columnar databases and in-memory computing adoption between 2000 and 2010 Big Data and Cloud data warehousing (2010s–present) Data warehouse works Sources of data Extract, Transform, and Load Data organization and storage Types of data warehouses Enterprise data warehouse Data mart Operational data store General stages of data warehouse Analysis of requirements Modelling data Extraction of data Data loading Transformation of data Data management and storage Management of metadata Analysis and querying Upkeep and evolution Need of data warehouse Uses and trends of data warehouse Data warehouse applications in various industries Banking Retail Healthcare Marketing Manufacturing Telecommunications Trends of data warehouse Database management system versus data warehouse Metadata Types of metadata Descriptive metadata Administrative metadata Structural metadata Technical metadata Provenance metadata Rights metadata Preservation metadata Role of metadata Metadata repository Benefits of metadata Challenges for metadata management Multidimensional data model Data cubes Data cube classification Operations in data cubes Advantages of data cubes Data cube disadvantages Schemas for multidimensional database Star schema Snowflake schema Star Schema Fact table Snowflake schema Fact table Fact constellation in data warehouse General structure of fact constellation Fact constellation schema architecture Conclusion Exercises 2. Data Warehouse Process and Architecture Introduction Structure Objectives Objectives of data warehouse architecture Single-tier architecture Two-tier architecture Three-tier architecture Properties of data warehouse architecture Types of data warehouse architecture Single-tier architecture Two-tier architecture Three-tier architecture Advantages of data warehouse architecture Disadvantages of data warehouse architecture Data warehouse database Online transaction processing and online analytical processing Online transaction processing Online analytical processing Advantages and disadvantages of Online transaction processing and online analytical processing Types of online analytical processing Examples of all types, advantages and disadvantages of different OLAP systems Servers Data warehouse manager Conclusion Exercises 3. Data Warehouse Implementation Introduction Structure Objectives Introduction of data warehouse implementation Data warehouse implementation guidelines Components of data warehouse implementation Benefits of implementing a data warehouse Computation of data cubes Subtracting two cubes for differences Modeling Online Analytical Processing data Online Analytical Processing queries manager Data warehouse back-end tools Complex aggregation at multiple granularities Tuning and testing of data warehouse Tuning and testing of data warehouse: Enhancing performance and reliability Conclusion Exercises 4. Data Mining Definition and Task Introduction Structure Objectives Introduction to data mining Defining business objectives/problem definition Importance Stakeholder meetings Problem definition Prioritization Scope definition Deliverables Benefits Data mining tasks Data mining versus data analysis Data mining functionality Knowledge Discovery in Databases versus data mining Data mining techniques Classification Clustering Association rule mining Regression analysis Correlation versus causation Anomaly detection Global outliers Contextual outliers Collective outliers Sequence pattern mining Text mining Data mining tools and applications Conclusion Exercises 5. Data Mining Query Languages Introduction Structure Objectives Introduction to data mining query languages Syntax of Data Mining Query Languages Structured Query Language GROUP BY ORDER BY WINDOW operations (using OVER) Creating a view as part of data preprocessing Various clauses to explore the attributes or dimensions Data Mining Query Language Multidimensional Expressions MDX query clauses MDX functions Datalog XQuery XPath and XQuery CSS selectors Web scraping libraries JSONPath SPARQL RESTful APIs Web Scraping Frameworks Graph Query Languages Standardization of Data Mining Languages Data specification DMQL for characterization DMQL for discrimination Specifying knowledge Hierarchy specification Pattern presentation and visualization specification Data mining languages and standardization of data mining Conclusion Exercises 6. Data Mining Techniques Introduction Structure Objectives Data mining techniques Association rules Mathematical explanation of association rule and all of these components Types of association rules in data mining Algorithms for generating association rules in data mining Clustering techniques Types of clustering Clustering methods Partitioning method Hierarchical methods Density-based method Grid-based method Model-based methods Constraint-based method Decision tree Rough sets Support vector machines and fuzzy techniques Support vector machines Hyperplane Support vectors Margin Fuzzy techniques Conclusion Exercises 7. Mining Complex Data Objects Introduction Structure Objectives Mining complex data objects Time-series data mining Sequential pattern mining in symbolic sequences Data mining of biological sequences Graph pattern mining Statistical modeling of networks Mining spatial data Mining cyber-physical system data Mining multimedia data Mining web data Mining text data Mining spatiotemporal data Mining data streams Spatial databases Multimedia databases Multimedia databases and temporal data Difference between spatial and temporal data Different types of multimedia applications Challenges with multimedia database Architecture for multimedia data mining Applications of multimedia mining Time series and sequence data mining Components of time series Categories of time series movements Text databases and mining Word Wide Web Streaming data processing: An in-depth exploration Types of windows for aggregating streaming data Conclusion Exercises Index