Cover -- Contents -- Preface -- Chapter 1: Introduction to Data Warehouse -- For Using the Data Warehouse -- For Building the Data Warehouse -- For Administering the Data Warehouse -- Business Metadata -- Technical Metadata -- Reporting and Managed Query Tools -- OLAP Tools -- Application Development Tools -- Data Mining Tools -- Data Visualization Tools -- First Generation Client/Server Model -- Second Generation Client/Server Model -- Multiple Choice Questions -- Answers -- Chapter 2: Building a Data Warehouse -- Business Considerations -- Design Considerations -- Technical Considerations -- Implementation Considerations -- Data Partitioning -- Data Clustering -- Parallel Processing -- Summary Levels -- Multiple Choice Questions -- Answers -- Chapter 3: Data Warehouse: Architecture -- Compute Cube Operator -- Partial Materialization -- Star Schema -- Snowflake Schema -- Fact Constellation Schema -- Multiple Choice Questions -- Answers -- Chapter 4: OLAP Technology -- MOLAP Architecture -- Data Design and Preparation -- Administration -- Performance -- OLAP Platforms -- OLAP Tools and Products -- Implementation Steps -- Indexing OLAP Data -- Processing of OLAP queries -- Multiple Choice Questions -- Answers -- Chapter 5: Introduction to Data Mining -- Class/Concept Description -- Mining Frequent Patterns, Associations and Correlations -- Classification and Prediction -- Cluster Analysis -- Outlier Analysis -- Evolution Analysis -- On the Basis of Prediction/Description -- On the Basis of Automatic/Manual Mining of Data -- Multiple Choice Questions -- Answers -- Chapter 6: Data Preprocessing -- Wavelet Transforms -- Principal Components Analysis (PCA) -- Regression -- Log-Linear Models -- Histograms -- Clustering -- Sampling -- Input -- Output -- Procedure -- Explanation -- Multiple Choice Questions -- Answers.

Chapter 7: Mining Association Rules -- Generalized Association Rules -- Multi-level Association Rules -- Multidimensional Association Rules -- Multiple Choice Questions -- Answers -- Chapter 8: Classification and Prediction -- Naive Bayesian -- Bayesian Belief Network -- Linear Regression -- Non-linear Regression -- Bagging -- Boosting -- Multiple Choice Questions -- Answers -- Chapter 9: Cluster Analysis -- Statistical Distribution-based Outlier Detection -- Distance-based Outlier Detection -- Density-based Local Outlier Detection -- Deviation-based Outlier Detection -- Multiple Choice Questions -- Answers -- Chapter 10: Advanced Techniques of Data Mining and Its Applications -- Financial Data Analysis -- Retail Industry -- Intrusion Detection -- Telecommunication Industry -- Multiple Choice Questions -- Answers -- Index.

Data Warehousing and Data Mining is presented in a question-and-answer format following the examination pattern and covers all key topics in the syllabus. The book is designed to make learning fast and effective and is precise, up-to-date and will help students excel in their examinations. The book is part of the Express Learning is a series of books designed as quick reference guides to important undergraduate courses. The organized and accessible format of these books allows students to learn important concepts in an easy-to-understand, question-and-answer format. These portable learning tools have been designed as one-stop references for students to understand and master the subjects by themselves.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2018. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.