CS614 - Data Warehousing
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Course Category: Computer Science/Information Technology
Course Level: Imdregraduate
Credit Hours: 3
Pre-requisites: CS101 CS403 CS610

Course Synopsis

The focal area of this course is to provide awareness of data warehouse basic components, importance of data warehouse in business, important steps and techniques to be considered during data warehouse development, and future trends and usage of data warehouse. This course will provide the knowledge and skills to design and implement a Data Warehouse. Presented in the regular lectures and 5 lab lectures, participants will experience all phases of a Data Warehouse implementation from Extract, Transform and Load (ETL) of the data to running queries on the final database. The course will have a look at OLAP, MOLAP, ROLAP and other systems and how they differ from one another. It will provide an understanding of how to use data warehousing techniques in implementing it on web.

Course Learning Outcomes

After completing this course you should be able to:
  • Design and implement a Data Warehouse
  • Define the basic concepts and importance of data warehouse
  • Identify the business areas where data warehouse is required
  • Use data warehouse for data mining projects

Course Contents

Introduction to Data Ware Housing, Normalization, De-Normalization, De-Normalization Techniques, Issues of De-Normalization, Online Analytical Processing (OLAP, Multidimensional OLAP (MOLAP, Relational OLAP (ROLAP, Dimensional Modeling (DM, Process of Dimensional Modeling, Issues of Dimensional Modeling,Extract Transform Load (ETL), Issues of ETL, ETL Detail: Data Extraction & Transformation, ETL Detail: Data Cleansing, Data Duplication Elimination & BSN Method, Introduction to Data Quality Management (DQM), DQM: Quantifying Data Quality, Total DQM Need for Speed: Parallelism, Need for Speed: Hardware Techniques, Conventional Indexing Techniques, Need for Speed: Special Indexing Techniques, Join Techniques, A Brief Introduction to Data mining (DM, What Can Data Mining Do Supervised Vs. Unsupervised Learning, DWH Lifecycle: Methodologies, DWH Implementation: Goal Driven Approach I, DWH Implementation: Goal Driven Approach II, DWH Life Cycle: Pitfalls, Mistakes, Tips., Course Project, Case Study: Agri-Data Warehouse Part-I, Case Study: Agri-Data Warehouse Part-II, Web Warehousing: An introduction, Web Warehousing: Issues, Lab Lecture-1: Data Transfer Service (DTS, Lab Lecture-2: Lab Data Set, Lab Lecture-3: Extracting Data Using Wizard, Lab Lecture-4: Data Profiling, Lab Lecture-5: Data Transformation & Standardization

Course Related Links

Data mining
ETL
Cache coherence
Clustering
Machine Learning
Software Agent
OLAP
Partition Elimination
Partition Elimination
Cache Coherence
Speed-up
Claude Shannon’s theory
KDD
OLAP
Multi-Attribute
Data Warehousing and Data Mining
Course Instructor

Dr. Ahsan Abdullah
PhD Computer Science
UNIVERSITY OF STIRLING,
Scotland, UK.
Books

Building the Data Warehouse by W. H. Inmon

Data Warehousing Fundamentals by Paulraj Ponniah