2025-Data Warehousing and MiniNg

Jan 2025 

• Computing

• Information System

• Introduction to Data Warehouse

• Learning Activity: UG-Project ( Scaling for large data size and computing resources) -

• Assignment Submission Link

Text and Reference Books 

• Gupta, G. K. (2014). Introduction to data mining with case studies. PHI Learning Pvt. Ltd.. 

• Ponniah, P. (2011). Data warehousing fundamentals for IT professionals. John Wiley & Sons. 

• Han, J., Kamber, M., & Pei, J. (2012). Data mining concepts and techniques third edition. University of Illinois at Urbana-Champaign Micheline Kamber Jian Pei Simon Fraser University. 

PPTs / Learning Resources ( Internal Test-2 and Internal Test-3)

• Computing, System, Information System 

• Introduction to Data Mining

• Data Understanding

• Data Preprocessing

• OLAP-OLTP

• Data Cube Technology

• Frequet Patterns (Basic)

• Frequent Patterns (Advanced)

• Classification (Basic)

• Classification (Advanced)

• Clustering Techniques (Basic)

• Clustering Techniques (Advanced)

• Outlier Analysis

• Future Trends in Data Mining

• Some Problems and Projects

1. Can you provide an example illustrating the subject-oriented nature of a data warehouse? Focus on how data in the data warehouse is organized by business subjects rather than by operational applications.

2. Could you offer an example to demonstrate the integrated nature of a data warehouse? Highlight how data inconsistencies are resolved and data from various operational applications is consolidated.

3. Can you exemplify the nonvolatile characteristic of a data warehouse? Provide an instance where data in the data warehouse remains static without frequent updates or deletions.

4. How can you illustrate data granularity using an example? Explain the concept of data granularity and demonstrate how multiple levels of detail are incorporated in a data warehouse, potentially featuring dual levels of granularity.

5. Can you enumerate and compare at least five distinctions between a data warehouse and a data mart?

6. Please elucidate various types of data warehouse architecture. Discuss 

• CENTRALIZED

• INDEPENDENT

• DATAMARTS

• FEDERATED

• HUB-AND-SPOKE

• DATA-MART

• BUS architectures, 

provide examples for each.

7. How would you depict the architecture of a data warehouse using its building blocks or components? Discuss the essential components that constitute the architecture of a data warehouse and their interrelations.

8. As a data mining consultant for an internet search engine company, how can data mining techniques such as clustering, classification, association rule mining, and anomaly detection benefit the company? Provide specific examples of how each technique can be applied to improve the company's operations.

9. Compare and contrast OLAP with OLTP, highlighting their respective characteristics, functionalities, and use cases.

10. Could you elaborate on common issues encountered during the Extract, Transform, Load (ETL) process? Provide examples illustrating challenges related to data extraction, transformation, and loading in the context of building a data warehouse.

Solve