Data Warehousing and Mining (Theory)

Paper Code: 
24CBDA313
Credits: 
03
Periods/week: 
03
Max. Marks: 
100.00
Objective: 

This Course enables the students to

  1.  Understand the basic data warehouse and data mining concepts.
  2.  Understand recent trends in data warehousing.
  3.  Understand architectural components of data warehouse.
  4.  Study various data mining techniques.

 

Course Outcomes: 

Course

Learning outcome

(at course level)

Learning and teaching strategies

Assessment Strategies

Course Code

Course

Title

24CBDA 313

Data Warehousing and Mining

(Theory)

 

CO139. Analyse the significance of dataware house and data mining in information management.

CO140. Develop a project plan for implementing a Data Warehouse to meet organizations’ requirements.

CO141. Explain the concepts and architecture of data warehouse.

CO142. Design a Data Warehouse by assessing the different constraints.

CO143. Categorise Data Mining techniques and investigate the applications of data mining in different domains.

CO144. Contribute effectively in course-specific interaction.

Approach in teaching: Interactive Lectures, Discussion, Reading assignments, Demonstration.

 

 

 

Learning activities for the students: Self learning assignments, Effective questions, Seminar presentation.

  • Assignment
  • Classroom activity
  • Multiple choice questions
  • Semester End Examination
 

 

9.00
Unit I: 
Data Warehousing:

Need for strategic information, Decision support system, Operational versus Decision-Support Systems, Data Warehousing-the only solution, definitions of Data warehousing and data mining, features of Data warehouse, Data Marts, Metadata.

9.00
Unit II: 
Trends in Data Warehousing:

significant trends and growth.

Planning Data warehouse, project team, project management considerations, information packages & requirements gathering methods and Requirements definition: Scope and Content.

 

9.00
Unit III: 
Architectural components:

Objectives, Data Warehouse Architecture, Distinguishing Characteristics, Architectural Framework. Operational & Physical Infrastructure.

9.00
Unit IV: 
Implementation of Data warehouse:

ETL (Extract, Transform and Load in Data warehouse) Physical design: steps, considerations, physical storage, indexing, Data lake vs. Data warehouse

 

9.00
Unit V: 
Data mining:

Basics of data mining, related concepts, Data mining techniques, Data Mining Applications.

ESSENTIAL READINGS: 

SUGGESTED TEXT BOOKS

  1. Paulraj Ponnian,”Data Warehousing Fundamentals”, John Wiley.
  2. Jiawei Han and Micheline Kamber, ―Data Mining Concepts and Techniques‖, Third Edition, Elsevier, 2012.

 

REFERENCES: 

SUGGESTED REFERENCE BOOKS

  1. Jiawei Hen and Micheline Kamber, “ Data Mining Concepts and Techniques”
  2. Sima Yazdani, Shirley S. Wong, “Data warehousing with oracle”
  3. Han Kamber, Morgan Kaufmann, “Data Mining Concepts and Techniques”
  4. “Introduction to Business Intelligence and Data Warehousing”, PHI
  5. Ralph Kimball, “The Data Warehouse Lifecycle tool kit”, John Wiley.

 

e-RESOURCES:

  1. https://nptel.ac.in/courses/106106182
  2. https://www.geeksforgeeks.org/

 

JOURNALS:

  1. https://vciba.springeropen.com/
  2. https://appliednetsci.springeropen.com/

 

Academic Year: 
2024-25
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