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Data Warehousing and Mining [1]

Paper Code: 
25CBDA313
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

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

25CBDA

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          

ESSENTIAL READINGS: 

1. Paulraj  Ponnian,”Data Warehousing Fundamentals”, John  Wiley.ne  Kamber, ―Data  Mining Concepts and  Tech, ThirdEdition,  Elsevier,  2012.

 

REFERENCES: 

SUGGESTED READINGS:

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/ [2]

JOURNALS:

1.    https://vciba.springeropen.com/

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

 

Academic Year: 
2025-26 [3]

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Source URL: https://www.csit.iisuniv.ac.in/courses/subjects/data-warehousing-and-mining

Links:
[1] https://www.csit.iisuniv.ac.in/courses/subjects/data-warehousing-and-mining
[2] http://www.geeksforgeeks.org/
[3] https://www.csit.iisuniv.ac.in/academic-year/2025-26