AKTU Data Warehousing & Data Mining (BOE095) Question Paper 2025-26

Note: Attempt all Sections. If require any missing data; then choose suitably.

Section A

Q1. Attempt all questions in brief · 2 × 7 = 14 marks

1. Briefly explain the Star Schema .
2. Define Metadata in the context of a Data Warehouse.
3. C O - Explain the concept of Data Integration.
4. Briefly Explain the use of learning rate in Neural network .
5. What are Outliers in a dataset?
6. Explain support and confidence in Association rule mining .
7. List any four applications of web mining .

Section B

Q2. Attempt any three of the following · 7 × 3 = 21 marks

1. Explain the concept of Multidimensional Data Models and how they differ from Relational Models.
2. Define Data warehouse and Data Mart ? How does data mart is differ ent from Data Warehouse?
3. Discuss the various Data Preprocessing techniques, focusing on Data Cleaning, Integration, and Transformation.
4. Compare and contrast Classification and Prediction. Discuss the criteria for evaluating a classifier's accuracy.
5. Discuss the application of Data Mining and warehousing in health care and insurance sector.

Section C

Q3

Attempt any one part of the following · 7 × 1 = 7 marks

1. Describe the various OLAP Operations (Roll - up, Drill - down, Slice, Dice, and Pivot) with examples.
2. Explain the 3 - tier Architecture of a Data Warehouse with a neat diagram.

Q4

Attempt any one part of the following · 7 × 1 = 7 marks

1. Discuss the design and implementation of a Data Warehouse . Explain the "Top - down" vs. "Bottom - up" approach . " - "
2. Explain the ETL (Extract, Transform, and Load ) process and its significance in building a Data Warehouse. ( , )

Q5

Attempt any one part of the following · 7 × 1 = 7 marks

1. What do you mean by missing data values? Explain three Central Tendency Imputation Methods to fill the massing values with suitable examples .
2. What do you mean by normalization? Explain the Min - M ax, . Z - score and Decimal S caling normalization with suitable examples .

Q6

Attempt any one part of the following · 7 × 1 = 7 marks

1. Explain the K - Means Clustering algorithm. What are its strengths and weaknesses?
2. How can you identify the Symmetric, Positively .skewed and negatively skewed data from the 5 - number summary box plot? Explain in detail with all statistical description used in Box plot . 5 - ,

Q7

Attempt any one part of the following · 7 × 1 = 7 marks

1. Differentiate between ROLAP , HOLAP, and MOLAP . ROLAP, HOLAP MOLAP
2. What do you mean by Time - Stamped Data in t emporal Mining ? Explain the different applications of temporal mining.

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