AKTU Data Warehousing and Data Mining · Important Questions 2026Source: ryzenstudy.com PYQs

BCS058 Data Warehousing and Data Mining Important Questions 2026 (AKTU)

Repeat analysis + most-asked questions, unit-wise

What we did: built from all 4 AKTU Data Warehousing and Data Mining papers (2021-22–2024-25): 102 questions grouped into 80 distinct questions, 16 of which were asked in more than one paper.

Last Updated: November 13, 2026

of marks were repeat questions: 38%; of marks were repeat questions
questions asked in 2+ papers: 16; questions asked in 2+ papers
papers analysed (2021-22–2024-25): 4; papers analysed (2021-22–2024-25)
units, mapped to current syllabus: 5; units, mapped to current syllabus

How much of the paper repeats?

Repeats are real: when we checked each newer BCS058 paper against the papers before it, on average 38% of its marks were questions already asked in an earlier paper.
2023-24 paper: 25% of marks were repeats (8 of 8 repeated questions were in our top list).
2024-25 paper: 51% of marks were repeats (6 of 11 repeated questions were in our top list).
Topic weightage is a guide, not a guarantee: our Tier 1 + Tier 2 topics covered 64% of the next paper's marks — picking the same number of topics at random would have covered about 52%.

Across 10 subjects and 23 papers we tested, 43% of marks repeated earlier questions on average.

1.Unit-wise most-asked questions

Ranked by how many papers asked them, then by how recently. Click a paper to open it.

Unit 1: Data Warehousing

18% of past marks

Question	Asked
Define Data warehouse 2 marks2021-22 Q1(a)2022-23 Q1(a)2023-24 Q1(a)2024-25 Q1(g)	Asked 4×
Discuss Snowflakes, and Fact Constellations Schemas for Multidimensional Data Models 10 marks2022-23 Q3(b)2024-25 Q2(c)	Asked 2×
How mapping a 2D table into multidimensional data model? Explain with suitable example 10 marks2022-23 Q3(a)2023-24 Q3(a)	Asked 2×
Discuss the Fact Constellation. 2 marks2022-23 Q1(b)2023-24 Q1(b)	Asked 2×

Unit 2: Data Warehouse Process and Technology

8% of past marks

Question	Asked
Explain Distributed DBMS implementation. 2 marks2022-23 Q1(c)2023-24 Q1(c)	Asked 2×
Define Warehousing Software. 2 marks2022-23 Q1(d)2023-24 Q1(d)	Asked 2×
Explain Warehouse Schema Design 10 marks2022-23 Q2(b)	Asked 1×
Describe in detail about the Hardware and Operating Systems for Data Warehousing 7 marks2021-22 Q2(b)	Asked 1×

Unit 3: Data Mining · Data Reduction

16% of past marks

Question	Asked
Explain the following: (i) Regression (ii) Normalization 10 marks2024-25 Q5(b)	Asked 1×
Explain Binning method for dealing with noisy data 2 marks2024-25 Q1(j)	Asked 1×
What does negative, positive and zero Value Correlation coefficient indicates? 2 marks2024-25 Q1(i)	Asked 1×
Discuss issues to consider during data integration 2 marks2024-25 Q1(b)	Asked 1×

Unit 4: Classification · Clustering

37% of past marks

Question	Asked
Explain the Following i. various measures to calculate the central tendency of Data ii. Standard deviation and Variance 10 marks2022-23 Q4(b)2023-24 Q1(g)2024-25 Q6(a)	Asked 3×
Explain DBSCAN Clustering algorithm 10 marks2023-24 Q6(b)2024-25 Q4(b)	Asked 2×
Explain Decision tree with a suitable example 10 marks2023-24 Q5(a)2024-25 Q6(b)	Asked 2×
Explain Top-down and Bottom-up approach for Hierarchical Clustering 10 marks2022-23 Q1(h)2024-25 Q3(b)	Asked 2×

Unit 5:

22% of past marks

Question	Asked
Differentiate ROLAP, MOLAP and HOLAP Server Architectures 10 marks2022-23 Q7(a)2023-24 Q2(e)2024-25 Q7(b)	Asked 3×
Compare OLTP and OLAP Systems 10 marks2023-24 Q7(a)2023-24 Q7(a)2024-25 Q2(b)	Asked 2×
Explain the following: (iii) Spatial Mining (iv) Temporal Mining 10 marks2021-22 Q5(b)2024-25 Q5(a)	Asked 2×
Explain web content mining and web structure mining 10 marks2022-23 Q1(i)2024-25 Q7(a)	Asked 2×

2.Topic weightage: which topics carry the marks

Blue = Tier 1 + Tier 2 (topics that carried ~80% of past marks). Grey = the rest. It shows where marks have gone, not a promise of what comes next.

Statistical and Distance-Based Algorithms · U4

13.8% (cum 14%)

OLAP Functions and Tools · U5

7.3% (cum 21%)

Hierarchical and Density Methods · U4

6.6% (cum 28%)

Web, Spatial and Temporal Mining · U5

5.8% (cum 34%)

Clustering: Similarity and Distance · U4

4.9% (cum 38%)

Grid, Model and Association Rules · U4

4.6% (cum 43%)

ROLAP, MOLAP and HOLAP · U5

3.9% (cum 47%)

Building a Data Warehouse · U1

3.6% (cum 51%)

Decision Tree-Based Algorithms · U4

3.4% (cum 54%)

Data Integration and Transformation · U3

3.4% (cum 57%)

Data Mining Overview · U3

3% (cum 60%)

Stars and Snowflakes · U1

2.8% (cum 63%)

Motivation and Functionalities · U3

2.7% (cum 66%)

Definition and Components · U1

2.7% (cum 69%)

Database vs Data Warehouse · U1

2.7% (cum 71%)

Tuning and Testing Warehouse · U5

2.4% (cum 74%)

Multi Dimensional Data Model · U1

2.4% (cum 76%)

Warehousing Software · U2

2.1% (cum 78%)

Classification Definition · U4

1.9% (cum 80%)

Discretization and Decision Tree · U3

1.9% (cum 82%)

Dimensionality and Data Compression · U3

1.7% (cum 84%)

Data Warehousing Overview · U1

1.6% (cum 85%)

Warehouse Schema Design · U2

1.5% (cum 87%)

Warehousing Application Types · U5

1.5% (cum 88%)

Hardware and Operating Systems · U2

1.4% (cum 90%)

Mapping to Multiprocessor Architecture · U1

1.2% (cum 91%)

Parallel Processors and Clusters · U2

1.2% (cum 92%)

Data Cube Aggregation · U3

1.2% (cum 93%)

Analytical Characterization · U4

1.1% (cum 94%)

Fact Constellations · U1

1% (cum 95%)

Distributed DBMS Implementations · U2

1% (cum 96%)

Data Processing · U3

0.9% (cum 97%)

Warehousing Strategy · U2

0.7% (cum 98%)

Data Cleaning and Missing Values · U3

0.7% (cum 99%)

Aggregation and Historical Information · U5

0.7% (cum 99%)

Noisy and Inconsistent Data · U3

0.5% (cum 100%)

Data Generalization · U4

0.5% (cum 100%)

Topic	Papers	Share
Statistical and Distance-Based Algorithms · Unit 4 · Tier 1	4/4	13.8%
OLAP Functions and Tools · Unit 5 · Tier 1	4/4	7.3%
Hierarchical and Density Methods · Unit 4 · Tier 1	4/4	6.6%
Web, Spatial and Temporal Mining · Unit 5 · Tier 1	4/4	5.8%
Clustering: Similarity and Distance · Unit 4 · Tier 1	3/4	4.9%
Grid, Model and Association Rules · Unit 4 · Tier 1	3/4	4.6%
ROLAP, MOLAP and HOLAP · Unit 5 · Tier 1	3/4	3.9%
Building a Data Warehouse · Unit 1 · Tier 1	2/4	3.6%
Decision Tree-Based Algorithms · Unit 4 · Tier 2	3/4	3.4%
Data Integration and Transformation · Unit 3 · Tier 2	2/4	3.4%
Data Mining Overview · Unit 3 · Tier 2	1/4	3%
Stars and Snowflakes · Unit 1 · Tier 2	2/4	2.8%
Motivation and Functionalities · Unit 3 · Tier 2	3/4	2.7%
Definition and Components · Unit 1 · Tier 2	2/4	2.7%
Database vs Data Warehouse · Unit 1 · Tier 2	2/4	2.7%
Tuning and Testing Warehouse · Unit 5 · Tier 2	2/4	2.4%
Multi Dimensional Data Model · Unit 1 · Tier 2	2/4	2.4%
Warehousing Software · Unit 2 · Tier 2	3/4	2.1%
Classification Definition · Unit 4 · Tier 2	2/4	1.9%
Discretization and Decision Tree · Unit 3 · Tier 2	2/4	1.9%
Dimensionality and Data Compression · Unit 3 · Tier 3	2/4	1.7%
Data Warehousing Overview · Unit 1 · Tier 3	3/4	1.6%
Warehouse Schema Design · Unit 2 · Tier 3	1/4	1.5%
Warehousing Application Types · Unit 5 · Tier 3	1/4	1.5%
Hardware and Operating Systems · Unit 2 · Tier 3	1/4	1.4%
Mapping to Multiprocessor Architecture · Unit 1 · Tier 3	1/4	1.2%
Parallel Processors and Clusters · Unit 2 · Tier 3	1/4	1.2%
Data Cube Aggregation · Unit 3 · Tier 3	1/4	1.2%
Analytical Characterization · Unit 4 · Tier 3	1/4	1.1%
Fact Constellations · Unit 1 · Tier 3	2/4	1%
Distributed DBMS Implementations · Unit 2 · Tier 3	2/4	1%
Data Processing · Unit 3 · Tier 3	1/4	0.9%
Warehousing Strategy · Unit 2 · Tier 3	1/4	0.7%
Data Cleaning and Missing Values · Unit 3 · Tier 3	1/4	0.7%
Aggregation and Historical Information · Unit 5 · Tier 3	1/4	0.7%
Noisy and Inconsistent Data · Unit 3 · Tier 3	1/4	0.5%
Data Generalization · Unit 4 · Tier 3	1/4	0.5%

3.Paper pattern (2024-25)

Section	Questions	Rule
A	8 × 2 marks	Attempt all
B	5 × 10 marks	Attempt any 3
C	10 × 10 marks	Attempt any 1

4.In the syllabus, never asked yet

Low priority, but read them once — a new paper can always start here.

Warehouse Database · U1
Data Cubes · U1
Warehouse Management and Support · U2
Warehouse Planning and Implementation · U2
Client/Server Computing Model · U2
Data Pre-processing Forms · U3
Attribute Relevance Analysis · U4
Mining Class Comparisons · U4
Query Facility · U5
OLAP Servers · U5
Data Mining Interface · U5
Security, Backup and Recovery · U5

5.Study order

1Statistical and Distance-Based Algorithms (Unit 4, Tier 1)
2OLAP Functions and Tools (Unit 5, Tier 1)
3Hierarchical and Density Methods (Unit 4, Tier 1)
4Web, Spatial and Temporal Mining (Unit 5, Tier 1)
5Clustering: Similarity and Distance (Unit 4, Tier 1)
6Grid, Model and Association Rules (Unit 4, Tier 1)
7ROLAP, MOLAP and HOLAP (Unit 5, Tier 1)
8Building a Data Warehouse (Unit 1, Tier 1)
9Decision Tree-Based Algorithms (Unit 4, Tier 2)
10Data Integration and Transformation (Unit 3, Tier 2)
11Data Mining Overview (Unit 3, Tier 2)
12Stars and Snowflakes (Unit 1, Tier 2)
13Motivation and Functionalities (Unit 3, Tier 2)
14Definition and Components (Unit 1, Tier 2)
15Database vs Data Warehouse (Unit 1, Tier 2)
16Tuning and Testing Warehouse (Unit 5, Tier 2)
17Multi Dimensional Data Model (Unit 1, Tier 2)
18Warehousing Software (Unit 2, Tier 2)
19Classification Definition (Unit 4, Tier 2)
20Discretization and Decision Tree (Unit 3, Tier 2)

FAQ

Which units of BCS058 carry the most marks?

Unit 4 (Classification · Clustering, 37% of past marks) and Unit 5 (, 22% of past marks).

Do AKTU Data Warehousing and Data Mining questions repeat?

Yes. On average 38% of the marks in each newer BCS058 paper came from questions already asked in an earlier paper, and 16 questions have appeared in more than one paper.

How is this list made?

Every question from the 4 papers (2021-22–2024-25) was mapped to the current AKTU syllabus and grouped with the same question from other years. Questions are ranked by how many papers they appeared in, then by how recently.

Study this subject

Note: this is based on past AKTU papers, not a guarantee. Questions are mapped to the current syllabus; topics that are no longer in the syllabus are left out.