Data Warehousing and Data Mining (BSc CSIT, CSC410): the questions likely to come
85 analyzed questions from 8 past papers (2074-2082), grouped by syllabus unit — each with its probability, how often it's been asked, and where to study the answer.
What is a data warehouse? Explain the three-tier architecture of a data warehouse in detail with a neat diagram.
Data Warehouse
A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data that supports management's decision-making process (W. H. Inmon). It is a central repository that consolidates data from multiple heterogeneous operational sources for analysis and reporting (OLAP) rather than transaction processing.
Key characteristics:
- Subject-oriented: organized around major subjects (customer, product, sales) rather than applications.
- Integrated: data from different sources is cleaned and made consistent (naming, units, encoding).
- Time-variant: stores historical data with a time dimension (years of data).
- Non-volatile: data is loaded and read, but not updated/deleted in real time.
Three-Tier Architecture
A data warehouse is commonly built using a three-tier architecture:
+-------------------------------------------------------+
| Top Tier: Front-End Tools |
| (Query, Reporting, OLAP, Data Mining, Dashboards) |
+-------------------------------------------------------+
^
|
+-------------------------------------------------------+
| Middle Tier: OLAP Server |
| (ROLAP / MOLAP / HOLAP engine) |
+-------------------------------------------------------+
^
|
+-------------------------------------------------------+
| Bottom Tier: Data Warehouse Server (RDBMS) |
| Data Marts | Metadata | Monitoring & Administration |
+-------------------------------------------------------+
^ ^
| ETL | (Extract, Transform, Load)
+-----------------------+
| Operational DBs, Flat |
| files, External data |
+-----------------------+
1. Bottom Tier — Data Warehouse Server (Data layer):
- A back-end relational database that stores the warehouse data.
- Data is fed from operational databases, flat files and external sources through ETL (Extract, Transform, Load) / gateways (ODBC, JDBC, OLEDB).
- Also holds the metadata repository (definitions of data, source mappings) and tools for monitoring and administration.
2. Middle Tier — OLAP Server: Presents the multidimensional view of data to the user. Implemented as:
- ROLAP (Relational OLAP): maps multidimensional operations to standard relational tables (star/snowflake schema). Scales to large data.
- MOLAP (Multidimensional OLAP): stores data in special multidimensional array structures (data cubes) for fast retrieval.
- HOLAP (Hybrid OLAP): combines ROLAP storage for detailed data with MOLAP for aggregates.
3. Top Tier — Front-End / Client Tools:
- Tools used by end users: query and reporting tools, analysis tools, OLAP tools (slice, dice, drill-down), and data mining tools (prediction, classification, clustering).
- Produces reports, charts and dashboards for decision making.
Conclusion: The separation into three tiers gives modularity, scalability and the ability to optimize each layer (storage, processing, presentation) independently.
Data Warehouse and OLAP Technology
What is a data warehouse? Explain the three-tier architecture of a data warehouse in detail with a neat diagram.
Explain the data warehouse architecture and the ETL process in detail. Discuss the role of metadata in a data warehouse.
Define data warehouse and list its key features.
What are the advantages and disadvantages of MOLAP over ROLAP? Explain the multidimensional data model with the help of a data cube.
Define data warehousing. Explain the data warehouse design process and the components of a data warehouse with a diagram.
What is data cube? List the different variations of cube materializations.
What is data mart? Why do we need multidimensional data model?
List the components of data warehouse. Discuss about the trust propagation on social network.
Explain the architecture of a data warehouse. Differentiate between MOLAP, ROLAP, and HOLAP.
Differentiate between operational database and data warehouse.
Explain the OLAP operations with examples.
Explain the slice and dice OLAP operations.
Compare MOLAP, ROLAP, and HOLAP.
Explain the concept of data cube and aggregation.
Differentiate between star and snowflake schema.
What is a concept hierarchy? How is it generated?
Differentiate between ROLAP and MOLAP.
Explain the bottom-up and top-down approaches of building a data warehouse.
Explain the concept of data cube.
Explain the snowflake schema with an example.
What is a fact table and a dimension table?
Write short notes on metadata in data warehousing.
What is a data mart? How does it differ from a data warehouse?
Write short notes on the star schema.
What is a concept hierarchy? Explain with an example.
Sit a probable paper
A full mock exam built from the most likely questions, mirroring the real paper's structure. Every slot is a real past question.
Most Probable Paper
Mirrors the real structure · 60 marks · based on 8 past papers
- 1.[10 marks]
Explain classification using a decision tree. Construct a decision tree using ID3 for the given dataset and predict the class label of a new instance.
This question has recurred in 3 of 8 years; so far only in internal assessments, not the board; and its topic (Classification and Prediction) appears in 100% of years.
- 2.[10 marks]
What is a data warehouse? Explain the three-tier architecture of a data warehouse in detail with a neat diagram.
This question has recurred in 2 of 8 years; so far only in internal assessments, not the board; and its topic (Data Warehouse and OLAP Technology) appears in 100% of years.
- 3.[10 marks]
Explain the K-means algorithm. Cluster the given set of points into two clusters and show all iterations until convergence.
This question has recurred in 2 of 8 years; so far only in internal assessments, not the board; and its topic (Cluster Analysis) appears in 100% of years.
- 1.[5 marks]
Define data warehouse and list its key features.
This question has recurred in 2 of 8 years; so far only in internal assessments, not the board; and its topic (Data Warehouse and OLAP Technology) appears in 100% of years.
- 2.[5 marks]
Differentiate between operational database and data warehouse.
This question has recurred in 2 of 8 years; so far only in internal assessments, not the board; and its topic (Data Warehouse and OLAP Technology) appears in 100% of years.
- 3.[5 marks]
Explain the OLAP operations with examples.
This question has recurred in 2 of 8 years; so far only in internal assessments, not the board; and its topic (Data Warehouse and OLAP Technology) appears in 100% of years.
- 4.[5 marks]
What are the limitations of the Apriori algorithm?
This question has recurred in 2 of 8 years; so far only in internal assessments, not the board; and its topic (Mining Frequent Patterns, Associations and Correlations) appears in 100% of years.
- 5.[5 marks]
Explain the DBSCAN algorithm and the concepts of core, border, and noise points.
This question has recurred in 2 of 8 years; so far only in internal assessments, not the board; and its topic (Cluster Analysis) appears in 100% of years.
- 6.[5 marks]
Write short notes on the applications of data mining.
This question has recurred in 2 of 8 years; so far only in internal assessments, not the board; and its topic recurs in 5 of 8 years.
- 7.[5 marks]
Explain about web content, web usage and web structure mining.
This question has recurred in 2 of 8 years; so far only in internal assessments, not the board; and its topic recurs in 5 of 8 years.
- 8.[5 marks]
What is data cube? List the different variations of cube materializations.
Asked once (2082); so far only in internal assessments, not the board; and its topic (Data Warehouse and OLAP Technology) appears in 100% of years.
- 9.[5 marks]
What is data mart? Why do we need multidimensional data model?
Asked once (2082); so far only in internal assessments, not the board; and its topic (Data Warehouse and OLAP Technology) appears in 100% of years.
Behind the numbers
The raw evidence the predictions are computed from: marks per unit per year, syllabus weights, trends, and coverage.
Show the heatmap, topic table and coverage analysis
The receipt: marks per unit, per year
Each row is a syllabus unit, each column an exam year, each cell the marks that unit earned that year. Click any cell to see the actual questions behind it.
| # | Syllabus unit | Probability | Appeared | Avg marks | Syllabus weight | Exam vs syllabus | Trend | Questions |
|---|---|---|---|---|---|---|---|---|
| 1 | U3Data Warehouse and OLAP Technology | Very likely100% | 21.9 | 20%9 lecture hrs | Over-examinedexam 29% · syllabus 20% | Fading | 4 recurring25 total | |
| 2 | U4Mining Frequent Patterns, Associations and Correlations | Very likely100% | 13.1 | 18%8 lecture hrs | Balancedexam 18% · syllabus 18% | Steady | 1 recurring13 total | |
| 3 | U5Classification and Prediction | Very likely100% | 12.5 | 20%9 lecture hrs | Balancedexam 17% · syllabus 20% | Rising | 1 recurring13 total | |
| 4 | U6Cluster Analysis | Very likely100% | 11.9 | 11%5 lecture hrs | Balancedexam 16% · syllabus 11% | Steady | 2 recurring12 total | |
| 5 | U2Data Preprocessing | Very likely88% | 5.7 | 16%7 lecture hrs | Under-examinedexam 7% · syllabus 16% | Fading | none repeat8 total | |
| 6 | U1Introduction to Data Mining | Likely62% | 11 | 11%5 lecture hrs | Balancedexam 9% · syllabus 11% | Steady | 1 recurring9 total | |
| 7 | U7Mining Complex Types of Data and Applications | Likely62% | 6 | 4%2 lecture hrs | Balancedexam 5% · syllabus 4% | Steady | 1 recurring5 total |
Study smart, not hard
Drag the slider: studying the top 5 units in priority order covers ~86% of all observed marks.
- ~80% line
Lecture time vs exam marks
Where the exam pays more than the curriculum spends: ● lectures vs ● exam marks, as a share of the whole course. A long teal-leading bar = high-yield unit.