B.C.A. (Semester Examination, 2017
INTRODUCTION TO DATA MINING AND WAREHOUSING
Day Date: Wednesday, 19-04-2017 Max. Marks: 80
Time: 10.30 AM to 01.30 PM
N.B. Q.1 and Q.7 are compulsory.
Attempt any two questions from Q. 3 and 4.
Attempt any one question from Q. 5 and 6.
Figures to the right indicate full marks.
Q.1 Choose correct alternatives: 16
The goal of star schema design is to simplify
Logical data model Physical data model
Conceptual data model None of these
Change data capture is one of the challenging technical
issues in
Data Extraction Data Loading
Data Transformation Data Cleansing
The input to the data warehouse can come from OLTP or
transactional system but not from other third party database.
True False
Normalization effects performance.
True False
Collapsing tables can be done on the ______relationship.
One-to-One Many-to-Many
Both a and b None of these
Data warehouse is about taking/collecting data from different

Harmonized Identical
Homogeneous Heterogeneous
class of Decision Support Environment.
OLTP OLAP DBMS Network
D-normalization speeds up
Data retrieval Data duplication
Data extraction Data loading
The fact table is a way of visualizing as an
Rolled Up Un-rolled
Rolled Down None of these
10) Full and incremental Extraction are the types of ___Extraction.
Logical Physical Both a and b None of these
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SLR-U 30
11) Selectivity is low in
Data Warehouse DBMS
OLTP None of these
12) The input to the data warehouse can come from OLAP or
Analytical system.
True False
13) Data mining evolves as mechanism to cater the limitations of
to deal massive data sets with high
dimensionality, multiple heterogeneous data recourses etc.
OLTP OLAP DSS DWH
14) For a DWH project, the key requirements are
product experience.
Tools Industry
Software None of these
15) DSS queries do not involve a primary key.
True False
16) Multidimensional database typically use proprietary
to store pre-summarized cube structures.
File Application
Aggregate Database
Q.2 What are the strategies for data transformation? Explain. 06
Explain Data mining Task Primitives. 06
What are Parallel Processing Hardware Options and Software
implementations?
04
Q.3 Explain Market Basket Analysis with example. 06
What are the applications of data mining? Explain. 06
Explain the basic procedure of principal component analysis. 04
Q.4 Compare OLTP and OLAP. 06
Explain data mining problems for different types of data. 06
Note on Data Fusion. 04
Q.5 In real-world data, tuples with missing values for some attributes
are a common occurrence. Describe various methods for handling
this problem.
08
What are different types of data that need to be integrated in data
warehouse to support decision making?
08
Q.6 Explain Association Rule Mining in detail. 08
Explain three-tier data warehousing architecture. 08
Q.7 Write short notes on the following (Any four) 16
Snowflake schema
Need of data warehouse
Advanced Visualization Techniques
OLAP Tools
Database Data
Data reduction