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PROGRAM-MCA
SEMESTER-V
SUBJECT
CODE & NAME-mca5043– Data Warehousing and Data Mining
CREDIT-4
BK
ID-B1633
MAX.
MARKS-60
Q1.
Differentiate between OLTP and Data Warehouses.
Answer:
Differentiate Data Warehouse database
and OLTP Databases:
2.
Explain the Data Warehouse Kimball life cycle. 10
Answer.
Data Warehouse Kimball life cycle
The Kimball Lifecycle
is a
3.
Describe about Hyper Cube and Multicube 5+5
Answer.
Hyper Cube and Multicube
Multidimensional
databases can present their data to an application using two types of cubes: hypercubes and multicubes.
4.
List and explain the Strategies for data reduction. 5*2
Answer.
Strategies for data reduction
Data reduction:
Obtain a reduced representation of the data set that is much smaller in volume
but yet produces the same (or almost the
5.
Describe K-means method for clustering. List its advantages and drawbacks. 5+5
Answer.
K-means method for clustering
k-means clustering is
a method of vector quantization, originally from signal processing, that is
popular for cluster analysis in data mining. k-means clustering aims to
partition n observations into k clusters in which each observation belongs to
the cluster with the nearest mean, serving as a prototype of the cluster. This
results in a partitioning of the data space
6.
Describe about Multilevel Databases and Web Query Systems 5+5
Answer.
Multilevel Databases and Web Query Systems
Multilevel
Databases
A multilevel database
system (MDBMS) supports the application of a multilevel policy for regulating
access to the database objects. In this approach
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