T.Y.M.C.A. (Part (Under Faculty of Engg) Examination, 2017
DATA WAREHOUSING AND DATA MINING
Day and Date Saturday, 6-5-2017 Total Marks 100
Time 10.30 a.m. to 1.30 p.m.
Instructions Figures to the right indicate marks.
Q. 3 A and Q. 5 A are compulsory.
Write a example if necessary.
1. Multiple Choice Questions 20
What is ETL stand for
Execute Transmit and Load Extract Transform and Load
Execute Transform and Load All the above
The important aspect of the data warehouse environment is that data found
within the data warehouse is
Subject-oriented Time-variant
Integrated All of the above
The data is stored, retrieved and updated in
OLAP OLTP
SMTP FTP
describes the data contained in the Data warehouse.
Relational data Operational data
Metadata Informational data
is the specialized data warehouse database.
Oracle DBZ
Informix Redbrick
Record cannot be updated in
OLTP Files
RDBMS Data Warehouse
An operational system is
Used to run the business in real time and is based on historical data
Used to run the business in real time and is based on current data
Used to support decision making and is based on current data
Used to support decision making and is based on historical data
The partition of overall data warehouse is
Database Data cube
Data mart Operational data
The main organizational justification for implementing a data warehouse is to
provide
Cheaper ways of handling transportation
Decision support
Storing large volume of data
Access to data
10) is an important functional component of metadata.
Digital directory Repository
Information directory Data dictionary
11) data are noisy and have many missing attribute values.
Preprocessed Cleaned
Real-world Transform
12) clustering technique start with as many clusters as there are
records, with each cluster having only one record.
Agglomerative Divisive
Partition Numeric
13) clustering techniques starts with all records in one cluster
and then try to split that cluster into small pieces.
Agglomerative Divisive
Partition Numeric
14) In algorithm each cluster is represented by the centre of gravity
of the cluster.
K-medoid k-means
STIRR ROCK
15) Pick out a k-medoid algorithm.
DBSCAN BIRCH
PAM CURE
16) In web mining, is used to know which URLs tend to be requested
together.
Clustering Associations
Sequential analysis Classification
17) The partition algorithm uses scans of the databases to discover
all frequent sets.
Two Four
Six Eight
18) The basic idea of the apriori algorithm is to generate item sets of
a particular size and scans the database.
Candidate Primary
Secondary Super key
19) is the most well known association rule algorithm and is used
in most commercial products.
Apriori algorithm Partition algorithm
Distributed algorithm Pincer-search algorithm
20) Rule based classification algorithms generate rule to perform
the classification.
if-then While
Do while Switch
SECTION I
2. Write short note on (any 20
Data Explosion Problem.
Data Warehousing Components.
Browser tools.
Cognos Impromptu.
Data Mart.
3. What is Data warehouse How to map data warehouse in multiprocessor
architecture 10
Which are the different operations perform on OLAP explain with example 10
OR
Differentiate ROLAP, MOLAP and HOLAP. 10
SECTION II
4. Write a short note on (any 20
Applications of Web Mining.
Agglomerative hierarchical clustering.
Cluster analysis.
BIRCH algorithm.
Data Mining.
5. What is KDD process Explain the steps of KDD process 10
Explain K-Means clustering algorithm with example. 10
OR
Which is the different Trends Affecting in Data Mining 10