ALCCS
NOTE:
· Question 1 is compulsory and
carries 28 marks. Answer any FOUR questions from the rest. Marks are indicated against each question.
· Parts of a question should be
answered at the same place.
Q.1 a. Differentiate between the operational data & Decision
Support System data.
b. Explain how the system development
life cycle for the data warehouse is exactly opposite to the classical SDLC.
c. Explain the four levels of data in the
architectural environment.
d. Is the data in data warehouse homogenous or
heterogeneous? Illustrate with an example.
e. What
is granularity? Why it is important in data warehouse?
f. Explain ‘Normalization’ in data warehouse.
List its advantages.
g.
List the changes to be made in corporate
data model when it is to be applied to the data warehouse. (7
4)
Q.2 a. Explain the term ‘Living Sample Database’
in detail.
b. What is “Partitioning of data”? Illustrate
with an example. Explain the ways to carry it out. Which one is better and why? (8+10)
Q.3 a. Explain how process model and data model can
apply to the architecture environment? Why Process model is not suitable for
data warehouse.
b. Explain in detail all the three data warehouse
data models. (8+10)
Q.4 a. Discuss star join with an example. Creating a star join for the
data warehouse is a
mistake. Comment.
b. Explain Multidimensional DBMS. Discuss the
ways of implementing it by providing
its strength and weaknesses. How is
Multidimensional DBMS different from warehouse? (8+10)
Q.5 a. Building the warehouse on multiple levels is
easiest scenario to manage, with fewest risks. Explain.
b. Discuss in detail “Design review”. (8+10)
Q.6 a. Discuss the technique of Event Mapping.
b. Explain how the data warehouse acts as a basis
for EIS. (8+10)
Q.7 Write
short notes on any THREE:
(i)
Cyclicity
of Data
(ii)
Drill
Down Analysis
(iii)
Importance
of metadata in data warehouse
(iv)
Local
data V/s
Global data (6+6+6)