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CUSTOMER_CODE
SMUDE
DIVISION_CODE
SMUDE
EVENT_CODE
JULY2016
ASSESSMENT_CODE MC0088_JULY2016
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
5227
QUESTION_TEXT
Distinguish the features between OLTP and OLAP
SCHEME OF
EVALUATION
Users and sytem orientatioin: An OLTP system is customer oriented and
is used for transaction and query used for transacrion and
query processing by clerks,clients and information technology
professionals. An OLAP system is market oriented and is usd for data
analysis by knowledge workers, including managers, executives and
analysts. (2 marks)
Data contents: An OLTP system managers current data that typically are
too detailed to be easily used for decision making. An OLTP system
managers large amounts of historiacl data, provides facilities for
summarization and aggregation and stores and managers information at
different levels of granularity. These features make the data easier to use
in informed decision making. (2 marks)
Database design:An OLTP system usually adopts an entity relationship
data model and an application oriented database design. An OLAP
system typically adopts either a star or snowflake model and subject –
oriented database design. (2 marks)
View:An OLTP system focuses mainly on the current data within an
enterprise or department without referring to historical data or data
in different organizations. In contrast OLAP system often spans multiple
versions of a database schema, due to the evolutionary process of an
organization. OLAP systems also deal with information that originates
from different organizations, integrating information from many data
stores. Because of their huge volume, OLAP data are stored on multiple
storage media. (2 marks)
Access patterns: The access patterns of an OLTP system consists manily
of short, atomic transactions. Such a system requires
concurrency concurrency control and recovery mechanisms. However
access to OLAP systems are mostly read only operations although many
could be complex queries. (2 marks)
(Total 10 marks)
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
5228
QUESTION_TEXT
Explain the concept of data transformation.
SCHEME OF
EVALUATION
In data transformation, the data are transformed or consolidated into
forms appropriate for mining. It involves the following: (2 marks)
Smoothing: which works to remove the noise form data? Such
techniques include binning, clustering and regression.
Aggregation, where summary of aggregation operations are applied to
the data. This step is typically used in constructing a data cube for
analysis of the data at multiple granularities. (2 marks)
Generalization of the data, where low level or primitive data are
replaced by higher level concepts through the use of concept
hierarchies. Ex like street can be generalizes to city or country (2
marks)
Normalization, where attribute data are scaled so as to fall within a
small specified range such as 1.0 to 1.0 or 0.0 to 1.0 (2 marks)
Attribute construction where new attributes are constructed and added
from the given set of attributes to help the mining process. (2 marks)
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
72561
QUESTION_TEXT
Discuss key features of a Data warehouse as per W. H. Inmon’s
statement.
SCHEME OF
EVALUATION
Key features are:
● Subject-oriented
● Integrated
● Time variant
● Non-volatile
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
72562
QUESTION_TEXT
What are the Four different views regarding the design of a data
warehouse? Explain.
SCHEME OF
EVALUATION
The views are:
● Top-down view
● Data source view
● Data warehouse view
● Business query view
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
117791
QUESTION_TEXT
Discuss Data Mining technologies.
a.
Decision trees
b.
Rule induction
c.
Genetic algorithms
d.
Nearest neighbor
e.
Artificial neural networks
SCHEME OF EVALUATION
(2 marks each with explanation)
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
117794
QUESTION_TEXT
List and explain the web content mining challenges.

Data/Information extraction

Web information integration and schema matching

Opinion extraction from online sources

Knowledge synthesis

Segmenting web pages and detecting noise
SCHEME OF EVALUATION
5×2=10 marks