Download CUSTOMER_CODE SMUDE DIVISION_CODE SMUDE

CUSTOMER_CODE
SMUDE
DIVISION_CODE
SMUDE
EVENT_CODE
APR2016
ASSESSMENT_CODE BC0041_APR2016
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
35430
QUESTION_TEXT
What are the services provided by a database system?
SCHEME OF EVALUATION
Each carries 1 mark
● Data Storage, Retrieval and Update
● A User Accessible Catalog.
● Transaction support
● concurrency control services
● Recovery Services
● Authorization Services
● Support for data Communication
● Integrity Services
● Services to Promote Data Independence.
● Utility Services
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
35433
QUESTION_TEXT
Briefly describe the steps of building a warehouse.
SCHEME OF EVALUATION
Data extraction (2 marks)
Data consistency (2 marks)
Data cleaning (2 marks)
Data Integration (2 marks)
Data loading (2 marks)
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
73652
QUESTION_TEXT
Explain 3 level architecture of a Database and also explain the concept
of data independence
SCHEME OF
EVALUATION
i. A commonly used view of data approach is the three-level
architecture suggested by ANSI/SPARC. The three levels of the
architecture are three views of data :
-External : Individual user view
- Conceptual – community user view
- Internal – physical or storage view.
(2 marks)
ii. External view: This is the view that the individual user of the
database has. This view is often a restricted view of the database and
the same database may provide a number of different views for
different classes of users. In general, the end users and even the
application programmers are only interested in subset of the database.
For example, a department head may only be interested in the
departmental finances and student enrolments but not library
information. The librarian would not be expected to have any interest in
the information about academic staff.
(2 marks)
iii. Conceptual view: it is the information model of the enterprise and
contains view of the whole enterprise without any concern for the
physical implementation. The conceptual view is the overall community
view of the database and it includes all the information that is going to
represented in the database. The conceptual view is defined by the
conceptual schema which includes definitions of each of the various
types of data.
(2 marks)
iv. Internal view: this view is about the actual physical storage of data.
It tells us what data is stored in database and how. At least the
following aspects are considered at this level:
a. Storage allocation e.g.: B-trees, hashing etc.,
b. Access paths e.g.: specification of primary and secondary keys,
indexes and pointers and sequencing
c. Miscellaneous e.g.: data compression and encryption techniques,
optimization of the internal structures. (2 marks)
v. Data Independence: the separation of the conceptual view from
the internal view enables us to provide a logical description of the
database without need to specify physical structures. This is often called
physical data independence
Separating the external views from the conceptual view enables us to
change the conceptual view without affecting the external views. This
separation is sometimes called logical data independence.
(2 marks)
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
73654
QUESTION_TEXT
Explain the SELECT statements with ‘IN’, ‘BETWEEN’, ‘LIKE’, ‘UNION’ and
‘GROUP BY’ clauses (give examples)
SCHEME OF
EVALUATION
i. IN: is easier method of using compound conditions. For example, if
you want to list all Employees belonging to city ‘Mangalore’,
‘Bangalore’, ‘Manipal’:
SELECT EMPLOYEENAME FROM EMPLOYEE WHERE
CITY IN(‘Mangalore’, ‘Bangalore’, ‘Manipal’); (2 marks)
ii. BETWEEN: this is an easier method of extracting records between
two values of a column. For example, if you want to list those
employees whose salary is greater than or equal to 30,000, but less
than 50,000, we can use :
SELECT EMPLOYEENAME FROM EMPLOYEE WHERE
SALARY BETWEEN 30000 AND 50000; (2 marks)
iii. LIKE: this can be used in such cases, where we want to extract
names staring or ending with specific letters or name has a specific
string. For example, if you want to display all the names staring with
letter ‘S’, we can use :
SELECT EMPLOYEENAME FORM EMPLOYEE WHERE
EMPLOYEENAME LIKE ‘S%’;
(2 marks)
iv. UNION: there are occasions where you might want to see the
results of multiple queries together, combining their output: use
UNION. To merge the output of the following two queries, displaying
the ID’s of all Buyers, plus all those who have an Order placed :
SELECT BUYERID FROM ANTIQUES
UNION
SELECT OWNERID FROM ORDERS; (2 marks)
GROUP BY: one special use of GROUP BY is to associate an aggregate
function with group of rows. First, assume that the Antiques table has
the Price column, and each row has a value for that column. We want to
see the price of the most expensive item bought by each owner. We
have to tell SQL to group each owner’s purchases, and tell us the
maximum purchase price:
SELECT BUYERID, MAX(PRICE) FROM ANIQUES
GROUP BY BUYERID; (2 marks)
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
73655
QUESTION_TEXT
Describe briefly the concept of genetic algorithm and neural networks.
SCHEME OF
EVALUATION
Genetic Algorithms
Genetic algorithms (GAs) are a class of randomized search procedures
capable of adaptive and robust search over a wide range of search space
topologies. Modeled after the adaptive emergence of biological species
from evolutionary mechanisms, and introduced by Holland, GAs have
been successfully applied in such diverse fields such as image analysis,
scheduling, and engineering design.
Genetic algorithms extend the idea from human genetics of the fourletter alphabet (based on the A, C, T, G nucleotides) of the human DNA
code. The construction of a genetic algorithm involves devising an
alphabet that encodes the solutions to the decision problem in terms of
strings of that alphabet. Strings are equivalent to individuals. A fitness
function defines which solutions can survive and which cannot. The
ways in which solutions can be combined are patterned after the crossover operation of cutting and combining strings from a father and a
mother. An initial population of well-varied population is provided, and
a game of evolution is played in which mutations occur among strings.
They combine to produce a new generation of individuals; the fittest
individuals survive and mutate until a family of successful solutions
develops.
The solutions produced by genetic algorithms (GAs) are distinguished
from most other search techniques by the following characteristics:
*
A GA search uses a set of solutions during each generation rather
than a single solution.
*
The search in the string-space represents a much larger parallel
search in the space of encoded solutions.
*
The memory of the search done is represented solely by the set of
solutions available for a generation.
*
A genetic algorithm is a randomized algorithm since search
mechanisms use probabilistic operators.
*
While progressing from one generation to the next, a GA finds
near-optimal balance between knowledge acquisition and exploitation by
manipulating encoded solutions.
Genetic algorithms are used for problem solving and clustering
problems. Their ability to solve problems in parallel provides a powerful
tool for data mining. The drawbacks of GAs include the large
overproduction of individual solutions, the random character of the
searching process, and the high demand on computer processing. In
general, substantial computing power is required to achieve anything of
significance with genetic algorithms.
Neural Networks
Neural network is a technique derived from artificial intelligence
research that uses generalized regression and provides an iterative
method to carry it out. Neural networks use the curve-fitting approach to
infer a function from a set of samples. This technique provides a
"learning approach"; it is driven by a test sample that is used for the
initial inference and learning. With this kind of learning method,
responses to new inputs may be able to be interpolated from the known
samples. This interpolation however, depends on the world model
(internal representation of the problem domain) developed by the
learning method.
Neural networks can be broadly classified into two categories:
supervised and unsupervised networks. Adaptive methods that attempt to
reduce the output error are supervised learning methods, whereas those
that develop internal representations without sample outputs are called
unsupervised learning methods.
Neural networks self-adapt; that is, they learn from information on a
specific problem. They perform well on classification tasks and are
therefore useful in data mining. Yet, they are not without problems.
Although they learn, they do not provide a good representation of what
they have learned. Their outputs are highly quantitative and not easy to
understand. As another limitation, the internal representations developed
by neural networks are not unique. Also, in general, neural networks
have trouble modeling time series data. Despite these shortcomings, they
are popular and frequently used by several commercial vendors.
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
73656
QUESTION_TEXT
a. Define Relation. List and explain the properties of relations.
b. Define the following terms with reference to Relational
Database Management System.
i. Attribute
ii. Cardinality of a relation
iii. Degree of a relation
iv. Domain of an attribute
v. Primary key
SCHEME OF
EVALUATION
a. A relation is a table.
(1 Mark)
The properties of Relations include:
● No duplicate tuples
● Tuples are unordered
● Attributes are unordered
● Attribute values are Atomic
(1 x 4 = 4 Marks)
b. ● Attribute – A field or a column in a relation
● Cardinality of a relation – the number of tuples in a relation
● Degree of a relation – the number of attributes in a relation
● Domain of an attribute - Set of all values that can be taken by
the attribute
● Primary key of a relation – An attribute or a combination of
attributes that uniquely defines each tuple in a relation
(1 x 5 = 5 Marks)