Download CUSTOMER_CODE SMUDE DIVISION_CODE SMUDE

CUSTOMER_CODE
SMUDE
DIVISION_CODE
SMUDE
EVENT_CODE
APR2016
ASSESSMENT_CODE MCA5043_APR2016
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
11570
QUESTION_TEXT
List and explain the web content mining problems.
SCHEME OF EVALUATION There are 5 problems. Each explanation carries 2 Marks
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
72812
QUESTION_TEXT
What is Data Loading in Data Warehouse? Explain different types of
Data Loading.
SCHEME OF
EVALUATION
Data Loading implies physical movement of the data from the
computer(s) storing the source database(s) to that which will store the
data warehouse database, assuming it is different. (1 mark)
Data Loading Types:
Initial Load: (3 marks)
Populating all the Data Warehouse tables for the very first time.
Creation of indexes on initial loads or full refreshes requires special
consideration. Index creation on mass loads can be too timeconsuming. So drop the indexes prior to the loads to make the loads go
quicker. You may rebuild or regenerate the indexes when the loads are
complete.
Incremental Load: (3 marks)
Applying ongoing changes as necessary in a periodic manner. These are
the application of ongoing changes from the source systems. Changes
to the source systems are always tied to specific times, irrespective of
whether or not they are based on explicit time stamps in the source
systems.
Full Refresh: (3 marks)
Completely erasing the contents of one or more tables and reloading
with fresh data. This type of application of data involves periodically
rewriting the entire Data Warehouse. Sometimes partial refreshes also
requires rewriting only specific tables. Partial refreshes are rare because
every dimension table is intricately tied to the fact table. As far as the
data application modes are concerned, full refresh is similar to the
initial load. However in the case of full refreshes, data exists in the
target tables before incoming data is applied.
QUESTION_T
DESCRIPTIVE_QUESTION
YPE
QUESTION_ID 72814
QUESTION_T
Define Data Mining. Differentiate between Data Mining and DBMS.
EXT
Data Mining: It is the search for the relationships and global patterns that
exist in large databases but are hidden among vast amounts of data, such as
relationship between patient data and their medical diagnosis. It is the
process of discovering meaningful, new correlation patterns and trends by
sifting through large amounts of stored in repositories, using pattern
recognition techniques. (2 marks)
DBMS VS Data Mining
SCHEME OF
EVALUATION
(8 marks)
QUESTION_T
DESCRIPTIVE_QUESTION
YPE
QUESTION_I
126111
D
QUESTION_T
Explain the basic tasks involved in Data transformation.
EXT
Selection : This takes place at the beginning of the whole process of data
transformation. You select either the whole records or parts of several
records from the source systems. The task of selection usually forms part
of the extraction function itself
–2 Marks
Splitting/Joining : This task includes the types of data manipulation you need
to perform on the selected parts of source records. Sometimes you will be
splitting the selected parts even further during data transformation. Joining
of parts selected from many source systems is more widespread in the
Data Warehouse
environment
2
Marks
Conversion : This is an all–inclusive task. It includes a large variety of
rudimentary conversions of single fields for two primary reasons – one to
SCHEME OF
standardize among the data extraction from disparate source systems, and
EVALUATIO
the other to make the fields usable and understandable to the
N
users
2 Marks
Summarization : Sometimes you may find that it is not feasible to keep data at
the lowest level of detail in your Data Warehouse. It may be that none of
your users ever need data at the lowest granularity for analysis or
querying
2 Marks
Enrichment : This task is the rearrangement and simplification of individual
fields to make them more useful for the Data Warehouse environment.
You may use one or more fields from the same input record to create a
better view of the data for the Data Warehouse. This principle is extended
when one or more fields originate from multiple records, resulting in a
single field for the Data
Warehouse
2 Marks
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
126114
QUESTION_TEXT
Discuss the following data warehouse schema
a. Star schema
b.
Snowflake schema
a.
Star schema (5 marks)
b.
Snowflake schema (5 marks)
SCHEME OF EVALUATION
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
126115
Explain the categories of web mining.
QUESTION_TEXT
SCHEME OF
EVALUATION
Web mining can be broadly divided into three catagories.
a)Web content mining.
b)Web structure mining.
c)Web usage mining.
a)Web content mining : web content mining targets the knowledge discovery in which
the main objects are the traditional collections of multimedia document such as images
, video and audio which are embedded in or linked to the web pages. Web content
mining could be differentiated from two points of view: Agent based approach or
database approach. The first approach aims on improving the information finding and
filtering. The second approach aims on modeling the data on web into more structured
form in order to apply standard database querying mechanism and datamining
application analyze it. Web content mining problems and challenges are
data/information extraction , web information integration, opinion extraction from
online sources,knowledge synthesis, segmenting web pages and detecting noise .
b) Web structure mining: this focuses on analysis of the link structure of the web and
one of its purpose is to identify more preferable documents. The different objets are
linked in some way. The appropriate handling f the links could lead to potential
correlations and then improve the predictive accuracy of the learned models. The goal
of the wb structure mining is to generate structural summary about the web site and
web page. Based on the topology web structure mining will categorize the web pages
and generate the information such as the similarity and relationship between different
web sites. Web structure mining can also have another direction discovering the
structure of web document itself. This type of structure mining can be used to reveal the
structure of web pages.
c) Web usage mining: this focuses on the techniques that could predict the behavior of
users while they are interacting with the WWW. Web usage mining discover user
navigation patterns from web data tries to discover the use full information from the
secondary data derived from the interactions of the users while surfing on the web. Web
usage mining collects the data from web log records to discover user access patterns of
web pages .The insight knowledge could be utilized in personalization, system
improvement, site modification, business intelligence and usage characterization. In
general there are mainly 4 kinds of data mining techniques applied to the web mining
domain to discover the user navigation pattern: Association rue mining, sequential
pattern mining, clustering, classification.