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
11571
QUESTION_TEXT
What are the objectives of using data mining in business?
Explain.
SCHEME OF
EVALUATION
There are 8 objectives. Each carries 1.25 Marks
QUESTION_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
11572
QUESTION_TEXT
Explain various characteristics of data warehouse?
1.Subject oriented
2.Integrated
SCHEME OF EVALUATION 3.Non Volatile
4.Time variant
(2.5 marks each)(10 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_TYPE
DESCRIPTIVE_QUESTION
QUESTION_ID
72813
QUESTION_TEXT
Discuss the significance of BI in various fields.
SCHEME OF
EVALUATION
Significance of BI to know about Customers: (2 marks)
Having access to timely and accurate information is an important
resource for a company, which can expedite decision-making and
improve customers’ experience. BI enables companies to gather
information on the trends in the marketplace and come up with
innovative products or services in anticipation of customer’s changing
demands.
Significance of BI know about Competitors’ Market: (2 marks)
BI applications can also help manages to be better informed about
actions that a company’s competitors are taking. BI systems can also be
designed to provide managers with information on the state of
economic trends or marketplace factors, or to provide managers with in
depth knowledge about the internal operations of a business.
Significance of BI for avoiding guesswork: (2 marks)
BI can be used to help analysts and managers determine which
adjustments are most likely to responds to changing trends. BI systems
can help companies develop a more consistent data-based decision
making process for business decisions, which can produce better results
than making business decisions by guesswork.
Significance of BI for sharing of information: (2 marks)
BI can help companies share selected strategic information with
business partners. Some businesses use BI systems to share information
with their suppliers like inventory levels, performance metrics other
supply chain data.
Significance of BI for improving performance: (2 marks)
BI applications can enhance communication among departments,
coordinate activities, and enable companies to respond more quickly to
changes. When a BI system is well-designed and properly integrated
into a company’s processes and decision making processes, it may be
able to improve a company’s performance.
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
SCHEME OF
rudimentary conversions of single fields for two primary reasons – one to
EVALUATIO
standardize among the data extraction from disparate source systems, and
N
the other to make the fields usable and understandable to the
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
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.