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Examination Paper of Analytics with R
IIBM Institute of Business Management
IIBM Institute of Business Management Examination Paper
Analytics with R
MM.100
Section A: Objective Type & Short Questions (30 marks) Part one:
Multiple choice:
I. R is an programming language?
a) Closed source
b) GPL
c) Open source
d) Definite source
II. Who developed R?
a) Dennis Ritchie
b) John Chambers
c) Bjarne Stroustrup
III. R was named partly after the first names of R authors?
a) One
b) Two
c) Three
d) Four
IV. Packages are useful in collecting sets into
unit ?
a) Single
b) Multiple
V. R is an interpreted language so it can access through ?
a) Disk operating system
b) User interface operating system
c) Operating system
d) Command line interpreter
VI. Many quantitative analysts use R as their tool?
a) Leading tool
b) Programming tool
c) Both the above
This section consists of multiple choices and Short Notes type questions.
Answer all the questions.
Part one questions carry 1 mark each & Part two questions carry 5 marks each.
Examination Paper of Analytics with R
IIBM Institute of Business Management
VII. Predictive analysis is the branch of
analysis?
a) Advanced
b) Core
c) Both the above
VIII. is used to make predictions about unknown future events?
a) Descriptive analysis
b) Predicitive analysis
c) Both the above
XI. How many steps does the predictive analysis process contained?
a) 5
b) 6
c) 7
d) 8
X. How many types of R objects are present in R data type?
a) 4
b) 5
c) 6
d) 7
Part Two:
1. Explain the data import in R language. (5)
2. Explain how to communicate the outputs of data analysis using R language. (5)
3. What is R? (5)
4. What are the disadvantages of R Programming? (5)
Section B: Caselets (40 marks)
END OF SECTION A
This section consists of Caselets.
Answer all the questions.
Each Caselet carries 20marks.
Detailed information should form the part of your answer (Word limit 150 to 200 words).
Examination Paper of Analytics with R
IIBM Institute of Business Management
Caselet 1
In the internet era, prediction of customer behavior is a very valuable insight, since it helps a marketer to analyse its products’ value and send updates for selling its products. The online market depends on the history of its customers. Devising new strategies for markets and attracting customers to stores and trying to convert the incoming traffic into sales profitably are all vital to the financial health of retailers. Every retailer uses different strategies to increase store traffic and convert traffic into profits. They invest in prime real estate with desirable properties such as high foot-traffic of their targeted customer segments, customer populations, customer convenience and visibility. Once they determine a location, retailers drive store traffic in a variety of ways such as spending on advertising, offering loss-leader about the products with various discounts or conducting various promotional events in local markets, such as offering discounts at various levels or price deductions. Whenever costumers visit a store, retailers try to convert the customers profitably through several means. They ensure that the right product is available at the right place, at the right time and at the right price. They invest in store labour to ensure that costumers experience a good and competitively priced shopping service that would encourage them to purchase and return to the store in future as well. Such relationships are critical to retailers for the following reasons. First , they get to know the feedback of other stores and requirements of the customers. Financial data of the local customers can be calculated using time series data. Decision tree is very important for this type of problem as we can calculate the risk factors in the local market and understands the needs of the customers from their previous behavior. This is also known as learning the cognitive behavior of the customer. Let us take the example of iphone 7 that was launched recently. This brand also uses time-series analysis for understanding the behavior of their customers by means of data gathered from the earlier models like iphone 6 and iphone 6s. How the customer used these earlier models and what features they look for in competitive products provides important insights for product development. Decision tree is very useful for gathering information about new market values as these depend on the time series that comes from historical data. Using such data, we can analyse information from new products as well . We can analyse customer behavior in conjunction with their financial status and give them best discounts for their needs. If we analyse historical data, many products have failed badly because they were not able to understand the requirements of the market at that time. So, to play it safe, every company nowadays tries to understand the market and its needs as per the market values, thus, creating a decision tree from the time-series data is an essential task for them. Decision trees can help in reducing errors by means of information gain from the parent to the child. Tree baised induction in ID3 helps to generate a recommendation engine. Such an engine is a powerful tool to understand the needs of the market and help companies choose profitable markets. Decision trees have many features that are very helpful to retailers and companies for offering discounts by comparing the information gain and loss in the market. This is also done by understanding the behavior of the customer with regards to the new product and older products-iphone 6 and 6s being pertinent examples here because after launching iphone 7 and 7s the prices of iphone 6 and 6s were reduced by 20k in the Indian Market. Using decision tree and its properties in data mining, we can increase the profits for retailers and help companies convert customer traffic into profits. Data mining is presented in more detail in the next few chapters.
Questions
1. What is the features of decision trees? (10)
2. Define properties in data mining, by which we can increase the profits for retailers? (10)
Examination Paper of Analytics with R
IIBM Institute of Business Management
Caselet 2
The term, ‘recommender systems’ is widely used nowadays. Recommender systems are composed of very simple algorithms that aim to provide the most relevant and accurate information to users by sorting/filtering useful information from very large databases. Recommendation enginers discover data patterns from a given dataset by learning the consumers’ information and then producing outcomes that correlate to their needs and interests. In addition, recommendation engines narrow down the risk that could become a complex decision to just a few recommendations search. Big data supports recommendations at an unimaginable level these days. Recommendation engines work mainly in one of the following two ways, viz., either they rely on the properties of items with their bread crumps that a uses likes, which are analysed to determine what else the user may like, or they rely on the likes and dislikes of other users, which the recommendation engine uses to compute a similarity index between users and recommends items to them accordingly . It is also possible to combine both these methods to build a highly-advanced recommendation engine. The main goal is to achieve the recommended collective information of users for the items that might interest customers. These systems have access to user-centric information with profile attributes, such as demographics and product descriptions. They differ in the way they interact while analyzing the data to develop affinity values between users and items, which can be used to indentify well-matched pairs. A collaborative filtering system is used for matching and analyzing historical interaction alone, while content-based filtering is used for profiling-based attributes. Let us see how we can implement a recommendation engine with a collaborative memory-based recommendation engine. However, before that we must first understand the logic behind such a system. To this engine, each item and each user is nothing but an identifier or token element. Let us take the example of Netflix . Please note that we will not take any other attribute of a movie, such as cast, director, genre, etc., into consideration while generating recommendations for users. The similarity between two users is represented by using a decimal number between- 1.0and 1.0.. We will call this number, the similarity index. The possibility of a user liking a movie will be represented by using another decimal number between -1.0 and 1.0. Now that we have modeled the world around this system using simple terms, we can unleash a handful of elegant mathematical equations to define the relationship between these identifiers and numbers. In our recommendation algorithm, we will maintain a number of sets, which should represent a member of supersets with all users and identifiers. Each user will have two sets, viz., a set of movies the user likes and a set of movies the user dislikes, Each movie will also have two sets associated with it, viz., a set of users who liked the movie and a set of users who disliked the movie. During the performance where recommendations start to generate, a number of sets will be produced, mostly unions or intersections of the other sets. We will also have ordered lists of suggestions and similar users for each user. Similarly, like movies we can use the following recommendations. Personalised Product Information E-commerce Sites Such engines help in understanding customers’ preferences on the basis of their visit on the website. They show the customers the most relevant recommendation-type products as per their needs or there likes in real time. Recommendation improve as the cognitive learning improves with regression about each visitor each time. Website Personalisation
This is used by many organizations to calculate revenue on the basis of the number of hits from visitors. It increases their sales and targets new customers through segmentation into different cluster. It also allows getting in touch by message-centric methods. Real-time Notifications This is used by e-commerce for letting their customers know about the new top selling brands and available discounts. Such engines help brands build trust among their customers and create a sense of presence and urgency while showing real-time notification of shoppers’ activities on their website.
Questions
1. Which filtering system is used for matching and analysing historical interaction alone and define? (20)
Examination Paper of Analytics with R
IIBM Institute of Business Management
Section C: Applied Theory (30 marks)
1. Compare R & Python (15)
2. Explain the data import in R language (15)
S-2-010619
This section consists of Applied Theory Questions.
Answer all the questions.
Each question carries 15marks.
Detailed information should form the part of your answer (Word limit 200 to 250 words).
Examination Paper of Business Analytics Using SAS
IIBM Institute of Business Management
IIBM Institute of Business Management
Examination Paper Business Analytics Using SAS
MM.100
Section A: Objective Type & Short Questions (30 marks) Part one:
Multiple choice:
I. Which one of the following is the value of the variable
data work.one;a = 2;b = 3; c = a ** b;run;
(1)
a. 6
b. 8
c. 9
d. None of the above
II. Which one of the following statement can’t be part of “PROC FREQ” (1)
a. Output
b. Weight
c. Set
d. Tables
III. Which one is not a data type in SAS.
(1)
a. Numeric
b. Date
c. Character
d. Boolean
IV. Which of the following statements are used to read delimited raw data file and create an SAS data set (1)
a. Data, Infile, Input
b. Data, Set, Input
c. Data, Set, Infile
d. Data & Set
V. Which of these is casual model technique
(1)
a. Moving average
b. Exponential Smoothing
c. Regression
d. Trend Model
VI. When variance between predicted and actual values is not same, it is called –
(1)
a. Multicolinearity
b. Heteroskedasticity
c. Homoscadesticity
d. Autocorrelation
This section consists of multiple choices and Short Notes type questions.
Answer all the questions.
Part one questions carry 1 mark each & Part two questions carry 5 marks each.
Examination Paper of Business Analytics Using SAS
IIBM Institute of Business Management
VII. Is there a limitation of records in SAS? (1)
a. No
b. Ye
c. Can’t say
d. None of the above
IX. what is syntax used for assigning library in SAS (1)
a. LibSet
b. LibData
c. Data
d. Libname
VIII. Which one is not a window in SAS (1)
a. Editor
b. Log
c. drive
d. results
X. What is the syntax to print results in SAS (1)
a. Proc set
b. Proc print var
c. Proc Print
d. Proc Sql
Part Two
1 . Write data step to assign work dataset, say ABC with limitation of 10 observations only? (5)
2. Write the role of PDV in SAS (5)
3. Explain lib name function with read-only access (5)
4. What is the starting date in SAS, how does SAS reads any given date (5)
Section B: Caselets (40 marks)
Caselet 1
1. Answer the question based on data given below. Name of the dataset is class-
Roll Number
Class
Age
Gender
Subject
Sports
001
1
10
M
Maths
Cricket
END OF SECTION A
This section consists of Caselets.
Answer all the questions.
Each Caselet carries 20marks.
Detailed information should form the part of your answer (Word limit 150 to 200 words).
Examination Paper of Business Analytics Using SAS
IIBM Institute of Business Management
002
1
11
M
English
Tennis
003
2
9
F
Maths
Football
004
4
7
F
Science
Football
005
3
7
M
Maths
Cricket
006
5
10
F
Science
Tennis
007
3
7
F
Art
Cricket
Questions
1. Write code to see frequency distribution by Gender & Subject? (10)
2. Write code to filter student age less than 10 and like football and Maths? (10)
3. Write code to sort the data by age? (10)
4. Which sport has the maximum demand, write code to see proportionate (10)
Section C: Applied Theory (30 marks)
1. Explain how SAS can replace excel, talk about 3 key features of SAS? (15)
2. What is the meaning of debugging in SAS, how can it help in code writing?
(15)
S-2-010619
This section consists of Applied Theory Questions.
Answer all the questions.
Each question carries 15marks.
Detailed information should form the part of your answer (Word limit 200 to 250 words).
END OF SECTION C
Examination Paper of Business Analytics
IIBM Institute of Business Management
IIBM Institute of Business Management Examination Paper Business Analytics
MM.100
Section A: Objective Type & Short Questions (30 marks)
Part one:
Multiple choice:
I. in business intelligence allows huge data and reports to be read in a single graphical interface
a) Reports
b) OLAP
c) Dashboard
d) Warehouse
III. What makes BI 2.0 different?
a) Dynamic querying of real-time corporate data
b) Unstructured data is taken care of
c) Both a and b
d) Semi structured data is taken care of
V. What is the use of Temporal and sequential patterns analysis technique of BI?
a) Trend and deviation
b) sequential patterns
c) Identify relationships between attributes
d) Both a and b
II. Down ward communication flows from to .
a) Upper to lower
b) Lower to upper
c) Horizontal
d) Diagonal
IV. Which of the following are benefits or use of BI?
a) With BI, firms can identify their most profitable customers
b) Quickly detect warranty-reported problems to minimize the impact of
c) Data mining
d) Both a and b
VI. What is the purpose of Read Contingent access right?
a) allows members of the role to read from the object
b) allows members of the role to read a cell value only if the user can access all the cells from which the value is derived
c) provides Read access for any cells specified by this permission that are not derived from other cells
d) Both b and c
This section consists of multiple choices and Short Notes type questions.
Answer all the questions.
Part one questions carry 1 mark each & Part two questions carry 5 marks each.
Examination Paper of Business Analytics
IIBM Institute of Business Management
VII. Can an instance of Analysis Services contain multiple databases?
a) Yes
b) No
VIII. What is the use of Analysis Services Execute DDL task?
a) create, drop, or alter mining models
b) Assign permissions and roles
c) create, drop, or alter cubes and dimensions
d) Both a and c
XI. The Analysis Services Processing task can be used to process which of the following objects?
a) Cubes
b) dimensions
c) mining models
d) All of the above
X. The Analysis Services Processing task can process only analytic objects created by using the SQL Server tools
a) True
b) False
Part Two:
1. What Is Business Analysis? (5)
2. Who Uses The Output Produced By Business Analyst? (5)
3. What Is The Difference Between Data Model And An Entity Relationship Diagram? (5)
4. Mention The Components Of Uml? (5)
Section B: Caselets (40 marks)
Caselet 1
A major telecom provider became mired in too much data and not enough insight. Even a simple business question could take weeks or months to answer. The organization’s previous attempts to leverage big data had been costly and inefficient; they needed help getting started in a way that could be scaled to broader company-wide initiatives. The company’s leadership wanted to improve decision making by using big data and advanced analytics in an efficient, cost-effective way.
Analytics capabilities were spread across the company—each business had its own data analysts and technologies. This disconnected organizational structure meant that data and insights were not shared across business units and many efforts were duplicated. The analysts, however, did have one thing in common: their biggest challenge was
END OF SECTION A
This section consists of Caselets.
Answer all the questions.
Each Caselet carries 20marks.
Detailed information should form the part of your answer (Word limit 150 to 200 words).
Examination Paper of Business Analytics
IIBM Institute of Business Management
getting fast access to the big data and gaining business-relevant insights from it.
When analysts requested data from various internal sources and vendors, it arrived— sometimes weeks later— with inconsistent formats and missing data definitions. Time-consuming cleansing was needed before analysis could begin. A plethora of siloed tools and data environments made analysis tasks difficult to execute and share. With analytics-related expenses running into the millions—of which two-thirds was spent outside IT—leaders expected timely, reliable, and actionable insights for running the business. Something had to change.
Questions
1. What are the challenges faced by the telecom company; quote them in analytics terminology? (10)
2. What could be the possible solution you would give as consultant? (10)
Caselet 2
For one major retailer, the gap between visitors to the site and completed purchases was widening. A growing number of consumers were losing interest in following the digital maze required to locate and purchase the products they were seeking. As a result, the retailer was having an increasingly difficult time converting online customer visits into sales.
“We were constantly making changes to our website and offering special online promotions, but we found that some changes were having an opposite impact on sales than we were expecting,” the company’s
director of marketing explained. “It seemed like one step forward often meant taking two steps backward.”
Questions
1. What would you suggest this company? (10)
2. What are the type of Analytics Company can use and how? (10)
Section C: Applied Theory (30 marks)
1. Does The Business Analyst Interact With Clients Directly? If So State The Reason For The Same? (15)
2. What Can A Business Analyst Do Differently Than Project or Program Manager (design Architect) With Respect To Successfully Getting The Project Implementation Done? (15)
S-2-010619
This section consists of Applied Theory Questions.
Answer all the questions.
Each question carries 15marks.
Detailed information should form the part of your answer (Word limit 200 to 250 words).
END OF SECTION C
Examination Paper of Data Mining and Predictive Analytics
a. all of the above
IIBM Institute of Business Management
IIBM Institute of Business Management Examination Paper
Data Mining and Predictive Analytics
MM.100
Section A: Objective Type & Short Questions (30 marks) Part one:
Multiple choice:
I. Background knowledge referred to
a) Additional acquaintance used by a learning algorithm to facilitate the learning process
b) A neural network that makes use of a hidden layer
c) It is a form of automatic learning.
d) None of these
II. Querying of unstructured textual data is referred to as
a) Information access
b) Information updation
c) Information manipulation
d) Information retrieval
IV. A manual component to data mining, consists t processing data in form of
a) Discovered processes
b) Discovered algorithms
c) Discovered features
d) Discovered patterns
VI. Analysis tools precompute summaries of very large amounts of data, in order to give
a) Queries response
b) Data access
c) Authorization
d) Consistency
This section consists of multiple choices and Short Notes type questions.
Answer all the questions.
Part one questions carry 1 mark each & Part two questions carry 5 marks each.
III. A manual component to data mining, consists of preprocessing data to a form acceptable to
a) Variables
b) Algorithms
c) Rules
d) Processes
V. Patterns that can be discovered from a given database, can be of
a) One type only
b) No specific type
c) More than one type
d) Multiple type always
Examination Paper of Data Mining and Predictive Analytics
IIBM Institute of Business Management
VII. Data can be store , retrieve and updated in …
a) SMTOP
b) OLTP
c) FTP
d) OLAP
VIII. Which of the following is a good alternative to the star schema?
a) snow flake schema
b) star schema
c) star snow flake schema
d) fact constellation
XI. Background knowledge is…
a) It is a form of automatic learning.
b) A neural network that makes use of a hidden layer
c) The additional acquaintance used by a learning algorithm to facilitate the learning process
d) None of these
X. Which of the following is true for Classification?
a) A subdivision of a set
b) A measure of the accuracy
c) The task of assigning a classification
d) All of these
Part Two:
1. What are data mining techniques? (5)
2. What are the applications of data mining? (5)
3. Why is data mining important? (5)
4. Differentiate Between Data Mining And Data Warehousing? (5)
Section B: Caselets (40 marks)
Caselet 1
User-generated content is an indispensable part of today’s industry as every other company needs user data to sell and buy products and provide the best possible support to its users and clients. While user data is important, it needs to be processed to make it relevant for the company. Data mining is the most important tool to process such data and make it relevant and useful.
The decision tree algorithm with the apriori algorithm can be used to support the needs of the client.
To explain this problem, we will turn to smart technology –something that makes our lives easier. Whenever we install any application in our smartphone, we are asked for permission for the installation, but we do not pay too much attention to the information these application require to be installed. In the process, we unknowingly
END OF SECTION A
This section consists of Caselets.
Answer all the questions.
Each Caselet carries 20marks.
Detailed information should form the part of your answer (Word limit 150 to 200 words).
Examination Paper of Data Mining and Predictive Analytics
IIBM Institute of Business Management
disseminate varied information on maps, massages, contacts, etc. With the help of this information the application, besides collating customer data, also tries to support the users to make their life easier and at the same time makes them dependent on the application in the near future.
Once the user information is gathered, the data is analysed to get the required information so as to give the best information to the algorithm at different times. This type of analysis starts from data pre-processing steps, steps that have already been explained in Chapters 1 and 2. However, for this type of data pre-processing the information gain happens by designing the decision tree at different levels-the depth decision tree or 2-10 level decision tree as well.
Each data gives a valid point of information and these points are used in designing the clusters among different types of data but they are very centric in information as they provide the information of different users according to same contents. The frequency of the matching data is processed by means of decision tree under info gain and Apriori.
It is a common experience nowadays for different applications to recommend the same item for buying from different applications or portals, Users are also able to exercise their choices when it comes to reading the news by selecting the content that is more liked. Through their preferences, they provide the application information about the cognitive behavior of users. This allows prediction of the way a particular4 consumer behaves and recommendations are accordingly tweaked. Most studies of systems or online reviews so far have used only numeric information about sellers or products to examine their economic impact. The understanding that text matters has not been fully realized in electronic markets or in online communities. Insights derived from text mining of user-generated feedback can thus provide substantial benefits to businesses looking for competitive advantages.
Let us summarise some of the chief benefits utiling user-centric data:
It saves money: Since the users themselves provide relevant content for prediction and subsequent recommendations, users data need not be bought and efficiency in terms of time and costs in increased.
It provides variety: By using the user data, the customer can be apprised of various new features or upgrades to the existing product. Further, the user gets to know about the discounts being offered and can avail the support extended to the end user.
It offers a voice to the user: The company is in a position to offer individual customers different products as per individual preferences and a user can provide any specific information of the item he /she wants to use
.
These benefits of user-centric data should be firmly kept in mind to make such data more predictive and relevant in our fast-paced technological era.
Questions
1. What do you understand by user generated content? (10)
2. Do you really think user generated content is effective? (10)
Caselet 2
Big data is the collection and cross-referencing of large numbers and varieties of data sets that allows organizations to identify patterns and categories of cardholders through a multitude of attributes and variables. Every time customers use their cards, big data suggests the products that can be offered to the customers. These days many credit card users receive calls from different companies offering them new credit cards as per their needs and expenses on the existing cards. This information is gathered on the basis of available data provided by vendors.
There are quite a few option available to customers to choose from. Sometimes customers even switch their existing credit card companies. But competition may not always work in the best interests of consumers. It also
Examination Paper of Data Mining and Predictive Analytics
IIBM Institute of Business Management
involves bank’s profit. Competition may also be focused on particular features of credit cards that may not represent long-term value or sustainability.
Those paying interest on balances may be paying more than they realize or expect. Some consumers use up their credit limits quickly or repeatedly make minimum payments without considering how they will repay their credit card debt. A proportion of consumers may also be over-borrowing and taking on too much debt, and there are signs that some issuers may profit more from higher risk borrowers (by which we mean customers at greater risk of credit default).
With the launch of this credit card market study, we intend to build up a detailed picture of the market and assess the potential identified issues. We plan to focus on credit card services offered to retail consumers by credit card providers, including banks, mono-line issuers and their affinity and co-brand partners.
While mass marketing continues to dominate most retailers’ advertising budgets, one-to-one marketing is growing rapidly too. In this case study, you will learn how to improve performance by communicating directly with customers and delighting them with relevant offers. Personalised communication is becoming a norm. Shoppers now expect retailers to provide them with product information and promotional offers that match their needs and desires. They count on you to know their likes, dislikes and preferred communication method-mobile device, email or print media.
On the surface, generating customer-specific offers and communications seems like an unnerving task for many retailers, but like many business problems, when broken into manageable pieces, each process step or analytical procedure is attainable. First, let’s assume you have assembled promotions that you intend to extend as a group of offers (commonly called “offer bank”) to individual customers. Each offer should have a business goal or objective, such as:
Category void for cross or up-selling of a particular product or product group
Basket builder to increase the customer’s basket size
Trip builder to create an additional trip or visit to the store or an additional e-commerce session
Reward to offer an incentive to loyal customers
Questions
1. How Big data used in this case study- Define? (20)
Section C: Applied Theory (30 marks)
1. What Are Olap And Oltp? (15)
2. What Are Different Stages Of "data Mining"? (15)
S-2-010619
This section consists of Applied Theory Questions.
Answer all the questions.
Each question carries 15marks.
Detailed information should form the part of your answer (Word limit 200 to 250 words).
END OF SECTION C
Examination Paper of Information Technology
1
IIBM Institute of Business Management
IIBM Institute of Business Management
Examination Paper MM.100
Database Management Systems
Section A: Objective Type & Short Questions (30 Marks)
This section consists of Multiple Choice and Short answer type questions.
Answer all the questions.
Part one carries 2 marks each & Part Two carries 5 marks each.
Part One:
Multiple choices:
1. A collection of related sets of data items along with necessary data/ information associated with it.
a. Data
b. Information
c. Process
d. Database
2. ___________connects computers which are very remotely placed.
a. Local Area Network
b. Wide Area Network
c. Both (a) & (b)
d. None
3. A column in a table is called__________
a. Field
b. Record
c. Tuple
d. Link
4. DDL stands for ___________
a. Data Definition Language
b. Data Decision Language
c. Database Definition Language
d. None
5. SQL stands for ___________
a. Structured Query Language
b. Statement Query Language
c. Strict Query language
d. None
Examination Paper of Information Technology
2
IIBM Institute of Business Management
Part Two:
1. List the different types of DBMS.
2. Differentiate between ‘DBMS’ and ‘RDBMS’.
3. What do you mean by ‘Data Dictionary’?
4. Differentiate between discretionary access control and mandatory access control.
Section B: Caselets (40 marks)
This section consists of Caselets.
Answer all the questions.
Each Caselet carries 20 marks.
Detailed information should form the part of your answer (Word limit 200 to 250 words).
Caselet 1
Database management system is the complex software which is aimed at the management of the information stored in the database effectively. A high-quality management system helps organize, manipulate, transform, store, retrieve and create data professionally. It is important that the whole information kept in the database could be accessible, manageable, and easy for manipulation. A successful DBMS should possess a strict logical structure, which enables everyone to find the required data easily. The high-quality management system gives the opportunity for the user to change the required information without any harm to the whole application. Database management systems are extremely important today, because the humanity lives in the age of information and the whole information is kept in databases which require professional skilful management and flexibility.
Every organization, private and public, connected with business or not possesses the necessary information which is essential for its proper functioning. The information is supposed to be stored in security and only the employees of an organization can have access to it. The idea of a good database management system is to make the work of an organization easier, faster and of higher quality, because the easier and the faster the access to the data is, the faster the work will be. Moreover, if the information becomes out-of-date, the experts can modify it and introduce the necessary changes to make it valid.
1. What are the roles of a database in present scenario?
Caselet 2
The most dramatic advance of the past decade in software technology has been the development of database management systems (DBMS). There is little question about the potential of these systems for enhancing system support to managers and users while reducing design, structuring, and
END OF SECTION A
Examination Paper of Information Technology
3
IIBM Institute of Business Management
maintenance problems. Database systems also provide a way of improving information system flexibility by decoupling user-oriented data structures from physical storage methods. In spite of the vast potential of database management systems, the information systems community has not reacted with the total enthusiasm that might have been expected. Significant resistance has been encountered in some organizations, both from users, systems managers, and programming staff members. Although the literature on the features of database systems is substantial, there is little discussion of resistance problems encountered during the actual implementation and use of these systems in organizations. The purpose of this panel is to examine issues related to resistance toward DBMS in organizations. The panel members, each of whom is experienced in this area, will examine a number of organizational, technical, and application issues pertinent to the problem of resistance. The discussion will focus on why this resistance has occurred and how, if at all possible, it could have been avoided. Both behavioral and technical issues will be examined. This session should be of interest to both the practitioner and theorist alike. Database management systems are collectively the most significant software product advance in the last decade. There is little question about the potential of these systems for improving data management in organizations. Yet not all persons show a level of enthusiasm for these systems that their capabilities would merit. Users and systems persons alike have been known to resist acquisition and/or introduction of database management systems, sometimes strongly. In the discussion that follows, the problem of resistance as it applies to database management systems is introduced. The intent is to raise issues for research and investigation rather than to provide concrete answers to problems.
1. Discuss various anomalies in databases. How would you improve data management in organizations?
Section C: Applied Theory (30 marks)
This section consists of Long Questions.
Answer all the questions.
Each question carries 15 marks.
Detailed information should form the part of your answer (Word limit 150 to 200 words).
1. What do you understand by relational data model? Explain relational constraints and relational database schemas
2. What are the similarities and dissimilarities in the software development life cycle and database development life cycle?
END OF SECTION B
END OF SECTION C
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