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CS614_Share Your Current Papers of Mid Term ,Fall 2015 at one Place from 19 Dec to 31 Dec 2015.

CS614 ALL Current Mid Term Papers

Fall 2015 at One Place

from 19 Dec 2015 to 31 Dec 2015  

 

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  1. What is the purpose of "aggregate awareness"? (2 marks)

Ans: Aggregate awareness allows using pre-built summary tables by some

front-end tools.

     2. The problems associated with the extracted data can correspond to non-primary keys.List down any four problems associated with the non-primary key. (2 marks)

     3.What is “ranking” in data source selection? Explain with an example. (3 marks)

Ranking is all about selecting the “right” source system. Rank establishment has to be based on which source system is known to have the cleanest data for a particular attribute.

For example, consider the case of the gender data coming from two different source systems A and B. It may be the case that the highest quality data is from source system A, where the boxes for the gender were checked by the customers themselves. But what if someone did not check the gender box? Then you go on to the next cleanest source

system i.e. B, where the gender was guessed based on the name. 

     4. Identify the given statement as correct or incorrect and justify your answer in either case. (3 marks)

     5. In context of MOLAP cube, identify the proportionality relation between the cardinality of dimension and cube size. Justify your answer with logical reason. (5 marks)

When the cardinality of a dimension forces a cube to become larger than what can reasonably be supported in a single cube, it is common practice to partition the cube into multiple “sub-cube” instantiations.  The  sub-cubes are usually defined around logical partitions within the dimensional hierarchies.  For example, if there are a large number of

entries at the most detailed level in a product hierarchy, it may be advisable to create distinct cubes along product category boundaries to divide-and-conquer.

     6. Identify the types of transformations used for the following two scenarios. (5 marks)

  • One data element form the source system is mapped to several columns in the DWH.
  • One set of values is mapped to another set of values using straightforward rules.
  • •   Simple one-to-one scalar transformations.
  • •   One-to-many element transformations.

    •   Complex many-to-many element transformations. ....See  More page 144 from handouts (softcopy)

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20 Mcqz (50 to 60 % from Past Papers)

1-      Two limitation of aggregation? … (2 Marks)

ƒ  Aggregation limits the questions that can be answered.

ƒ   What, when, why, where, what-else, what-next

ƒ  Aggregation can hide crucial facts.

ƒ  The average of 100 & 100 is same as 150 & 50

2-      One too Many Transformation with example … (2 Marks)

A one-to-many transformation is more complex than scalar transformation. As a data element form the source system results in several columns in the DW. Consider the 6×30 address field (6 lines of 30 characters each), the requirement is to parse it into street address lines 1 and 2, city, sate and zip code by applying a parsing algorithm.

3-      What is “ranking” in data source selection? Explain with an example. (3 Marks) Rep

4- Performance vs Trade Space? (3 Marks)

ƒ  Maximum performance boost implies using lots of disk space for storing every  pre-calculation.

ƒ  Minimum performance boost implies no disk space with zero pre-calculation.

ƒ  Using Meta data to determine best level of pre-aggregation from which all other aggregates can be computed.      

 

5- Given a table of DATE, PRODUCT, CITY, SALES_Amount, Average … Choose Additive Facts and described it? (5 Marks)

6- Two Statement given … Tell them Correct or incorrect and described it? (5 Marks)


------------------------------------------------------------------------------------

1. Additive and non additive facts    2 marks

Additive facts are those facts which give the correct result by an addition operation. Examples of such facts could be number of items sold, sales amount etc. Non-additive facts can also be added, but the addition gives incorrect results. Some examples of non-additive facts are average, discount, ratios etc.

2. One to many transformation with example 3 marks 

rep

  1. Aik statement d hoi thi about timestamp  btana tha true hae yan false with reason.

Time Stamps  page 150

4.Star aur snow flock schema ki digrame thi and identify karna tha k kon sa schema hai.

The two Schemas

Figure-13.3: The two schemas

StarSnow-flake         page 105

5. What is murge/purge when we performed the cleansing in the dataware house 5 marks

Records referring to the same entity are represented in different formats in the different data sets or are represented erroneously. Thus, duplicate records will appear in the merged database. The issue is to identify and eliminate these duplicates.

The problem is known as the merge/purge problem

6.    5 Marks

Product Id

Region ID

Period

Quantity

1

N

Month

25

2

N

Month

  50

2

S

Week

30

Find the dimension, primary key & dimension.---------------------------------------------

1-      Two limitation of aggregation? … (2 Marks)

Rep

One too Many Transformation with example … (2 Marks) Rep

What is “ranking” in data source selection? Explain with an example. (3 Marks) Rep
4- Performance vs Trade Space? (3 Marks) Rep

 

5- Given a table of DATE, PRODUCT, CITY, SALES_Amount, Average … Choose Additive Facts and described it? (5 Marks) Rep

6- Two Statement given … Tell them Correct or incorrect and described it Rep

following were the subjective questions;

     1. What is the purpose of "aggregate awareness"? (2 marks) Rep

  1.      2. The problems associated with the extracted data can correspond to non-primary keys.List down any four problems associated with the non-primary key. (2 marks) Rep

 

     3.What is “ranking” in data source selection? Explain with an example. (3 marks) Rep

     4. Identify the given statement as correct or incorrect and justify your answer in either case. (3 marks) Rep

 

     5. In context of MOLAP cube, identify the proportionality relation between the cardinality of dimension and cube size. Justify your answer with logical reason. (5 marks) Rep

     6. Identify the types of transformations used for the following two scenarios. (5 marks) One data element form the source system is mapped to several columns in the DWH.

  • One set of values is mapped to another set of values using straightforward rules. rep


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Q1  : Write two disadvantages of MOLAP?   (2marks)

     1)Long load time ( pre-calculating the cube may take days!).

ƒ   2) Very sparse cube (wastage of space) for high cardinality (sometimes in small

                      hundreds). e.g. number of heaters sold in Jacobabad or Sibi.

 

 Q2; What is merge/purge problem in data cleansing?(2 marks) Rep

 

Q3: Identify the given statement as correct or incorrect and justify your answer in either case. 3    “ROLLUP” is used for transfer the data(something like that sorry I forget full statement)

ƒ  Rollup: summarize data

ƒ  e.g., given sales data, summarize sales for last year by product category

and region... page #80

Q4: The problems associated with the extracted data can correspond to non-primary keys. List down any four problems associated with the non-primary key? 3 Rep

Q5:  What is “ranking” in data source selection? Explain with an example.? 5 Rep

Q6:  The table is given and need to identified the additive fact ? 5  See  lec#15 page 119

 Answer plzzzz???

Q#1The problems associated with the extracted data can correspond to non-primary keys.List down any four problems associated with the non-primary key????

Q#2

 Given a table of DATE, PRODUCT, CITY, SALES_Amount, Average … Choose Additive Facts and described it? (5 Marks)

Q#3

Product Id

Region ID

Period

Quantity

1

N

Month

25

2

N

Month

  50

2

S

Week

30

Find the dimension, primary key & dimension.

Q#4

     6. Identify the types of transformations used for the following two scenarios. (5 marks)

  • One data element form the source system is mapped to several columns in the DWH.
  • One set of values is mapped to another set of values using straightforward rules.

Q#1The problems associated with the extracted data can correspond to non-primary keys.List down any four problems associated with the non-primary key????
Ans:
(1)Same primary key but different data

(2)Same entity with different keys

(3)Primary key in same information
(4)Source might contain invalid data.

Q#4

     6. Identify the types of transformations used for the following two scenarios. (5 marks)

  • One data element form the source system is mapped to several columns in the DWH.Ans:(One to Many ..)
  • One set of values is mapped to another set of values using straightforward rules.(One to one ..)

Q#3

Product Id

Region ID

Period

Quantity

1

N

Month

25

2

N

Month

  50

2

S

Week

30

Find the dimension, primary key & dimension.

Ans:

Primary key: product Id, region Id
Dimension: Period, quantity
1.Automatic Data Cleansing
1) Statistical
2) Pattern Based3) Clustering
4) Association Rules

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