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My today paper of CS614

Paper both easy tha, and both acha huwa…….

Total questions were 26……….. 20 mcq’s and 6 Questions


Q#1:2 ways to simplify ER? 2 Marks
Answer: De normalization and Dimensional modeling

Q#2: Which Type of anomalies “Lexical error” can be used..? 2 Marks

Answer: Syntactical error….

Q#3: Tree types of errors or problem due to duplicatation. 3 Marks


Q#5: Merge/purge problem in data cleansing. 5 Marks

Q#6 Suppose there is a table sale. Grain is “sales by day by product by store. Identify at least three facts so that sales table can easily be built. (5 marks)
Answer (page 74)

• Quantity sold
• Amount
• Sales volume
• Total Rs.sales

Some mcq’s were

1. 3NF removes even more data redundancy than 2NF but it is at the cost of

  • Simplicity and performance page 48
  • Complexity
  • No of table
  • Relations

2. ________ is an application of intelligence and experience.


  • Skill


  • Power


  • Knowledge

3. Transactional fact tables do not have records for events that do not occur. These are called


  • Not Recording Facts pg 120 Fact-less Facts


  • Null Facts


  • Empty Facts

4. "Change Data Capture" is one of the challenging technical issues in _____________


  • Data Extraction pg 149


  • Data Loading


  • Data Transformation
  • Data Cleansing



5. the most common use of range partitioning in data warehouse is on


  • Date pg 66


  • Most redundant column Fact


  • Dimensions

6. Which statement is true for De-Normalization?


  • Redundant data is a performance liability at query time, but is a performance benefit at update time.


  • Redundant data is a performance benefit at both query time and update time. Redundant data is a performance liability at both query time and update time.


  • Redundant data is a performance benefit at query time, but is a performance liability at update time. 51

7.    Very complex and poorly documented source system. 2.    Data has to be extracted not once but many times. 3.    People extracting data have limited expertise. Which of the following option represents correct reason?

  • 1 & 2 only pg 132


  • 1 & 3 only


  • 2 & 3 only All 1, 2 and 3

8. Select the statement which is true for Insurance Data Warehouse


  • It has Long Operational Business Cycle   36
  • It has Long Development & Implementation Cycle
  • It has Short Operational Business Cycle
  • It has Short Development & Implementation Cycle 4.


9.. Syntactically Dirty Data class of anomalies includes which of the following:


  1. Lexical Errors


  1. Integrity Constraints Violation
  2. Business Rule Contradiction
  3. Irregularities


  1. Duplication


  • Option 1 and 4 pg 160
  • Option 2 and 3


  • Option 2, 3, and 5
  • Option 1, 4, and 5


10. A company has implemented data warehouse for analytical purpose. Quantity sold is stored as a fact. This quantity sold is


  • Additive Fact 119


  • Non-Additive Fact

11. Fact-less fact table is a fact table without numeric fact columns. It is used to capture relationship between __________


  • Dimensions pg 121


  • Attributes


  • Tables


  • Facts

12. Data ____________ is vitally important to the overall health of a warehouse project.


1.  Cleansing  2.  Cleaning 3.  Scrubbing


Which of the following options is true?

  • Option 1 only pg 158


  • Option 2 only


  • Option 1 & 2 only Option 1, 2 & 3

13. The need to synchronize data upon update is called

  •  Data Manipulation
  •  Data Replication
  •  Data Coherency (Page 12)
  • Data Imitation

14. During ETL process of an organization, suppose you have data which can be transformed using any of the

transformation method. Which of the following strategy will be your choice for least complexity?

  • One-to-One Scalar Transformation (Page 144)
  • One-to-Many Element Transformation
  • Many-to-Many Element Transformation
  • Many-to-One Element Transformation

15. Multidimensional databases typically use proprietary __________ format to store pre-summarized cube


  • File (Page 79)
  • Application
  • Aggregate
  • Database

16. Multi-dimensional databases (MDDs) typically use ___________ formats to store pre-summarized cube


  • SQL
  • proprietary file (Page 79)
  • Object oriented
  • Non- proprietary file

Remember me in your prayers…

Today paper of cs614
80% mcqs from past

1.describe defrnc B/w Additv and Non additv?
2.Statment is correct or incorrect( If defects are found in the process of attribute domain validation thn it is betr to fix the error in DWH and leave the data source as it is).
3. Merge/purge problem
4.table diya hwa tha un me say addtv fact ko find karna tha.
5."Dimnsion hav hierarchies" Specify the hierarchies.
6.In data extrctn " change data captr" is considerd as the most chalnging activity, y?

kis file sy aya can u share here ?pls

2.Statment is correct or incorrect( If defects are found in the process of attribute domain validation thn it is betr to fix the error in DWH and leave the data source as it is

Point to be noted is that, if at all possible, fix the problem in the source system. People have the tendency of applying fixes in the DWH. This is a wrong i.e. if you are fixing the problems in the DW; you are not fixing the root cause. A crude analogy would clarify the point. If you keep cleaning the lake, and keep on flushing the toilet in the lake, you are not solving the problem. The problem is not being fixed at the source system, therefore, it will persist.

thanks dear's

My  paper of CS614 held on 2oth june 

mostly mcq's from moaz file






Q: Difference b/w additive and non additive facts(2).  


There can be two types of facts i.e. additive and non-additive. 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.



Q: What is merge and purge problem? (2)


Within the data warehousing field, data cleansing is applied especially when several databases are merged. 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.


Q: Basic task of data transmission (3)


I. Selection


II. Splitting/Joining


III. Conversion


IV. Summarization


V. Enrichment


Q#4. How clustering and associative rule work (3)


Clustering is the technique of reshuffling, relocating exiting segments in given data which is mostly heterogeneous so that the new segments have more homogeneous data items. This can be very easily understood by a simple example. Suppose some items have been segmented on the basis of color in the given data. Suppose the items are fruits, then the green segment may contain all green fruits like apple, grapes etc. thus a heterogeneous mixture of items. Clustering segregates such items and brings all apples in one segment or cluster although it may contain apples of different colors red, green, yellow etc. thus a more homogeneous cluster than the previous cluster.



Q: Define Physical Extraction and difference between offline and online extraction.[5]


Depending on the chosen logical extraction method and the capabilities and restrictions on the source side, the extracted data can be physically extracted by two mechanisms. The data can either be extracted online from the source system or from an offline structure.


Q: OLAP Implementations  (5)


1. MOLAP: OLAP implemented with a multi-dimensional data structure.

2. ROLAP: OLAP implemented with a relational database.

3. HOLAP: OLAP implemented as a hybrid of MOLAP and ROLAP.

4. DOLAP: OLAP implemented for desktop decision support environments





kisi or ka b agr ho gya to share kre plzzz

Plz share  Moaz Ali  file for MCQs



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