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CS614 Spring 2018 Final Term Papers Pattern & Questions 01 September 2018 to 12 September 2018 & Helping Material

CS614 Spring 2018 Final Term Papers Pattern & Questions 01 September 2018 to 12 September 2018 & Helping Material

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  1. We can identify the Session in World wide web by using time-contiguous log entries, there are some limitations, briefly explain any two limitations.
  2. Indentify the statement is correct or not.

”the problem of referential  integrity always occure in traditional OLTP system as well as in DWH”

  1. Advantages of driven attribute.
  2. Hash merge join is fast but expensive.why? and explain nested loop join
  3. Steps of Kimball DWH lifecycle.
  4. What will happen if write text for “date” rather than dd/mm/yyyy?
  5. Activities in requirement planning phase.

 

Sobia thanks for sharing 

my pleasure

sis koi idea do gi khan se tyri ki mcqs khan s ay pas s ya bok s?

moaz+waqar+arslan,zain sari files prepare ke the lakin book prepare kro ap, is sy best way koi nahi preparation ka
max 10 mcqs from p.p, rest were new n conceptual

Paper of CS614 Data warehouse Mcqs 75 percent from past paper subjective question: what is the difference b/w supervised and unsupervised learning and its technique .2 List five static attribute from scouts .3 list the name of any three dynamic attribute from scouts .4 how we split vertical split and why required vertical splitting . 5. Dimensional modeling m sy ak question tha tables m sy facts column batnana tha . 6. hash bases indexing ka ak long question tha.when the hash based indexing is used 7. web warehousing ka ak question tha.

mcqs kon c file prepare ki apny kind attache these file

  • What is the main source of web warehousing ?
  • Limitations of k-means clustering
  • partitioning types of shared nothing RDBMS architecture
  • gender records is missing?
  • "A situation may arise when an organization find itself Drowning in data and starving for information”. How data warehouse helps organizations avoid this situation?
  • What are the tasks performed through import / export data wizard to load data?Write any three.
  • List down any three Dynamic Attributes recorded by the scouts in Agriculture Data Warehouse Case Study.
  • Identify the given statement as correct or incorrect and justify your answer in either case.

"The problem of Referential Integrity always occurs in traditional OLTP system as well as in DWH".

  • From the list given below identify the "derived" attributes.

1) ProductID

2) Age

3) GPA (Grade Point Average)

4) City

5) AverageSale

 

  • List down the three steps which are performed in Requirement Definition Phase of Kimball’s approach for Data Warehouse Development Lifecycle?

 

 mcqs were difficult :(

Asalam Alikum all.

I gave my CS614 paper today. 80% of MCQs were from attached files.

For subjective I just prepared under mentioned questions and 80% of subjective was also from these questions.

We can identify the Session in World wide web by using time-contiguous log entries, there are some limitations, briefly explain any two limitations.

Page 364

Indentify the statement is correct or not.

”the problem of referential  integrity always occure in traditional OLTP system as well as in DWH”

Page 189 only happens in DWH

Advantages of driven attribute.

 

Hash based join is fast but expensive.why? and explain nested loop join

Page 239 Hash based = VLDB          Nested = OLTP

Steps of Kimball DWH lifecycle.

Page 289

What will happen if write text for “date” rather than dd/mm/yyyy?

Page 447

Activities in requirement planning phase.

Page 292

 

what is the difference b/w supervised and unsupervised learning and its technique .

Page 269 - 270

2 List five static attribute from scouts .

Page 342

3 list the name of any three dynamic attribute from scouts .

Page 342

4 how we split vertical split and why required vertical splitting .

Page 56

Dimensional modeling m syak question tha tables m sy facts column batnanatha .

Page 115             

hash bases indexing kaak long question tha.when the hash based indexing is used

Page 227              exact key value eg ID card numetc for data retrieval.

web warehousing kaak question tha.

Page 348

What is the main source of web warehousing ?

Page 349         world wide web is the data source which is the main source of web warehousig

Limitations of k-means clustering

Page 281         k defined – noisy data – non convex – mean is defined

partitioning types of shared nothing RDBMS architecture

Page 211                     hash – key range – list – round robin – range-hash, range-list

 

"A situation may arise when an organization find itself Drowning in data and starving for information”. How data warehouse helps organizations avoid this situation?

Page 10

What are the tasks performed through import / export data wizard to load data?Write any three.

Page 413

List down any three Dynamic Attributes recorded by the scouts in Agriculture Data Warehouse Case Study.

Page 342

Identify the given statement as correct or incorrect and justify your answer in either case.

"The problem of Referential Integrity always occurs in traditional OLTP system as well as in DWH".

Page 189

From the list given below identify the "derived" attributes.

1) ProductID

2) Age

3) GPA (Grade Point Average)

4) City

5) AverageSale

 

Page 60

List down the three steps which are performed in Requirement Definition Phase of Kimball’s approach for Data Warehouse Development Lifecycle?

Page 289         Technology track, data track and analytic application track 

3 ways to access data on web.

Page 350

Is TDQM a one-time process? Justify your answer.

Page 193

in K-mean algorithm O(tkn), what is "t", "k" and "n"? 

T = # iteration             k = #cluster     n = #objects

How can we counter fraud by using DataWarehouse

Page 37

List five common measurements of email, ad campaign and (dont remember)

Page 352         # visitors, # session, most requsted pages, robot activity

Constraints of DTS.

Page 395         unconditional  -           on success       -           on failure

There's a FULL TABLE SCAN query, list other two.

Page 217 & 243          naïve – indexed – temporary index

Market Basket Analysis

Page 261

 

1) pest scouting (5m)

Page 333 & 342

 

2)Reverse proxy (3m)

Page 369

 

3)Role of justification in Planning (5m)

Page 292

 

4)Amdahl law  numerical (5m)

Page 204

 

5)diff b/w Statistics and Data Mining (5m)

Page 256

 

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