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Download JNTUA MBA 2018 May-June Supply 3rd Sem 14E00319 Data Warehousing And Mining Question Paper

Download JNTUA (JNTU Anantapur) MBA (Master of Business Administration) 2018 May-June Supplementary 3rd Sem 14E00319 Data Warehousing And Mining Previous Question Paper

This post was last modified on 27 July 2020

JNTU Anantapur MBA 3rd Sem last 10 year question papers 2010 -2020 -All regulation- 2nd Year 1st Sem


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Code: 14E00319

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Time: 3 hours

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MBA III Semester Supplementary Examinations May 2018

DATA WAREHOUSING & MINING

(For students admitted in 2014, 2015 & 2016 only)

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PART - A

(Answer the following: (05 X 10 = 50 Marks)


  1. Explain the components of organizational memory.

    OR

    Explain in detail individual data management.

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  2. Explain sharing data between functional units.

    OR

    What are the differences among sharing data between functional units and sharing data between different levels of users? Explain in detail.

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  4. Explain an advantage of the multi dimensional database structure over the relational database structure for data warehousing applications.

    OR

    What is metadata and explain three reasons why it can be useful?

  5. Compare and contrast the data driven and the application-driven approaches to develop a data warehouse? Explain.

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    OR

  6. Explain conceptual structure of neural networks in data mining.

    OR

    Explain one significant disadvantage of the decision tree approach to data mining.

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PART - B

(Compulsory question, 01 X 10 = 10 Marks)

Case Study:

A Hospital collaborated with medical college is designing a data warehouse to enable deans, department chair and the superintendent's office to optimize department services, in terms of which services are offered in how many sections and at what times. The data warehouse planners hope they will be able to do this better after examining historical demand for department services and extrapolating any trends that emerge.

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Questions:

  1. Give three dimension data elements and two fact data elements that could be in the database for this data warehouse.
  2. Draw a data cube for this database.
  3. Suggest an aggregation that could apply to one of the dimension data elements.

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