Page 279 - DCAP603_DATAWARE_HOUSING_AND_DATAMINING

Unit 14: Quality Driven Data Warehouse Design

Fill in the blanks: notes
3. OLAP is not designed to store large volumes of ..................... or binary data.

4. ..................... are a special case of the Information Consumers group.
5. ..................... are physical structures that improve data access time by pre-computing
intermediary results
6. ..................... has become an important strategy to integrate heterogeneous information
sources in organizations.
7. Vendors agree that data warehouses cannot be off-the-shelf products but must be designed
and optimized with great attention to the ..................... situation.
8. A ..................... is designed for a specific purpose.
9. A data warehouse offloads the historical data from the .....................
10. A data warehouse provides a multidimensional view of data in an ..................... designed to
match the types of queries posed by analysts and decision makers.

14.8 review Questions

1. Describe how data mining is a data warehouse tool.
2. Describe the various architectural goals of data warehouse.
3. Write short note on users of data warehouse.

4. Explain with the help of suitable example how will you optimize and materializing of
views.
5. What do you mean by quality driven data warehouse?

6. Describe what is the interaction between quality factors and data warehouse tasks?
7. “The DWQ project will produce semantic foundations for DW design and evolution linked
explicitly to formal quality models”. Explain.
8. Describe various design methodologies of warehouse.

9. “A data warehouse provides a multidimensional view of data in an intuitive model
designed to match the types of queries posed by analysts and decision makers.” Discuss.
10. “The DWQ project will develop policies to extend active database concepts such that data
caching is optimized for a given transaction load on the DW.” Discuss.

answers: self assessment

1. (c) 2. (a)
3. text 4. Executives
5. Materialized views 6. Data warehousing

7. customer 8. relational database
9. OLTP 10. intuitive model

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