Page 210 - DCAP603_DATAWARE_HOUSING_AND_DATAMINING

Data Warehousing and Data Mining

notes 10.8 keywords

Dimensions: Dimensions contain a set of unique values that identify and categories data.

Hierarchy: A hierarchy is a way to organize data at different levels of aggregation.
Logical Cubes: Logical cubes provide a means of organizing measures that have the same shape,
that is, they have the exact same dimensions.
OLTP: Databases must often allow the real-time processing of SQL transactions to support
e-commerce and other time-critical applications. This type of processing is known as online
transaction processing (OLTP).

Star Schema: A star schema is a convention for organizing the data into dimension tables, fact
tables, and materialized views.

10.9 self assessment

Fill in the blanks:

1. Data warehouses and .................... tools are based on a multi-dimensional data model.
2. Measures are organized by dimensions, which typically include a ....................
3. Hierarchies and levels have a .................... relationship.
4. A cube shape is .................... dimensional.
5. A .................... stores all of the information about a dimension in a single table.

6. A .................... normalizes the dimension members by storing each level in a separate
table.
7. Data Warehouses use Online Analytical Processing (OLAP) to formulate and execute
....................
8. The .................... allows the user to summarise data into a more general level in hierarchy.
9. .................... allows the user to focus the analysis of the data on a particular perspective
from one or more dimensions.
10. .................... products organize, navigate and analyze data typically in an aggregated
form.

10.10 review Questions

1. Describe materialized views with the help of a suitable example.
2. Write short note on snowflake schema.
3. “Concept hierarchies that are common to many applications may be predefined in the data
mining system”. Explain.
4. “PIVOT is used to rotate a given data cube to select a different view.” Discuss
5. Describe OLAP operations in the multi-dimensional data model.
6. Describe PIVOT commands in detail with the help of suitable example.

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