This slideshow gives you an overview of Cube Design in Business Intelligence.

1. CUBE DESIGN
BY HANNES MEYER
OnLine Analytical Processing OLAP

2. Agenda
 What are cubes?
 Multidimensionality
 Storage of multidimensional data.
 Hierarchies
 Operations
 Demo

3. What are cubes?
 Multi-dimensional representation of data

4. What are cubes (cont.)?
 syn: Hypercube, multidimensional database (MDB),
olap cube
 Cubes can have more than three dimensions

5. Fact Tables
 Contain numerical measurements of a certain
business process.
 E.g. $12.000 sales in NY store on 12-01-08
 Additionally foreign keys to different dimension
tables
 E.g. further store/sales person information
 Center in star schema

6. Dimension Tables
 Contain attributes by which data can be grouped
 e.g. city/region of store, product category
 Linked to the fact table via their primary keys
 Slowly changing dimensions: dimensions which
change over time. Can be dealt with in 3 ways:
 Overwritingold values
 Add new row to table, distinguish records by versioning
 Add new column (attribute) to existing row

7. Data Storage Models
 relational databases (ROLAP)
 Datain tables
 Summaries stored in precalculated tables
 multi-dimensional databases (MOLAP)
 Data in multidimensional arrays
 + Less disk space
 + Better Performance (precalculated aggregates)
 - Time to aggregate & calculate
 - Updates require recalculation
 Hybrid (HOLAP)

8. Hierarchies
 Grouping of dimensions  e.g. country -> sales
 e.g. month -> semester - region -> state -> city
> quartal -> year -> store
 2008  Germany
 H1 2008  Southern germany
 Q1 2008  BaWue
 Jan 2008  Stuttgart
 Store A
 Feb 2008
 Store B
 March 2008
 Q2 2008 …  Bavaria
 Munich
 H2 2008 …  Store A B C

9. Operations: Slice
 Slicing is the process of retrieving a block of data
from a cube by filtering on one dimension

10. Operations: Dice
 Dicingis the process of retrieving a block of data
from a cube by filtering on all dimensions

11. Operations: Drill Up/ Down
 Drilling up: Presenting data at a higher level on the
hierarchy e.g. Store -> Region
 Drilling Down: Presenting data at a lower level on
the hierarchy Region -> Store

12. Building the cube in SSAS
 Preconditions
 Connecting datasources
 Defining views
 Selecting dimensions
 Define fact & dimension tables & time dimension
 Select measures
 Deploy & query the cube
  Demo