OLAP Cubes: Basic operations.

1. Sell volume
Cube as a metaphor
• In this example, a visual representation of multiple cubes over date dimension.
OLAP Cubes
Sthefan Berwanger

2. US
TX NY CA
Los
Angeles
San
Diego
New
York
BuffaloHouston Dallas
August
July
2
12
3
17
2012
Hierarchies
• With hierarchies, it`s possible to
control data granularity.
Dimensions and hierarchies
Sthefan Berwanger
2,5 3,0 1,5 2,5 4,0 2,5
3,0 4,0 0,5 1,0 2,5 1,5
1,0 3,0 0,0 2,5 0,5 1,0
2,5 1,5 3,0 0,5 1,0 3,0
Measures

3. 12,5 5,5 10,5
8,0 6,0 5,5
US
TX NY CA
August
July
2012
Data aggregation
• A query in a higher level of a
dimension, brings aggregated data to a
measure.
Measures: Aggregated data
Dimensions and hierarchies
Sthefan Berwanger

4. Slicing
• Two dimensions vary, and one is kept fixed.
Stock 1/98 2/98 3/98 4/98
TEL PN +5% -2% +7% +4%
PET PN +2,5% +3% +4% -1%
BB PN -1% +1,3% +2,3% +2,1%
LAME PN -2% +0,4% +1% +1,4%
Fixed dimension: State
Value: RJ
Operations over OLAP Cube
Sthefan Berwanger

5. Rotation
• Change of dimensions axis.
Fixed dimensions: State, Date, Stock
Values: RJ, 2/98, BB PN
Data TEL PN PET PN BB PN LAME PN
1/98 5% 2,50% -1% -2%
2/98 -2% 3% 1,30% 0,40%
3/98 7% 4% 2,30% 1%
4/98 4% -1% 2,10% 1,40%
Operations over OLAP Cube
Sthefan Berwanger

6. Dice
• All dimensios are kept fixed to obtain a point of data, represented by the cross section of all axis.
Ações 2/98
BB PN
+1,3
%
Fixed dimensions: State, Date, Stock
Values: RJ, 2/98, BB PN
Operations over OLAP Cube
Sthefan Berwanger

7. Drilling
• Drill up (Roll up): Decrease data granularity.
• Drill down: Increase data granularity.
• Drill Across: Navigation over dimension.
US
TX NY CADrill up Drill down
Drill cross
Operations over OLAP Cube
Los
Angeles
San
Diego
New
York
BuffaloHouston Dallas
Sthefan Berwanger