Transcript Document 7278971

Non-linear Aggregations in Large-Scale MultiDimensional Cubes
Georges Bory, Quartet Financial Systems
Distributed and Grid Computing in
Computational Finance.
Inria, Sophia Antipolis, October 20th
2008
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Agenda

OLAP cubes for finance
– Non linear behaviours
– Time constraint

ActivePivot solution

Performance and future work
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2
OLAP cubes for Finance

A lot of data
» And not much time to understand it
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A lot of data

Historical Var
– 2 years
– 500 000 deals
» 250 Million values

Monte-Carlo Var
– 5 000 simulations
– 100 000 deals
» 500 Million values

Potential exposure amount
– 100 000 deal
– 500 simulations
– 20 future points
» 1 Billion values
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OLAP cubes for Finance

Organize data into business hierarchies
» Drill down from top to bottom
» Filter
» Drill thru individual trades, scenario
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Business Hierarchies

High Cardinality Levels
Securities: >10 000
• Counterparty: > 2 000
• Time buckets: 80 future strips, >10 000 days
•

Low Cardinality Levels
Books
• Traders
• Currencies
• Index
•
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OLAP cardinality curse
OLAP Cardinality Curse
7000
6000
Memory
5000
4000
Cube
3000
2000
1000
0
1
2
3
4
Dimensions
5
6
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Non linear behaviours

Value at Risk
– Variance, Nth percentile loss

Potential Exposure Amount
– Max (Expectation, 0)
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Time constraint

Any time lost in aggregation is expensive in grid
hardware costs
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Agenda

OLAP cubes for finance
– Non linear behaviours
– Time constraint

ActivePivot solution

Performance and future work
www.quartetfs.com
10
ActivePivot solution

Non linear aggregation
» Aggregate objects rather than values
» Apply operators to aggregated objects
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ActivePivot solution
Compression Algos
7000
6000
Memory
5000
Cube
4000
QC-Tab
3000
QC Tree
2000
1000
0
1
2
4
3
Dimensions
5
6
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Transactional OLAP engine
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