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Non-linear Aggregations in Large-Scale Multi-Dimensional CubesGeorges 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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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
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7000
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Dimensions
Mem
ory
Cube
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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
ActivePivot solution
Non linear aggregation» Aggregate objects rather than values» Apply operators to aggregated objects
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ActivePivot solution
Compression Algos
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7000
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Dimensions
Me
mo
ry Cube
QC-Tab
QC Tree
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Transactional OLAP engine