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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