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Informix --- Where NAG Numerics Matter
In the world of finance, profits rise and fall with every market tick…Today, gauging how the market will behave tomorrow is less about hunches and more about math. All major banks and finance houses now devote considerable energy to building complex mathematical models that build analyses of projected future behavior of assets based on their historic prices. Typically, decades of data go into these models, and changes in value are considered after each and every transaction. This means that as the markets tick, quantitative analysts scurry to find their way through terabyte-sized databases. The Informix NAG Datablade helps them create the accurate financial analyses they need 'way fast'.Time-Series Data Becomes Manageable with the help of the Informix NAG Financial DatabladeFor large finance houses, assessing the financial impact of ticker data used to take days. Now this is possible in close to real time.By storing data in a time series, Informix reduces a dataset to around 1/5 of its original size and then stores it contiguously on disk for fast access by the server. Also on that server are 50 of NAG’s most powerful mathematical functions. By performing the analysis close to the data, the relatively slow process of moving large amounts of data around the system is avoided, providing an excellent performance boost. Thus, the server-centric system and the Informix NAG Datablade give quantitative analysts everything they need to make in depth analyses of massive financial datasets very quickly. Calculations are speeded anywhere from 20 to 2500 times. Why Use NAG Components?Terry Ralph, Executive Director, Database Business Development, of Informix suggests that any company with top quality software would be well served by using NAG components."When Informix developed this elegant way to handle time series data, it was only natural that we pair with the world’s expert in numerical calculations and include their routines in our software. NAG routines provide an incomparably powerful toolset to anyone who wants to do a statistical analysis rather than a relational analysis.""When Results Matter, Trust NAG" |
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