Method and system for aggregating financial measures in a distributed cache

• US 10,162,840 B1
• Filed: 09/02/2015
• Issued: 12/25/2018
• Est. Priority Date: 09/02/2015
• Status: Active Grant

• Current Assignee: JP Morgan Chase Bank, N.A. (JP Morgan Chase & Co)
• Original Assignee: JP Morgan Chase Bank, N.A. (JP Morgan Chase & Co)
• Inventors: Carter, Christopher Richard
• Primary Examiner(s): Al-Hashemi, Sana A
• Application Number:
  US14/842,966
• Time in Patent Office: 1,210 Days
• Field of Search: 707737, 707608, 707692, 707664, 707964, 707661, 707674
• CPC Class Codes: G06F 16/211 Schema design and management
• CPC Class Codes: G06F 16/24539 using cached or materialised query results

Abstract

According to an embodiment of the present invention, a system and method for consolidating financial data comprising: a relational database containing a plurality of schema comprising a staging schema, recent schema and historic schema; a distributed data storage platform comprising a plurality of nodes; and a computer processor, coupled to the relational database and the distributed data storage platform, and programmed to: store financial data in the distributed data storage platform comprising the plurality of nodes; create an aggregation specification to compute an aggregation for a financial measure; determine whether the aggregation is current; compose one or more keys for the aggregation specification; determine one or more dimensions for the keys; responsive to the keys and dimensions, process the aggregation specification via the plurality of nodes; and combine one or more aggregations to create a new aggregation algorithm.

Claims

1. An automated computer implemented system comprising:

a relational database comprising financial data and further containing a plurality of schema comprising a staging schema, recent schema and historic schema;

a distributed data storage platform that supports the relational database, the distributed data storage platform comprising a plurality of nodes; and

a computer processor, coupled to the relational database and the distributed data storage platform, and programmed to;

access the financial data from the relational database in the distributed data storage platform comprising the plurality of nodes;

automatically create an aggregation specification comprising context elements, iteration elements and a formula that represents an algorithm that processes financial market measures made available by the iteration elements, based at least in part on the financial data;

determine whether the aggregation is current;

compose one or more keys for the aggregation specification;

determine one or more dimensions for the keys;

responsive to the keys and dimensions, process the aggregation specification via the plurality of nodes; and

automatically assemble an outcome from each node of the plurality of nodes.

2. The system of claim 1, the computer processor further programmed to:

check for a previously computed aggregation.

3. The system of claim 1, wherein the new aggregation algorithm is created without requiring changes to the programming of the underlying distributed data storage platform.

4. The system of claim 1, wherein the distributed data storage platform accesses source scripts and compiles the source scripts as needed in processing the aggregation specification.

5. The system of claim 1, wherein the keys represent a set of unique identifiers that recover each measure used to compute the aggregation specification.

6. The system of claim 1, wherein the aggregation specification is processed over a serialized form.

7. The system of claim 1, wherein the aggregation specification is processed by recursively executing an aggregation formula upon each outcome of an initial summation.

8. The system of claim 1, wherein the staging schema loads data into the relational database and stores procedures to transform data into a form compatible with data in use.

9. The system of claim 1, wherein the recent schema represents a partition that is tuned to run more quickly than the historic schema.

10. The system of claim 1, wherein the historic schema represents a partition that is tuned to run slower than the recent schema.

11. An automated computer implemented method comprising the steps of:

storing financial data in a relational database in a distributed data storage platform comprising a plurality of nodes, wherein the relational database contains a plurality of schema comprising a staging schema, recent schema and historic schema and the distributed data storage platform comprises a plurality of nodes;

creating an aggregation specification comprising context elements, iteration elements and a formula that represents an algorithm that processes financial market measures made available by the iteration elements, based at least in part on the financial data;

determining whether the aggregation is current;

composing one or more keys for the aggregation specification;

determining one or more dimensions for the keys;

responsive to the keys and dimensions, processing the aggregation specification via the plurality of nodes; and

automatically assemble an outcome from each node of the plurality of nodes.

12. The method of claim 11, further comprising the step of:

checking for a previously computed aggregation.

13. The method of claim 11, wherein the new aggregation algorithm is created without requiring changes to the programming of the underlying distributed data storage platform.

14. The method of claim 11, wherein the distributed data storage platform accesses source scripts and compiles the source scripts as needed in processing the aggregation specification.

15. The method of claim 11, wherein the keys represent a set of unique identifiers that recover each measure used to compute the aggregation specification.

16. The method of claim 11, wherein the aggregation specification is processed over a serialized form.

17. The method of claim 11, wherein the aggregation specification is processed by recursively executing an aggregation formula upon each outcome of an initial summation.

18. The method of claim 11, wherein the staging schema loads data into the relational database and stores procedures to transform data into a form compatible with data in use.

19. The method of claim 11, wherein the recent schema represents a partition that is tuned to run more quickly than the historic schema.

20. The method of claim 11, wherein the historic schema represents a partition that is tuned to run slower than the recent schema.