Systems and methods for general aggregation of characteristics and key figures

• US 8,150,749 B2
• Filed: 08/18/2009
• Issued: 04/03/2012
• Est. Priority Date: 09/30/2004
• Status: Active Grant

• Current Assignee: SAP SE
• Original Assignee: SAP AG (SAP SE)
• Inventors: Kahn, Markus, Baumann, Marcus
• Primary Examiner(s): Badii, Behrang
• Application Number:
  US12/461,615
• Publication Number:
  US 20090313157A1
• Time in Patent Office: 959 Days
• Field of Search: 707/102, 707/6, 717/149, 718/100, 725/87, 725/112, 348/E7.071, 348/E17.111
• US Class Current: 705/35
• CPC Class Codes: G06Q 20/108 Remote banking, e.g. home banking
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

Computer-implemented methods, computer systems, and computer programs product are provided for automated generic and parallel aggregation of characteristics and key figures of unsorted mass data being of specific economic interest, particularly associated with financial institutions, and with financial affairs in banking practice. The parallel aggregation may reduce the amount of data for a customer defined granularity for the purpose of facilitating the handling of raw data related to all areas of credit risk management in banking practice. Moreover, the computing power of software and the software performance run time, respectively, may be improved in the case of mass data.

Claims

1. A computer-implemented method for automated generic and parallel aggregation of characteristics and key figures of data associated with financial institutions and with financial affairs in banking practice, the method comprising:

receiving, at a data processing system, mass data from a single database of a single data source or from different databases of different data sources, the mass data comprising a plurality of records, the records being associated with granularity characteristics and key figures;

selecting, according to a customer-defined aggregation, granularity characteristics of the received mass data, key figures of the received mass data, and aggregation operations associated with the key figures;

generating a plurality of data packages from the received mass data, the data packages comprising a plurality of records, the plurality of records of the data packages being smaller than the plurality of records of the received mass data;

processing, using a processor of the data processing system, the data packages to reduce a number of records in the data packages according to the customer-defined aggregation, wherein the processing comprises;

identifying a granularity level associated with the selected granularity characteristics, the identified granularity levels defining an order of the selected granularity characteristics;

sorting the records of the data packages according to the defined order of granularity characteristics;

aggregating the sorted records of the data packages for the selected key figures using the selected aggregation operations, the aggregation reducing the records of the data packages; and

identifying adjacent data packages by comparing, for the aggregated data packages, a key of a first record of the aggregated data packages with a key of a first record and a key of a last record of the other aggregated data packages, the identifying comprising;

computing termination criteria for pairs of the aggregated data packages, the termination criteria having the form;

key_pos1,xε

(key_pos1,y;

key<sub>posmax;

y</sub>),

 

wherein pos1 represents a first position of a data package, posmax represents a last position of a data package, and x and y represent numbers of data packages; and

identifying adjacent packages based on a violation of corresponding ones of the termination criteria, the adjacent data packages having first record keys that are closest together; and

saving, to a memory of the data processing system, the aggregated records of the data packages, wherein the stored records comprise fewer records than the received mass data at the customer-defined granularity.

2. The method of claim 1, wherein selecting comprises:

selecting the granularity characteristics from at least one of;

(i) a predetermined granularity characteristic of the received mass data or (ii) a customer-defined granularity characteristic; and

selecting the key figures from at least one of;

(i) a predetermined key figure associated with the received mass data or (ii) a customer-defined key figure.

3. The method of claim 1, wherein selecting comprises:

selecting the aggregation operation from a predetermined aggregation operation of a function pool and a customer defined aggregation operation.

4. The method of claim 1, further comprising:

enriching the generated data packages through parallel pre-processing using a secondary data source.

5. The method of claim 1, further comprising:

enriching the aggregated data packages through parallel post-processing using a secondary data source.

6. The method of claim 1, wherein processing further comprises processing the data packages in one or more jobs, the jobs comprising a plurality of the data packages.

7. The method of claim 6, wherein the jobs are processed in a parallel processing mode using a single processor.

8. The method of claim 6, wherein the jobs are processed in a parallel processing mode using a network of processors.

9. The method of claim 1, wherein processing further comprises, when adjacent data packages are identified:

merging the adjacent data packages to generate merged data packages; and

processing the merged data packages to reduce a number of records in the merged data packages according to the customer-defined aggregation.

10. The method of claim 1, wherein when no adjacent data packages are identified, the aggregated data packages are disjoint with respect to the selected granularity characteristics of the customer-defined granularity.

11. A computer system configured to perform automated generic and parallel aggregation of characteristics and key figures of data associated with financial institutions and with financial affairs in banking practice, comprising:

a module configured to receive mass data from a single database of a single data source or from different databases of different data sources, the mass data comprising a plurality of records, the records being associated with granularity characteristics and key figures;

a module configured to select, according to a customer-defined aggregation, granularity characteristics of the received mass data, key figures of the received mass data, and aggregation operations associated with the key figures;

a module configured to generate a plurality of data packages from the received mass data, the data packages comprising a plurality of records, the plurality of records of the data packages being smaller than the plurality of records of the received mass data;

a processor configured to process the data packages to reduce a number of records in the data packages according to the customer-defined aggregation, wherein the processor is further configured to;

identify a granularity level associated with the selected granularity characteristics, and the identified granularity levels defining an order of the selected granularity characteristics;

sort the records of the data packages according to the defined order of granularity characteristics;

aggregate the sorted records of the data packages for the selected key figures using the selected aggregation operations, the aggregation reducing the records of the data packages; and

identify adjacent data packages by comparing, for the aggregated data packages, a key of a first record of the aggregated data packages with a key of a first record and a key of a last record of the other aggregated data packages, the identifying comprising;

computing termination criteria for pairs of the aggregated data packages, the termination criteria having the form;

key_pos1,xε

(key_pos1,y;

key<sub>posmax;

y</sub>),

 

wherein pos1 represents a first position of a data package, posmax represents a last position of a data package, and x and y represent numbers of data packages; and

identifying adjacent packages based on a violation of corresponding ones of the termination criteria, the adjacent data packages having first record keys that are closest together; and

a memory configured to store the aggregated records of the data packages, wherein the stored records comprise fewer records than the received mass data at the customer-defined granularity.

12. The computer system of claim 11, wherein the one or more processors are further configured to enrich the generated data packages through parallel pre-processing using a secondary data source.

13. The computer system of claim 11, wherein the one or more processors are further configured to enrich the aggregated data packages through parallel post-processing using a secondary data source.

14. The computer system of claim 11, wherein when adjacent data packages are identified, the one or more processors are further configured to:

merge the adjacent data packages to generate one or more merged data packages; and

process the merged data packages to reduce a number of records in the merged data packages according to the customer-defined aggregation.

15. The computer system of claim 11, wherein when no adjacent data packages are identified, the aggregated data packages is disjoint with respect to the identified granularity characteristics of the received mass data.

16. A computer-readable storage medium comprising a plurality of instructions that, when executed by a processor, perform a method for automated generic and parallel aggregation of characteristics and key figures of data associated with financial institutions and with financial affairs in banking practice, the method comprising:

receiving mass data from a single database of a single data source or from different databases of different data sources, the mass data comprising a plurality of records, the records being associated with granularity characteristics and key figures;

selecting, according to a customer-defined aggregation, granularity characteristics of the received mass data, key figures of the received mass data, and aggregation operations associated with the key figures;

generating a plurality of data packages from the received mass data, the data packages comprising a plurality of records, the plurality of records of the data packages being smaller than the plurality of records of the received mass data;

processing the data packages to reduce a number of records in the data packages according to the customer-defined aggregation, wherein the processing comprises;

identifying a granularity level associated with the selected granularity characteristics, and the identified granularity levels defining an order of the selected granularity characteristics;

sorting the records of the data packages according to the defined order of granularity characteristics;

aggregating the sorted records of the data packages for the selected key figures using the selected aggregation operations, the aggregation reducing the records of the data packages; and

identifying adjacent data packages by comparing, for the aggregated data packages, a key of a first record of the aggregated data packages with a key of a first record and a key of a last record of the other aggregated data packages, the identifying comprising;

computing termination criteria for pairs of the aggregated data packages, the termination criteria having the form;

key_pos1,xε

key_pos1,y;

key<sub>posmax;

y</sub>),

 

wherein pos1 represents a first position of a data package, posmax represents a last position of a data package, and x and y represent numbers of data packages; and

identifying adjacent packages based on a violation of corresponding ones of the termination criteria, the adjacent data packages having first record keys that are closest together; and

saving the aggregated records of the data packages, wherein the stored records comprise fewer records than the received mass data at the customer-defined granularity.

17. The computer-readable storage medium of claim 16, the method further comprising:

enriching the generated data packages through parallel pre-processing using a secondary data source.

18. The computer-readable storage medium of claim 16, the method further comprising:

enriching the aggregated data packages through parallel post-processing using a secondary data source.

19. The computer-readable storage medium of claim 16, wherein processing further comprises, when adjacent data packages are identified:

merging the adjacent data packages to generate one or more merged data packages; and

processing the merged data packages to reduce a number of records in the merged data packages according to the customer-defined aggregation.

20. The computer-readable storage medium of claim 16, wherein when no adjacent data packages are identified, the aggregated data packages are disjoint with respect to the identified granularity characteristics of the received mass data.