Dynamic discovery of abstract rule set required inputs

• US 20070112745A1
• Filed: 11/10/2005
• Published: 05/17/2007
• Est. Priority Date: 11/10/2005
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Dettinger, Richard, Kolz, Daniel
• Granted Patent: US 7,440,945 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 16/289 Object oriented databases
• CPC Class Codes: Y10S 707/99931 Database or file accessing
• CPC Class Codes: Y10S 707/99932 Access augmentation or optimizing
• CPC Class Codes: Y10S 707/99936 Pattern matching access
• CPC Class Codes: Y10S 707/99945 Object-oriented database structure processing

Abstract

A method, system and article of manufacture for processing rule sets and, more particularly, for processing abstract rule sets. One embodiment provides a computer-implemented method of managing execution of an analysis routine on input data. The method comprises receiving input and receiving user-selection of an analysis routine for processing the received input. The analysis routine is defined by at least one abstract rule set having one or more abstract rules each having a conditional statement and a consequential statement. The method further comprises executing the analysis routine on the received input, comprising (i) identifying all required inputs for each abstract rule of the analysis routine, (ii) determining whether the received input includes data for all required inputs, and (iii) if the received input does not include data for one or more of the required inputs, retrieving suitable data for the one or more of the required inputs from the database.

Claims

1. A computer-implemented method of managing execution of an analysis routine on input data, comprising:

receiving input;

receiving user-selection of an analysis routine for processing the received input;

wherein the analysis routine is defined by at least one abstract rule set having one or more abstract rules each having a conditional statement and a consequential statement;

wherein the consequential statement defines a particular recommendation that is returned when the conditional statement is satisfied;

wherein the conditional statement and the consequential statement are defined using logical field definitions defined in an abstraction model that models underlying physical data in a manner making a schema of the physical data transparent to a user of the abstraction model; and

executing the analysis routine on the received input, comprising;

identifying all required inputs for each abstract rule of the analysis routine;

determining whether the received input includes data for all required inputs; and

if the received input does not include data for one or more of the required inputs, retrieving suitable data for the one or more of the required inputs from a database.

2. The method of claim 1, wherein receiving input comprises executing a query against the database to obtain a query result, the query result comprising the received input.

3. The method of claim 1, wherein retrieving the suitable data from the database comprises:

generating a query with result fields corresponding to the one or more of the required inputs; and

executing the generated query against the database to obtain the suitable data.

4. The method of claim 3, wherein the executed generated query returns a plurality of data values for at least one of the result fields;

and further comprising;

selecting one of the plurality of data values as input to a respective abstract rule for which the generated query was executed.

5. The method of claim 4, wherein selecting the one of the plurality of data values comprises:

prompting a user having selected the analysis routine to indicate the selected one of the plurality of data values; and

receiving user input indicating the selected one of the plurality of data values.

6. The method of claim 4, wherein selecting the one of the plurality of data values comprises:

identifying a point in time of current execution of the analysis routine; and

identifying, as the selected one of the plurality of data values, a data value that was obtained at a point in time being the closest point in time before the identified point in time.

7. The method of claim 1, wherein executing the analysis routine on the received input further comprises:

if the received input includes data for all required inputs, returning the particular recommendation as an analysis result for the analysis routine only if the conditional statement is resolved to true for all required inputs.

8. The method of claim 1, wherein executing the analysis routine on the received input further comprises:

identifying data values for all required inputs from the received input and the retrieved suitable data; and

running the analysis routine on the identified data values.

9. The method of claim 8, wherein running the analysis routine on the identified data values comprises:

transforming each abstract rule of the at least one rule set which defines the analysis routine into a transformed rule; and

executing, by the rules engine, each transformed rule on the identified data values.

10. The method of claim 9, wherein executing each transformed rule comprises:

if the conditional statement of a given executed transformed rule is resolved to true, returning the particular recommendation defined by the consequential statement of the given executed transformed rule as analysis result.

11. The method of claim 1, wherein all required inputs of the analysis routine describe aspects of a medical condition of a patient of a medical institution;

and the analysis routine is configured to provide a recommendation for treatment of the patient.

12. A computer-readable medium containing a program which, when executed by a processor, performs operations for managing execution of an analysis routine on input data, the operations comprising:

receiving input;

receiving user-selection of an analysis routine for processing the received input;

wherein the analysis routine is defined by at least one abstract rule set having one or more abstract rules each having a conditional statement and a consequential statement;

wherein the consequential statement defines a particular recommendation that is returned when the conditional statement is satisfied;

wherein the conditional statement and the consequential statement are defined using logical field definitions defined in an abstraction model that models underlying physical data in a manner making a schema of the physical data transparent to a user of the abstraction model; and

executing the analysis routine on the received input, comprising;

identifying all required inputs for each abstract rule of the analysis routine;

determining whether the received input includes data for all required inputs; and

if the received input does not include data for one or more of the required inputs, retrieving suitable data for the one or more of the required inputs from a database.

13. The computer-readable medium of claim 12, wherein receiving input comprises executing a query against the database to obtain a query result, the query result comprising the received input.

14. The computer-readable medium of claim 12, wherein retrieving the suitable data from the database comprises:

generating a query with result fields corresponding to the one or more of the required inputs; and

executing the generated query against the database to obtain the suitable data.

15. The computer-readable medium of claim 14, wherein the executed generated query returns a plurality of data values for at least one of the result fields;

and the operations further comprising;

selecting one of the plurality of data values as input to a respective abstract rule for which the generated query was executed.

16. The computer-readable medium of claim 15, wherein selecting the one of the plurality of data values comprises:

prompting a user having selected the analysis routine to indicate the selected one of the plurality of data values; and

receiving user input indicating the selected one of the plurality of data values.

17. The computer-readable medium of claim 15, wherein selecting the one of the plurality of data values comprises:

identifying a point in time of current execution of the analysis routine; and

identifying, as the selected one of the plurality of data values, a data value that was obtained at a point in time being the closest point in time before the identified point in time.

18. The computer-readable medium of claim 12, wherein executing the analysis routine on the received input further comprises:

if the received input includes data for all required inputs, returning the particular recommendation as an analysis result for the analysis routine only if the conditional statement is resolved to true for all required inputs.

19. The computer-readable medium of claim 12, wherein executing the analysis routine on the received input further comprises:

identifying data values for all required inputs from the received input and the retrieved suitable data; and

running the analysis routine on the identified data values.

20. The computer-readable medium of claim 19, wherein running the analysis routine on the identified data values comprises:

transforming each abstract rule of the at least one rule set which defines the analysis routine into a transformed rule; and

executing, by the rules engine, each transformed rule on the identified data values.

21. The computer-readable medium of claim 20, wherein executing each transformed rule comprises:

if the conditional statement of a given executed transformed rule is resolved to true, returning the particular recommendation defined by the consequential statement of the given executed transformed rule as analysis result.

22. The computer-readable medium of claim 12, wherein all required inputs of the analysis routine describe aspects of a medical condition of a patient of a medical institution;

and the analysis routine is configured to provide a recommendation for treatment of the patient.