System and method for the longitudinal analysis of education outcomes using cohort life cycles, cluster analytics-based cohort analysis, and probabilistic data schemas

• US 7,930,262 B2
• Filed: 10/18/2007
• Issued: 04/19/2011
• Est. Priority Date: 10/18/2007
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Goodwyn, Jay A., Young, Anthony W., Kraemer, James R., Friedlander, Robert R.
• Primary Examiner(s): Abel-Jalil; Neveen
• Assistant Examiner(s): PEACH, POLINA G
• Application Number:
  US11/874,382
• Publication Number:
  US 20090106179A1
• Time in Patent Office: 1,279 Days
• Field of Search: None
• US Class Current: 706/45
• CPC Class Codes: G06N 5/04 Inference or reasoning models

Abstract

A computer implemented method, computer program product, and data processing system for performing analysis on a plurality of data stored in a database. In an illustrative method, a first cohort is generated from the plurality of data. An optimal control cohort is generated from the plurality of data. Generating is performed based on the first cohort and at least one constraint, and a mathematical process is used to derive the optimal control cohort. A first inference is generated based on a comparison of the first cohort to the optimal control cohort, wherein the first inference is absent from the database. In an illustrative example, the first inference is stored.

Claims

1. A computer implemented method for performing analysis on a plurality of data stored in a database, the computer implemented method comprising:

generating a first cohort from the plurality of data;

generating an optimal control cohort from the plurality of data, wherein generating is performed based on the first cohort and at least one constraint, and wherein a mathematical process is used to derive the optimal control cohort;

generating a first inference based on a comparison of the first cohort to the optimal control cohort, wherein the first inference is absent from the database;

storing the first inference;

receiving an I^th query at the database regarding an I^th fact, wherein I is an integer reflecting how many times a recursion process has been conducted, wherein the I^th fact becomes a compound fact that includes multiple sub-facts on a subsequent iteration of the recursion process;

establishing the I^th fact as a frame of reference for the I^th query based on the first cohort and based on the optimal control cohort;

applying an I^th set of rules to the I^th query, wherein the I^th set of rules is determined for the I^th query according to a J^th set of rules, wherein J is equal to I−

1, wherein the I^th set of rules determines how the plurality of data are to be compared to the I^th fact, and wherein the first set of rules determines an I^th search space for the I^th query;

executing the I^th query to generate a second inference, wherein the second inference is determined from comparing the plurality of data according to the I^th set of rules;

storing the second inference; and

evaluating a hypothesis within a research study based on the first cohort, the optimal control cohort, the first inference, and the second inference performed by a processor.

2. The computer implemented method of claim 1 wherein the second inference either validates the first inference or is based on the first inference.

3. The computer implemented method of claim 1 wherein the second inference further comprises one of a probability of the second inference being correct or a probability that the first inference is correct.

4. The computer implemented method of claim 1 wherein the first inference further comprises a probability of the first inference being correct.

5. The computer implemented method of claim 1 wherein the database comprises a plurality of divergent data, wherein the plurality of divergent data includes a plurality of cohort data, wherein each datum of the database is conformed to the dimensions of the database, wherein each datum of the plurality of data has associated metadata and an associated key, wherein the associated metadata comprises data regarding cohorts associated with the corresponding datum, data regarding hierarchies associated with the corresponding datum, data regarding a corresponding source of the datum, and data regarding probabilities associated with integrity, reliability, and importance of each associated datum.

6. The computer implemented method of claim 1 wherein the first inference relates to a study on educational outcomes.

7. The computer implemented method of claim 1 wherein the second inference relates to a study on educational outcomes.

8. A non-transitory computer usable storage medium having computer usable program product encoded thereon for performing analysis on a plurality of data stored in a database, the computer program product including:

computer usable program code for generating a first cohort from the plurality of data;

computer usable program code for generating an optimal control cohort from the plurality of data, wherein generating is performed based on the first cohort and at least one constraint, and wherein a mathematical process is used to derive the optimal control cohort;

computer usable program code for generating a first inference based on a comparison of the first cohort to the optimal control cohort, wherein the first inference is absent from the database; and

storing the first inference;

computer usable program code for receiving an I^th query at the database regarding an I^th fact, wherein I is an integer reflecting how many times a recursion process has been conducted, wherein the I^th fact becomes a compound fact that includes multiple sub-facts on a subsequent iteration of the recursion process;

computer usable program code for establishing the I^th fact as a frame of reference for the I^th query based on the first cohort and based on the optimal control cohort;

computer usable program code for applying an I^th set of rules to the I^th query, wherein the I^th set of rules is determined for the I^th query according to a J^th set of rules, wherein J is equal to I−

1, wherein the I^th set of rules determines how the plurality of data are to be compared to the I^th fact, and wherein the first set of rules determines an I^th search space for the I^th query;

computer usable program code for executing the I^th query to generate a second inference, wherein the second inference is determined from comparing the plurality of data according to the I^th set of rules;

storing the second inference; and

evaluating a hypothesis within a research study based on the first cohort, the optimal control cohort, the first inference, and the second inference.

9. The computer usable medium of claim 8 wherein the second inference either validates the first inference or is based on the first inference.

10. The computer usable medium of claim 8 wherein the second inference further comprises one of a probability of the second inference being correct or a probability that the first inference is correct.

11. The computer usable medium of claim 8 wherein the first inference further comprises a probability of the first inference being correct.

12. The computer usable medium of claim 8 wherein the first inference and the second inference relate to a study on educational outcomes.

13. A data processing system comprising:

a bus;

at least one processor coupled to the bus;

a computer usable medium coupled to the bus, wherein the computer usable storage medium contains a set of instructions for performing analysis on a plurality of data stored in a database,

wherein the at least one processor is adapted to carry out the set of instructions to;

generate a first cohort from the plurality of data;

generate an optimal control cohort from the plurality of data, wherein generating is performed based on the first cohort and at least one constraint, and wherein a mathematical process is used to derive the optimal control cohort; and

generate a first inference based on a comparison of the first cohort to the optimal control cohort, wherein the first inference is absent from the database;

receive an I^th query at the database regarding an I^th fact, wherein I is an integer reflecting how many times a recursion process has been conducted, wherein the I^th fact becomes a compound fact that includes multiple sub-facts on a subsequent iteration of the recursion process;

establish the I^th fact as a frame of reference for the I^th query based on the first cohort and based on the optimal control cohort;

apply an I^th set of rules to the I^th query, wherein the I^th set of rules is determined for the I^th query according to a J^th set of rules, wherein J is equal to I−

1, wherein the I^th set of rules determines how the plurality of data are to be compared to the I^th fact, and wherein the first set of rules determines an I^th search space for the I^th query;

execute the I^th query to generate a second inference, wherein the second inference is determined from comparing the plurality of data according to the I^th set of rules;

store the second inference; and

evaluating a hypothesis within a research study based on the first cohort, the optimal control cohort, the first inference, and the second inference.

14. The data processing system of claim 13 wherein the second inference either validates the first inference or is based on the first inference.

15. The data processing system of claim 14 wherein the second inference further comprises one of a probability of the second inference being correct or a probability that the first inference is correct.

16. The data processing system of claim 13 wherein the first inference further comprises a probability of the first inference being correct.

17. The data processing system of claim 13 wherein the first inference and the second inference relate to a study on educational outcomes.