DATA QUALITY ENHANCEMENT FOR SMART GRID APPLICATIONS

• US 20110137876A1
• Filed: 04/26/2010
• Published: 06/09/2011
• Est. Priority Date: 12/07/2009
• Status: Active Grant

• Current Assignee: Accenture Global Services Limited (Accenture PLC)
• Original Assignee: Accenture Global Services Limited (Accenture PLC)
• Inventors: YEH, Peter Zei-Chan, KURTH, Scott W., MANERI, Erin Jennifer, MATHUR, Sanjay, AKRED, John Mills
• Granted Patent: US 8,412,735 B2
• Time in Patent Office: Days
• US Class Current: 707/694
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 50/06 Energy or water supply

Abstract

A method, in one embodiment, can include encoding knowledge about a topic domain into a data modeling technique. Additionally, a set of candidate conditional functional dependencies can be generated based on a data set of the topic domain. Moreover, the set of candidate conditional functional dependencies and the data modeling technique encoded with the topic domain knowledge can be applied to the data set to obtain a plurality of data quality rules for the data set.

Claims

1. A method comprising:

encoding knowledge about a topic domain into a data modeling technique;

generating a set of candidate conditional functional dependencies based on a data set of said topic domain; and

applying said set of candidate conditional functional dependencies and said data modeling technique encoded with said topic domain knowledge to said data set to obtain a plurality of data quality rules for said data set.

2. The method of claim 1, further comprising:

performing an analysis on said plurality of data quality rules to generate a derivative data quality rule for said data set.

3. The method of claim 2, wherein said performing an analysis comprises aggregating a first data quality rule and a second data quality rule of said plurality of data quality rules.

4. The method of claim 1, wherein said knowledge about said topic domain comprising a relationship that exists within an electricity utility smart grid.

5. The method of claim 1, wherein said knowledge about said topic domain comprising a valid range of relationship that exists within an electricity utility smart grid.

6. The method of claim 1, wherein said knowledge about said topic domain comprising a valid operating range that exists within an electricity utility smart grid.

7. The method of claim 1, wherein said knowledge about said topic domain comprising a data process dependency that exists within an electricity utility smart grid.

8. The method of claim 1, wherein said knowledge about said topic domain comprising a time sensitive data relationships that exist within an electricity utility smart grid.

9. A method comprising:

generating a set of candidate conditional functional dependencies based on a data set of a topic domain;

applying said set of candidate conditional functional dependencies to said data set to obtain a plurality of data quality rules for said data set; and

performing an analysis on said plurality of data quality rules to generate a derivative data quality rule for said data set.

10. The method of claim 9, wherein said performing an analysis comprises aggregating a first data quality rule and a second data quality rule of said plurality of data quality rules.

11. The method of claim 9, wherein said performing an analysis comprises performing a transformation on a data quality rule of said plurality of data quality rules.

12. The method of claim 9, wherein said performing an analysis comprises performing a transformation on a first data quality rule and a second data quality rule of said plurality of data quality rules.

13. The method of claim 9, wherein said applying further comprising applying said set of candidate conditional functional dependencies and a data modeling technique encoded with a topic domain knowledge to said data set to obtain said plurality of data quality rules for said data set, wherein said topic domain comprises a health care domain.

14. The method of claim 13, wherein said topic domain knowledge comprising a relationship that exists within said health care domain.

15. The method of claim 13, wherein said topic domain knowledge comprising a valid range of relationship that exists within said health care domain.

16. The method of claim 13, wherein said topic domain knowledge comprising a valid operating range that exists within said health care domain.

17. A system comprising:

a first module configured to encode knowledge about a topic domain into a data modeling technique; and

a discovery engine coupled to said first module, said engine configured to generate a set of candidate conditional functional dependencies based on a data set of said topic domain;

wherein said discovery engine configured to apply said set of candidate conditional functional dependencies and said data modeling technique encoded with said topic domain knowledge to said data set to obtain a plurality of data quality rules for said data set.

18. The system of claim 17, further comprising:

a second module coupled to receive said plurality of data quality rules, said second module configured to perform an analysis on said plurality of data quality rules to generate a derivative data quality rule for said data set.

19. The system of claim 18, wherein said analysis comprises aggregating a first data quality rule and a second data quality rule of said plurality of data quality rules.

20. The system of claim 17, wherein said knowledge about said topic domain comprising a relationship that exists within said topic domain.