Data quality enhancement for smart grid applications

• US 8,412,735 B2
• Filed: 04/26/2010
• Issued: 04/02/2013
• 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, Mathur, Sanjay, Kurth, Scott W., Miller, John Y., Maneri, Erin Jennifer, Akred, John Mills
• Primary Examiner(s): Wu, Yicun
• Application Number:
  US12/767,354
• Publication Number:
  US 20110137876A1
• Time in Patent Office: 1,072 Days
• Field of Search: 707/797, 707/776, 706/47, 706/59
• US Class Current: 707/776
• 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:

retrieving data from an electricity smart grid;

encoding said data into a data modeling technique;

performing a data quality rules discovery process using a data set of said electricity smart grid and said data modeling technique encoded with said data to said data set to produce a plurality of data quality rules associated with said electricity smart grid, said data quality rules discovery process comprising;

applying each of a set of candidate conditional functional dependencies to a data segment having a predetermined length of data points of said data set; and

after said applying, if a candidate conditional functional dependency has a result signature that does not meet a predetermined expectation, refining that candidate conditional functional dependency by eliminating an attribute in that candidate conditional functional dependency that took on the most values throughout the 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 result signature comprises a list of result values.

5. The method of claim 1, wherein said predetermined expectation comprises an estimate of how many different data points a candidate conditional functional dependency would apply to.

6. The method of claim 5, wherein said predetermined expectation further comprises an error estimate of how many different data points would fail a candidate conditional functional dependency.

7. The method of claim 1, wherein said predetermined expectation comprises an error estimate of how man different data points would fail a candidate conditional functional dependency.

8. The method of claim 1, wherein said data comprising a time sensitive data relationship that exists within said electricity smart grid.

9. A method comprising:

retrieving data about a health care domain;

encoding said data into a data modeling technique;

performing a data quality rules discovery process using a data set of said health care domain and said data modeling technique encoded with said data to said data set to produce a plurality of data quality rules for said data set associated with said health care domain, said data quality rules discovery process comprising;

applying each of a set of candidate conditional functional dependencies to a data segment having a predetermined length of data points of said data set; and

after said applying, if a candidate conditional functional dependency has a result signature that does not meet a predetermined expectation, refining that candidate conditional functional dependency by eliminating an attribute in that candidate conditional functional dependency that took on the most values throughout the 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 result signature comprises a list of result values.

13. The method of claim 9, wherein said data comprising a time sensitive data relationship that exists within said health care domain.

14. The method of claim 9, wherein said predetermined expectation comprises an estimate of how many different data points a candidate conditional functional dependency would apply to.

15. The method of claim 14, wherein said predetermined expectation further comprises an error estimate of how many different data points would fail a candidate conditional functional dependency.

16. The method of claim 9, wherein said predetermined expectation comprises an error estimate of how may different data points would fail a candidate conditional functional dependency.

17. A system comprising:

an interface capable of receiving data about an electricity smart grid;

a first module coupled to said interface, said first module configured to encode said data into a data modeling technique; and

a discovery engine coupled to said first module, said discovery engine configured to perform a data quality rules discovery process using a data set of said electricity smart grid;

and said data modeling technique encoded with said data to said data set to produce a plurality of data quality rules associated with said electricity smart grid, said data quality rules discovery process comprising;

applying each of a set of candidate conditional functional dependencies to a data segment having a predetermined length of data points of said data set; and

after said applying, if a candidate conditional functional dependency has a result signature that does not meet a predetermined expectation, refining that candidate conditional functional dependency by eliminating an attribute in that candidate conditional functional dependency that took on the most values throughout the 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 17, wherein said predetermined expectation comprises an estimate of how many different data points candidate conditional functional dependency would apply to.

20. The system of claim 17, wherein said predetermined expectation comprises an error estimate of how many different data points would fail a candidate conditional functional dependency.