IP Library Granted Patent US 7,698,345
Granted Patent B2
US 7,698,345 · App. 10/576,800 · Granted Apr 13, 2010

Methods and apparatus for fusing databases

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Quick Facts
Patent No.
US 7,698,345
App. No.
10/576,800
Granted
Apr 13, 2010
Kind
B2
Abstract

Methods and apparatus for fusing multiple databases into a single database are disclosed. A disclosed method determines a ranking of a plurality of matching variables associated with first and second datasets and generates a hierarchical matching grid including a plurality of levels based on the ranking of the plurality of matching variables. The example method identifies first and second sets of match candidates from the first and second datasets based on successive levels of the hierarchical matching grid and fuses records in the first and second sets of match candidates based on probabilities associated with the records.

Claims (40)

1. A method of fusing first and second datasets, comprising:

determining an importance ranking of a plurality of variables associated with the first and second datasets;

generating a hierarchical matching grid including a plurality of levels based on the importance ranking of the plurality of variables, wherein each of the levels defines match criteria for satisfying a matching records condition by indicating which of the variables are to match;

identifying first and second sets of match candidates from the first and second datasets based on one of the plurality of levels of the hierarchical matching grid; and

fusing records in the first and second sets of match candidates based on probabilities associated with the records.

2. A method as defined in claim 1 , wherein determining the importance ranking of the plurality of variables includes ranking the plurality of variables based on a relative strength of a relationship between each of the variables and a respondent characteristic.

3. A method as defined in claim 1 , wherein generating the hierarchical matching grid including the plurality of levels based on the importance ranking of the plurality of variables includes generating a series of binary values for each level of the hierarchical grid so that each of a plurality of bit positions associated with the binary values uniquely corresponds to one of the plurality of variables, wherein the binary values define whether a corresponding one of the variables is to match in the first and second sets of match candidates.

4. A method as defined in claim 1 , wherein the generating the hierarchical matching grid including the plurality of levels based on the importance ranking of the plurality of variables includes generating the hierarchical matching grid to allow skewed matching on one or more of the variables.

5. A method as defined in claim 1 , wherein generating the hierarchical matching grid including the plurality of levels based on the importance ranking of the plurality of variables includes establishing a minimum matching level.

6. A method as defined in claim 1 , wherein identifying the first and second sets of match candidates from the first and second datasets based on the one of the plurality of levels of the hierarchical matching grid includes using match criteria from the one of the plurality of levels of the hierarchical matching grid to identify records in the second dataset that match records in the first dataset on ones of the plurality of variables defined by the match criteria.

7. A method as defined in claim 1 , wherein fusing the records in the first and second sets of match candidates based on the probabilities associated with the records includes establishing the probabilities based on weights associated with records from at least one of the first and second sets of match candidates.

8. A method as defined in claim 1 , further comprising:

comparing a first sum of weights associated with the first set of match candidates with a second sum of weights associated with the second set of match candidates;

identifying one of the first and second sets of match candidates as overweight based on the comparison of the first and second sums of weights; and

trimming records of one of the first and second sets of match candidates identified as overweight prior to fusing the records in the first and second sets of match candidates.

9. A system for fusing first and second datasets, comprising:

a memory; and

a processor coupled to the memory and configured to:

determine an importance ranking of a plurality of variables associated with the first and second datasets;

generate a hierarchical matching grid including a plurality of levels based on the importance ranking of the plurality of variables, wherein each of the levels defines match criteria for satisfying a matching records condition by indicating which of the variables are to match;

identify first and second sets of match candidates from the first and second datasets based on one of the plurality of levels of the hierarchical matching grid; and

fuse records in the first and second sets of match candidates based on probabilities associated with the records.

10. A system as defined in claim 9 , wherein the processor is configured to determine the importance ranking of the plurality of variables by ranking the plurality of variables based on a relative strength of a relationship between each of the variables and a respondent characteristic.

11. A system as defined in claim 9 , wherein the processor is configured to generate the hierarchical matching grid including the plurality of levels based on the importance ranking of the plurality of variables by generating a series of binary values for each level of the hierarchical grid so that each of a plurality of bit positions associated with the binary values uniquely corresponds to one of the plurality of variables, wherein the binary values define whether a corresponding one of the variables is to match in the first and second sets of match candidates.

12. A system as defined in claim 9 , wherein the processor is configured to generate the hierarchical matching grid having the plurality of levels based on the importance ranking of the plurality of variables by generating the hierarchical matching grid to allow skewed matching on one or more of the variables.

13. A system as defined in claim 9 , wherein the processor is configured to generate the hierarchical matching grid including the plurality of levels based on the importance ranking of the plurality of variables by establishing a minimum matching level.

14. A system as defined in claim 9 , wherein the processor is configured to identify the first and second sets of match candidates from the first and second datasets based on the one of the plurality of levels of the hierarchical matching grid by using match criteria from the one of the plurality of levels of the hierarchical matching grid to identify records in the second dataset that match records in the first dataset on ones of the plurality of variables defined by the match criteria.

15. A system as defined in claim 9 , wherein the processor is configured to fuse the records in the first and second sets of match candidates based on the probabilities associated with the records by establishing the probabilities based on weights associated with records from at least one of the first and second sets of match candidates.

16. A system as defined in claim 9 , wherein the processor is configured to:

compare a first sum of weights associated with the first set of match candidates with a second sum of weights associated with the second set of match candidates;

identify one of the first and second sets of match candidates as overweight based on the comparison of the first and second sums of weights; and

trim records of the one of the first and second sets of match candidates identified as overweight prior to fusing the records in the first and second sets of match candidates.

17. A machine readable medium having instructions stored thereon that, when executed, cause a machine to:

determine an importance ranking of a plurality of variables associated with first and second datasets;

generate a hierarchical matching grid including a plurality of levels based on the importance ranking of the plurality of variables, wherein each of the levels defines match criteria for satisfying a matching records condition by indicating which of the variables are to match;

identify first and second sets of match candidates from the first and second datasets based on one of the plurality of levels of the hierarchical matching grid; and

fuse records in the first and second sets of match candidates based on probabilities associated with the records.

18. A machine readable medium as defined in claim 17 having instructions stored thereon that, when executed, cause the machine to determine the importance ranking of the plurality of variables by ranking the plurality of variables based on a relative strength of a relationship between each of the variables and a respondent characteristic.

19. A machine readable medium as defined in claim 17 having instructions stored thereon that, when executed, cause the machine to generate the hierarchical matching grid including the plurality of levels based on the importance ranking of the plurality of variables by generating a series of binary values for each level of the hierarchical grid so that each of a plurality of bit positions associated with the binary values uniquely corresponds to one of the plurality of variables, wherein the binary values define whether a corresponding one of the variables is to match in the first and second sets of match candidates.

20. A machine readable medium as defined in claim 17 having instructions stored thereon that, when executed, cause the machine to generate the hierarchical matching grid including the plurality of levels based on the importance ranking of the plurality of variables by generating the hierarchical matching grid to allow skewed matching on one or more of the variables.

Assignments (11)
RELEASE (REEL 053473 / FRAME 0001) Recorded May 11, 2023
From: CITIBANK, N.A.
To: A. C. NIELSEN COMPANY, LLC; EXELATE, INC.; GRACENOTE, INC.; GRACENOTE MEDIA SERVICES, LLC; THE NIELSEN COMPANY (US), LLC; NETRATINGS, LLC
Reel/Frame 063603/0001 →
RELEASE (REEL 054066 / FRAME 0064) Recorded May 11, 2023
From: CITIBANK, N.A.
To: A. C. NIELSEN COMPANY, LLC; EXELATE, INC.; GRACENOTE, INC.; GRACENOTE MEDIA SERVICES, LLC; THE NIELSEN COMPANY (US), LLC; NETRATINGS, LLC
Reel/Frame 063605/0001 →
SECURITY INTEREST Recorded May 8, 2023
From: GRACENOTE DIGITAL VENTURES, LLC; GRACENOTE MEDIA SERVICES, LLC; GRACENOTE, INC.; TNC (US) HOLDINGS, INC.; THE NIELSEN COMPANY (US), LLC
To: ARES CAPITAL CORPORATION
Reel/Frame 063574/0632 →
SECURITY INTEREST Recorded Apr 28, 2023
From: GRACENOTE DIGITAL VENTURES, LLC; GRACENOTE MEDIA SERVICES, LLC; GRACENOTE, INC.; TNC (US) HOLDINGS, INC.; THE NIELSEN COMPANY (US), LLC
To: CITIBANK, N.A.
Reel/Frame 063561/0381 →
SECURITY AGREEMENT Recorded Jan 31, 2023
From: GRACENOTE DIGITAL VENTURES, LLC; GRACENOTE MEDIA SERVICES, LLC; GRACENOTE, INC.; TNC (US) HOLDINGS, INC.; THE NIELSEN COMPANY (US), LLC
To: BANK OF AMERICA, N.A.
Reel/Frame 063560/0547 →
RELEASE (REEL 037172 / FRAME 0415) Recorded Oct 13, 2022
From: CITIBANK, N.A.
To: THE NIELSEN COMPANY (US), LLC
Reel/Frame 061750/0221 →
CORRECTIVE ASSIGNMENT TO CORRECT THE PATENTS LISTED ON SCHEDULE 1 RECORDED ON 6-9-2020 PREVIOUSLY RECORDED ON REEL 053473 FRAME 0001. ASSIGNOR(S) HEREBY CONFIRMS THE SUPPLEMENTAL IP SECURITY AGREEMENT. Recorded Oct 7, 2020
From: A.C. NIELSEN (ARGENTINA) S.A.; A.C. NIELSEN COMPANY, LLC; ACN HOLDINGS INC.; ACNIELSEN CORPORATION; ACNIELSEN ERATINGS.COM; AFFINNOVA, INC.; ART HOLDING, L.L.C.; ATHENIAN LEASING CORPORATION; CZT/ACN TRADEMARKS, L.L.C.; EXELATE, INC.; GRACENOTE, INC.; GRACENOTE DIGITAL VENTURES, LLC; GRACENOTE MEDIA SERVICES, LLC; NETRATINGS, LLC; NIELSEN AUDIO, INC.; NIELSEN CONSUMER INSIGHTS, INC.; NIELSEN CONSUMER NEUROSCIENCE, INC.; NIELSEN FINANCE CO.; NIELSEN FINANCE LLC; NIELSEN INTERNATIONAL HOLDINGS, INC.; NIELSEN MOBILE, LLC; NMR INVESTING I, INC.; TCG DIVESTITURE INC.; TNC (US) HOLDINGS, INC.; THE NIELSEN COMPANY (US), LLC; VIZU CORPORATION; VNU MARKETING INFORMATION, INC.; NMR LICENSING ASSOCIATES, L.P.; NIELSEN HOLDING AND FINANCE B.V.; THE NIELSEN COMPANY B.V.; VNU INTERNATIONAL B.V.
To: CITIBANK, N.A
Reel/Frame 054066/0064 →
SUPPLEMENTAL SECURITY AGREEMENT Recorded Jun 9, 2020
From: A. C. NIELSEN COMPANY, LLC; ACN HOLDINGS INC.; ACNIELSEN CORPORATION; ACNIELSEN ERATINGS.COM; AFFINNOVA, INC.; ART HOLDING, L.L.C.; ATHENIAN LEASING CORPORATION; CZT/ACN TRADEMARKS, L.L.C.; EXELATE, INC.; GRACENOTE, INC.; GRACENOTE DIGITAL VENTURES, LLC; GRACENOTE MEDIA SERVICES, LLC; NETRATINGS, LLC; NIELSEN AUDIO, INC.; NIELSEN CONSUMER INSIGHTS, INC.; NIELSEN CONSUMER NEUROSCIENCE, INC.; NIELSEN FINANCE CO.; NIELSEN FINANCE LLC; NIELSEN INTERNATIONAL HOLDINGS, INC.; NIELSEN MOBILE, LLC; NIELSEN UK FINANCE I, LLC; NMR INVESTING I, INC.; TCG DIVESTITURE INC.; TNC (US) HOLDINGS, INC.; THE NIELSEN COMPANY (US), LLC; VIZU CORPORATION; VNU MARKETING INFORMATION, INC.; NMR LICENSING ASSOCIATES, L.P.; NIELSEN HOLDING AND FINANCE B.V.; THE NIELSEN COMPANY B.V.; VNU INTERNATIONAL B.V.
To: CITIBANK, N.A.
Reel/Frame 053473/0001 →
SUPPLEMENTAL IP SECURITY AGREEMENT Recorded Nov 30, 2015
From: THE NIELSEN COMPANY ((US), LLC
To: CITIBANK, N.A., AS COLLATERAL AGENT FOR THE FIRST LIEN SECURED PARTIES
Reel/Frame 037172/0415 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 13, 2009
From: NIELSEN MEDIA RESEARCH, LLC (FORMERLY KNOWN AS NIELSEN MEDIA RESEARCH, INC.), A DELAWARE LIMITED LIABILITY COMPANY
To: NIELSEN COMPANY (US), LLC, THE, A DELAWARE LIMITED LIABILITY COMPANY
Reel/Frame 023084/0516 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 21, 2006
From: SAMSON, JEROME; MCMILLAN, FRANCIS G.
To: NIELSEN MEDIA RESEARCH, INC.
Reel/Frame 018331/0780 →