1. Arkansas Research Center
It’s about capacity and creating links. While this report demonstrates capacity at the state level, institutions have their own needs as well. With changes to FERPA, we are able to provide more feedback to institutions. We could provide an outcomes report to institutions. Our only request is that it be standardized for all institutions. It would also be nice to provide a detailed feedback report for high schools. What would be helpful for high schools to know about the college experience?
arc.arkansas.gov

2. You are the identity manager…
MARIA
WILSON HIGH SCHOOL
CASTILLO-DELGADO
MARIA
WILSON HIGH SCHOOL
CASTILLO-DELG

3. You are the identity manager…
MARIA D
WILSON HS
CASTILLO-DELGADO
MARIA C
WILSON HS
CASTILLO-DELG

4. You are the identity manager…
MARIA D
WILSON HS
CASTILLO-DELGADO
DOB: 11/05/1995
MARIA C
WILSON HS
CASTILLO-DELG
DOB: 9/24/1994

5. Identity Resolution Problems (K12)
There are ~55,000 unique first names among students in Arkansas and ~40,000 last names.
Approximately 20% of Arkansas students share both the same first and last name with another student.
Student Count
First Name
Last Name 64
JOSHUA
SMITH 56
ASHLEY 52
JESSICA 48
JUSTIN 37
JONES 31
WILLIAMS 30
JOHNSON 27
BROWN

6. Identity Resolution (K12)
There are 4,026 students in Arkansas that share an SSN with at least one other student in the state.
Between August and January, 874 student transfers to other schools resulted in an SSN change.
Between August and January, an additional 1,018 students changed their SSN—we have records for only 300 of these changes.
There are ~17,000 students in Arkansas with a “900” SSN

7. Identity Resolution (Workforce)
~55,000,000 records for 10 years, 2,938,718 unique SSNs, no DOBs, inconsistent naming standards.
7,865 SSNs used by two or more people, for a total of 18,278 different individuals. Those would be combined incomes and treated as the same person if SSN was the primary key.
The same person has two or more SSNs (because of a typo/transposition) 13,373 times. There would be 13,373 additional (non-existent) people with separate incomes if SSN was the primary key.

8. Problem Statement There are known knowns; there are things we
know we know. We also know there are known
unknowns; that is to say, we know there are
some things we do not know.
But there are also unknown unknowns –
the ones we don’t know we don’t know.
D. Rumsfeld 2/12/2002

9. Record Linking: Merge/Purge
File A
File B
Your knowledge is limited to what’s
in these two files ONLY

10. Knowledge Base Approach
All known representations are stored to facilitate matching in the future and possibly resolve past matching errors.
Bob Smith, Conway High School
Robert Smith, Acxiom
Knowledge Base
Cluster
Representation
KB5765
Bob Smith, CHS
Robert Smith, Acxiom
Bob Smith, UCA
Bob Smith, UCA

11. Knowledge Base Steps Do all 5 values match exactly (E5)? No.
Do 4 values match (E4)? No.
Do 3 values match (E3)? No/*.
Do 2 values match (E2)? Yes.
Are they enough for confidence? No.
CONCLUSION: NO MATCH
THEY ARE KEPT SEPARATE IN KNOWLEDGE BASE
MARIA D
WILSON HS
CASTILLO-DELGADO
DOB: 11/05/1995
* Last name is a special case
MARIA C
WILSON HS
CASTILLO-DELG
DOB: 9/24/1994

12. Exact v. Fuzzy (Deterministic v. Probabilistic)
Exact matching drives the majority of identity resolution (Pareto Rule—80% is easy) Probabilistic algorithms – Soundex, QTR, Edit Distance, Neural Networks (Pareto Rule—20% require 80% of effort) You want a system that does what YOU, a human, would do

13. Possible Matching Errors
False Positives (Over-consolidation) False Negatives (Under-consolidation)

14. Identity Management
Over-consolidation – split the records apart and update all affected systems Under-consolidation – bring the records together and update all affected systems

15. Identity Resolution Type
Actual Results
Identity Resolution Type
Records
Percent E5
52,287
51.8%
E3notLEAnotS
32,370
32.0%
E3notLEAnotF
3,742
3.7%
E2notLEAnotSghF
2,100
2.1%
E2notLEAnotSghD
2,083
E3notLEAnotL
1,826
1.8%
E3notLEAnotD
1,731
1.7%
E2notLEAnotSghL
948
0.9%
E2notDghFnonvalSSN
760
0.8%
E3uniqueFLnotSyrDornull
611
0.6%
E4notLEA
539
0.5%
E2notLEAnotFL
289
0.3%
E2notSuniqLLEA4yrDOB
199
0.2%
E2notLEAnotDF
174
5 additional types
358
0.4%
TOTAL identified students
100,017
99.0%
unknown identities
1,007
1.0%
TOTAL
101,024
100.0%
100,000+ records from Explore and Plan exams, 2008 and Match rate, 99%.

16. Examples: 1% Not Matched
First name
Last name
SSN
Date of Birth
A H LE
A ON TH P M
<provided>
AVER YAAAAAAA
CLA AAAAAAAAAAAAAAAAAAAA
<none>
YUMM ON UE
CA R TWRIGHT
Nov. 5, 1959
XYLON
SILVER
100% is not realistic – 99% is realistic, but what’s important is the ability to manage problems as they arise

17. Oyster Development
1st Generation – built in Access, automation of queries/functions creating Knowledge Base. (started in 2009) – shared with W. Virginia Data was longitudinal, but sourced from K-12 exclusively 2009 IES Grant included funding for research with UALR – this work became “Oyster” Oyster also funded with 2009 ARRA Grant

18. What is Oyster?
Open-System Entity Resolution Not database-driven, pure XML Java source code (unicode support) Matching by R-Swoosh methodologies but could be adapted to Fellegi-Sunter

19. Timeline 1stGenIDs (Access) K.I.M. (SQL/PHP) Oyster (Java/XML) 1.1 1.2
1.3
1.4
1.5
2.0
2009
1.x
2.x
3.0
3.1
3.2
2010
2011
K.I.M.
(SQL/PHP)
GUI
2.0
2012

20. Oyster and KIM
Oyster: Thorough documentation and GUI KIM: Little documentation and no GUI Oyster: Has not been benchmarked since memory fix KIM: Throughput is 1 – 5 million records an hour, depending on the data and use Oyster: R-Swoosh KIM: Fellegi-Sunter Both deal with over- and under-consolidations

21. Fellegi-Sunter: Record-based matching R-Swoosh: Attribute-based matching
Already determined to be the same individual Neil Gibson, Neal Gibson, Neal Gibbs, What about: Neal Gibson, (all correct) Neil Gibbs, (none correct)

22. Oyster XML Run Script

23. Oyster XML Index

24. Oyster Input GUI

25. Oyster Run Script GUI

26. TrustEd:
Knowledgebase Identity Management (KIM) TrustEd Identifier Management (TIM)
TIM
Identifier Management
KIM
TrustED
De-identified Research Databases
Identity Resolution

27. TrustEd: KIM & TIM TIM Identifier Management TrustED
Research Data
RecID
PII SourceID
RecID
TIM
Identifier Management
KIM
TrustED
De-identified Research Databases
Identity Resolution

28. TrustEd: KIM & TIM TIM Identifier Management TrustED
PII KBID
KIMID
TIM
Identifier Management
KIM
TrustED
KIMID
RecID
De-identified Research Databases
Identity Resolution

29. TrustEd: KIM & TIM TIM Identifier Management TrustED
KIMID SourceID
RecID TIMID
Research Data
AgencyID
TIM
Identifier Management
KIM
TrustED
De-identified Research Databases
Identity Resolution

30. TrustEd: KIM & TIM TIM Identifier Management TrustED
RecID SourceID TIMID: Management
Agency Crosswalks
Research Data
PII
TIM
Identifier Management
KIM
TrustED
De-identified Research Databases
Identity Resolution

31. TrustEd Results
TrustEd validates the request based on sharing rules and translates the requesting agency’s local IDs to that of the other agency. The results are then returned to the requesting agency without the use of personally identifiable information.
ADHE
DWS
What are the salaries for these individuals?
HE0236
HE0651
HE1327
WF4297 Salary: $36,000
WF8516 Salary: $28,000
WF3508 Salary: $41,000
TIM
HE0236 ↔ WF4297
HE0651 ↔ WF8516
HE1327 ↔ WF3508
Brokered Result 1
HE0236 Salary: $36,000
HE0651 Salary : $28,000
HE1327 Salary : $41,000
Brokered Result 2
Salary : $41,000
Salary : $36,000
Salary : $28,000
Brokered Result 3
Average Salary : $35,000

32. Examples of Multi-agency Research
UAMS nICU – 1998 births to 2011 K12 assessments
Pre-K programs to K12 preparedness/assessments
K12 indicators for Higher Ed on-time graduation
Employment outcomes – Higher Ed to Workforce
Special Ed outcomes – K12, Higher Ed, Workforce, and Dept. of Corrections

33. Questions?
Oyster Information – UALR KIM Information – ARC