Efficiently Querying Contradictory and Uncertain Genealogical Data

1
Efficiently Querying Contradictory and Uncertain
Genealogical Data

• Lars E. Olson and David W. Embley
• DEG Lab
• BYU Computer Science Dept.

Supported by National Science Foundation Grant
0083127
2
Introduction

• Integrating data from multiple sources
• Some data just doesnt fit the data model
• Multiple data sources conflicting data
• Uncertain or imprecise data
• Data that violates constraints
• Sometimes its not possible to resolve the data
• PAF / Gedcom

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Disjunctive Databases
OR-tables, Imielinski and Vadaparty, 1989
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Shortcomings of OR-tables

• Cant correlate between possible values

• Answering queries in general is CoNP-complete
  (Imielinski Vadaparty)

5
Sub-relation Data Construct

• Solution store the correlated data in its own
  relation

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Disjunctive Database Problems

• How do we avoid the CoNP-completeness problem and
  answer queries efficiently?
• If more than one value is possible, which one is
  the most likely?
• Other questions to be solved
• Where are the constraint violations?
• How do we map sub-relations to physical storage?
• How do we efficiently update the database?

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Transitive Closure of Disjunctive Graphs
Solving the CoNP-completeness problem LYY95
Disjunctive graph
Possible interpretation
b
b
e
e
a
c
a
c
f
f
d
d
Transitive closure of a a, d, e
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Using Disjunctive Graphs to Answer Queries
Table Person
Table Place
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Using Disjunctive Graphs to Answer Queries
Person
John Doe
Name
12 Mar 1840
ID
Birth Date
1
12 Mar 1841
Place
Nauvoo
ID
City
Marriage Date
1
Birth Place
Commerce
16 Jun 1869
15 Jun 1869
State
Illinois
State
City
ID
2
Quincy
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Using Disjunctive Graphs to Answer Queries
Place
Nauvoo
ID
1
Person
Commerce
ID
State
1
Illinois
State
City
ID
2
Quincy
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Using Disjunctive Graphs to Answer Queries
meaning what?

• Definitely known?
• All possible values?
• Most likely value?

Place
Nauvoo
1.0
ID
City
1
Person
Commerce
0.2
ID
State
Birth Place
1
Illinois
State
0.8
City
ID
2
Quincy
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Using Disjunctive Graphs to Answer Queries
Person P1
John Doe
12 Mar 1840
Person P2
ID 1
ID 2
12 Mar 1841
James Doe
13 Mar 1840
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Limiting the Search Space

• In genealogy, most disjunctions are mutually
  independent
• Disjunctions that arent independent are limited
  to immediate family relations
• Build a relation containing all immediate family
  members

(Person P1 P1.parent P2.ID Person P2
P2.ID P3.parent Person P3)
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Limiting the Search Space

• Example constraints
• Each parent should be born before their children
• Each child should be born at least 9 months apart
  (except multiple births)

Person P1
Person P2
Person P3
ID 1
ID 1
ID 1
1.0
ID 2
ID 2
ID 2
1.0
ID 3
ID 3
ID 3
ID 4
ID 4
ID 4
parent
child parent-1
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Conclusions

• Genealogical data can be stored in a disjunctive
  database format.
• Many common queries can be computed in polynomial
  time.
• We can detect intractable queries and limit the
  search space required, usually enough to get
  polynomial time.