1. Permitting Inconsistent Data in Data Storage Lars Olson DEG Lab BYU Computer Science Dept. Supported by National Science Foundation Grant #0083127

2. Motivation Conflicting or inconsistent data –Multiple data sources –Actual values may not be possible to find –We want to keep all possible values Disjunctive databases –e.g. “OR-tables,” Imielinski & Vadaparty [1989] –CoNP-complete time complexity

3. Transitive Closure of Disjunctive Graphs Solving the CoNP-completeness problem [LYY95] Disjunctive graphPossible model Transitive closure of a: {a, d, e} a b c d e f a b c d e f

4. Using Disjunctive Graphs to Answer Queries ID#NameBirth Date Birth Place ID# (references Table Place) Marriage Date 1John Doe 12 Mar. 1840 or 12 Mar. 1841 1 or 2 15 Jun. 1869 or 16 Jun. 1869.:.:.:.:.:.:.:.:.:.: ID#CityState 1 Commerce Or Nauvoo Illinois 2QuincyIllinois.:.:.:.:.:.: Table Person: Table Place:

5. Using Disjunctive Graphs to Answer Queries  State (  ID=1 Person  Place) Person Place John Doe 12 Mar 1847 12 Mar 1848 16 Jun 186915 Jun 1869 1 1 2 Nauvoo Commerce Illinois Quincy City State City Birth Place Birth Date Marriage Date Name ID#

6. Birth Place City Birth Place Using Disjunctive Graphs to Answer Queries –Definitely known? –All possible values? –Most likely value?  City,State (  ID=1 Person  Place) Person Place 1 1 2 Nauvoo Commerce Illinois Quincy State City ID# …meaning what?

7. Using Disjunctive Graphs to Answer Queries  P1.Name, P2.Name (Person P1  P1.BirthDate=P2.BirthDate Person P2) Person P1John Doe 12 Mar 1847 12 Mar 1848 ID #1 Person P2 James Doe 13 Mar 1847 ID #2

8. 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.