by Giorgos Flouris (FORTH), presented at the 3rd PRELIDA Consolidation and Dissemination Workshop, Riva, Italy, October, 17, 2014. More information about the workshop at: prelida.eu

1. Evolution Management for Preservation
PRELIDA Consolidation Workshop 17.10.2014
Giorgos Flouris (FORTH)
fgeo@ics.forth.gr

2. Evolution Management Problem
Preservation ↔ Evolution

3. Change Detection
• Change detection for evolution management
– Identifying changes between versions
• Challenges (in DIACHRON)
1. Diverse data models
2. Dynamic datasets
3. Recoverable versions
4. Changes as first-class citizens
5. Cross-snapshot queries

4. Evolution in DIACHRON
Pilot dataset DIACHRON
Version1
Pilot dataset DIACHRON
Version2

5. Change Types: Motivation
What a naïve diff will report
Add (Rec, diachron:subject, EFO_001927)
Add (Rec, diachron:hasRecordAttribute, rAtt1)
Add (rAtt1, diachron:predicate, rdfs:subClassOf)
Add (rAtt1, diachron:object, ObsoleteClass)
What the pilot expects
Add_SuperClass (EFO_001927, ObsoleteClass)

6. Change Hierarchy: Low-level (1/3)
• Low-level changes
– DIACHRON model, for internal use
– Fixed:
Add, Delete
– Just additions and deletions of triples
– Simple set difference

7. Change Hierarchy: Simple (2/3)
• Pilot terminology:
– Add_SuperClass
Add_Dimension
• Fixed, pre-defined
• Comprising of low-level changes
• Partitioning is perfect
– Complete and unambiguous

8. Change Hierarchy: Complex (3/3)
• Pilot terminology:
– Add_Synonym, Mark_As_Obsolete
• Totally custom, pilot-specific (defined at run-time)

9. Using Changes for
Evolution Management
• DIACHRON data model contains all versions
• Detection based on SPARQL queries
– Provided at deployment time (for simple)
– Generated at creation time (for complex)
• Recoverability
– Allows moving back and forth between versions

10. Representation Requirements
• Interesting queries
– Return the simple changes that dataset X underwent
between versions V1 and V2
– Return the changes that resource X underwent in the first
semester of 2014
– Give me all resources of type X that underwent change Y
– Return all countries for which the unemployment rate of
their capital city increased at a rate higher than the
average increase of the country as a whole, between
versions V1 and V2
• Access to both the changes and the data is required
– Changes are first-class citizens
– Allowing preservation

11. DIACHRON
Data
Changes Ontology
C1
Add_SuperClass
V1
V2
asc_p1
asc_p2
Simple_Change
Change
prov:Activity
Data level
Schema level
EFO_001927
ObsoleteClass
old_version
new_version
diachron:Entity
Add_Synonym
Complex_Change
… …

12. Conclusion
• Main DIACHRON message
– (Linked) data preservation is related to evolution management
• DIACHRON challenges
1. Diverse data models
2. Dynamic datasets
3. Recoverable versions
4. Changes as first-class citizens
5. Cross-snapshot queries
• Solutions
– DIACHRON data model (#1)
– Appropriate change definition and detection (#2, #3)
– Changes and data represented at the same level (#4, #5)