Evolution Management for Preservation PRELIDA Consolidation Workshop Giorgos Flouris (FORTH)

Evolution Management Problem Preservation ↔ Evolution

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

Evolution in DIACHRON Pilot datasetDIACHRON Version 1 Pilot datasetDIACHRON Version 2

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)

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

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

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

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

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

Data Changes Ontology C1C1 Add_SuperClass V1V1 V2V2 asc_p1 asc_p2 Simple_Change Change prov:Activity Data level Schema level EFO_ ObsoleteClass old_version new_version diachron:Entity Add_Synonym Complex_Change … … … …

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)