Update Exchange with Mappings and Provenance

1. Update Exchange with Mappings and Provenance Todd J. Green Grigoris Karvounarakis Zachary G. Ives Val Tannen University of Pennsylvania VLDB 2007 Vienna, Austria September 26, 2007

2. Adoption of data integration tools • Structured information is pervasive in the Internet age, as is the need to access and integrate it… • Need to collect, transform, aggregate information • Need to import related data into an existing database instance • … but after many years of research, few users of data integration tools • Why?

3. Not because the problem is too hard! • People are doing it anyway! (Just without help from DB research) • e.g., bioinformatics • Ad-hoc solutions (Perl scripts) developed for specific domains • e.g., at Penn, a large staff of programmers maintains the Genomics Unified Schema (GUS) • Point-to-point exchange between peers / collaborating sites • To be adopted, data integration tools need to offer significant additional value...

4. Needs unmet by data integration tools • Previous data integration tools do not offer: • Complete local control of data • Decide which data is import / integrated • Ability to modify any data, even data from elsewhere! • Support for different points of view • Disagreements about data, mappings, schemas... • Which sources are trusted / distrusted • Tracking of dataprovenance • Support for incremental updates • Changes to data, mappings, schemas... • Our system, Orchestra, addresses these needs

5. Requirements for Orchestra, a Collaborative Data Sharing System (CDSS) [Ives+05] • Give peers full control using local instance • Support different needs/ perspectives • Relate peers by mappings and trust policies • Support update exchange • Maintain dataprovenance ∆B+/− Peer B ∆C+/− Peer C Peer A DBMS ∆A+/− ∆A+/− Queries, edits PUBLISH

6. How Orchestra addresses CDSS requirements From one peer’s perspective: + Updates from other peers Local curation ∆Pother m σ r ∆Pc ∆Pf Translate through mappings with provenance Apply trust policies using provenance Apply final updates to peer Produce candidate updates Resolve conflicts − [TaylorIves06] 1 2 3 Contributions of this paper

7. Roadmap • Update exchange in a CDSS: • Schema mappings • Tracking of data provenance • Incremental propagation of updates • Provenance-based trust policies • Local curation via insertions / deletions • Prototype implementation • Experimental evaluation

8. Mappings and updates • CDSS setting: set of peers; set of declarative mappings (tgds) • Given: setting, base data, updates • Goal:local instance at each peer cf. data exchange paradigm [Fagin+03] • Universal solution yields the “certain answers” to queries • Can be computed using the chase • Our contribution: how to do it incrementally, with provenance... B m1 m3 ∙ G U m2

9. Incremental insertion + + m2 B G U (3, 5) m3 (3, 5, 2) m1 (2, 5) (3, 2) (1, 3, 3) m1 (3, 3) m3 (1, 3) + m2 + This graph represents the provenance information that Orchestra maintains `

10. Incremental deletion • Step 1: Use provenance graph to find derived tuples which can also be deleted • Step 2: Test other affected tuples for derivability, and delete any not derivable • Step 3: Repeat + + + m2 B G U (3, 5) m3 (3, 5, 2) m1 (2, 5) (3, 2) (1, 3, 3) m1 (3, 3) m3 (1, 3) + m2 +

11. Other approaches to incremental deletion • Many strategies (both research and commercial) for incremental deletions... • ... but we have to support recursion • Mappings can have cycles • Count-based algorithms don’t work (infinite counts) • Incremental maintenance for recursive datalog programs – DRed [GuptaMumick95] • DRed (“delete and re-derive”) computes superset of deletions, then corrects if needed • We use provenance to compute exact set of deletions

12. Trust policies (not every update should be propagated) • Updates can be filteredautomatically based on provenance and content • “Peer A distrusts any tuple U(i,n) if the data came from Peer B and n ≥ 3, and trusts any tuple from Peer C” • “Peer A distrusts any tuple U(i,n) that came from mapping m4 if n ≠ 2” • Local curation: user can also manually accept/reject updates, or introduce new ones...

13. Local curations • Extra tables for local insertions and deletions: • Contribution: conforms to data exchange paradigm by using internal mappings with local insertions/deletions: + (Mappings, trust policies, etc.) Local curation ∆Pc ∆Pf Final updates Candidate updates −

14. Prototype implementation • Middleware layer on top of relational DBMS • Mappings converted to datalogrules (as in Clio) • Separate tables for provenance info • Engine option 1: based on commercial DBMS (DB2) • Datalog fixpoints in Java and SQL (only linear recursion in DB2) • Labeled nulls supported via encoding scheme • Engine option 2: using in-house query engine (Tukwila) • BerkeleyDB for auxiliary storage and indexes • Custom operators for fixpoints, built-in labeled nulls • 30,000 lines of Java and C++ code

15. Experimental evaluation • DB2-based and Tukwila-based implementations • Workload typical of bioinformatics setting(at most 10s of peers, GBs of data) • Synthetic update workload sampled from SWISS-PROT biological data set • Randomly-generated schemas and mappings • Dual Xeon 5150 server, 8 GB RAM (2 GB for DB) • Variables: number of peers, complexity of mappings, volume of data, type of data, size of updates • Measured: time to join system, time to propagate updates, size of updated database

16. DRed Incremental Non-incremental Incremental deletion algorithm yields significant speedup Time to propagate deletions (sec) Parameters: 5 peers, full acyclic mappings, string data, 1 GB database

17. System scales to realistic #s of peers Time to propagate insertions (sec) 10% insertions (DB2) 1% insertions (DB2) 10% insertions (Tukwila) 1% insertions (Tukwila) Parameters: full acyclic mappings, integer data, up to 1 GB database

18. Contributions • Orchestra innovatively performs update exchange (not just mediated/federated query answering) • Tracks data provenance across a network of schema mappings • Supports provenance-based trust policies • Features algorithms for incremental propagation of updates • Solutions have been validated by experimental prototype for typical bioinformatics settings

19. Related work Peer data management systems Piazza [Halevy+03, 04], Hyperion [Kementsietsidis+04], [Bernstein+02], [Calvanese+04], ... Data exchange [Haas+99, Miller+00, Popa+02, Fagin+03], peer data exchange [Fuxman+05] Provenance / lineage [CuiWidom01], [Buneman+01], Trio [Widom+05], Spider [ChiticariuTan06], [Green+07], ... Incremental maintenance [GuptaMumick95], …

20. CDSS as a research platform: promising future directions • Ranking-based trust with provenance • Numeric weights and “accumulation of evidence” • More expressive mappings • e.g., “looking inside” attributes using regular expressions • Compact representations of provenance • Mixing virtual and materialized peers • Related to view selection problem • Supporting key dependencies / egds • Deletion propagation becomes challenging • Incorporating probabilistic mappings / data

21. Ongoing work at Penn • Deploying Orchestra in the real world • Pilot project with Penn Center for Bioinformatics • Bidirectional mappings • Propagating updates in both directions • Mapping evolution problem • Handling updates to mappings (not just data) • Fully distributed implementation • Using P2P database engine

23. Bioinformatics mappings example

24. Delta rules for insertions • As in DRed [GuptaMumick95]: