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

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Presentation on theme: "Update Exchange with Mappings and Provenance Todd J. Green Grigoris Karvounarakis Zachary G. Ives Val Tannen University of Pennsylvania VLDB 2007 Vienna,"— Presentation transcript:

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? 2

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... 3

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 data provenance – Support for incremental updates Changes to data, mappings, schemas... Our system, O RCHESTRA, addresses these needs 4

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

6 How O RCHESTRA addresses CDSS requirements σ PcPc PfPf m Local curation + Apply trust policies using provenance Translate through mappings with provenance Produce candidate updates Apply final updates to peer Updates from other peers Contributions of this paper 231 6 P other r Resolve conflicts From one peers perspective: [TaylorIves06]

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 7

8 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... Mappings and updates 8 G B U m1m1 m2m2 m3m3

9 ` Incremental insertion G B (3, 2) (1, 3) U (2, 5) (3, 5, 2) + (1, 3, 3) + (3, 5) + m1m1 m1m1 m2m2 m3m3 m2m2 m3m3 9 (3, 3) + This graph represents the provenance information that O RCHESTRA maintains

10 Incremental deletion G B (3, 5) (3, 2) (1, 3) U (2, 5) (3, 3) m3m3 + + (3, 5, 2) + (1, 3, 3) + m1m1 m1m1 m2m2 m2m2 m3m3 10 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 +

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 dont 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 11

12 Trust policies (not every update should be propagated) Updates can be filtered automatically 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 m 4 if n 2 Local curation: user can also manually accept/reject updates, or introduce new ones... 12

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

14 Prototype implementation Middleware layer on top of relational DBMS Mappings converted to datalog rules (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 14

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 15

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

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

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 18

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], … 19

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 20

21 Ongoing work at Penn Deploying O RCHESTRA 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 21


23 Bioinformatics mappings example 23

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

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