Update Exchange with Mappings and Provenance

Todd J. Green Grigoris Karvounarakis Zachary G. Ives Val Tannen

University of Pennsylvania

VLDB 2007 Vienna, AustriaSeptember 26, 2007

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

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

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, ORCHESTRA, addresses these needs 4

• 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 ORCHESTRA, a Collaborative Data Sharing System (CDSS) [Ives+05]

DBMS

Queries, edits PUBLISH∆A+/−

∆B+/−∆C+/−

5

Peer A

Peer B

Peer C

∆A+/−

How ORCHESTRA addresses CDSS requirements

σ ∆Pc ∆Pfm Local curation

+

−Apply trust policiesusing

provenance

Translate through

mappings with provenance

Produce candidate updates

Apply final updates to

peer

Updates from other peers

Contributions of this paper

2 31

6

∆Pother r

Resolve conflicts

From one peer’s perspective:

[TaylorIves06]

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

• 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

∙

m1

m2

m3 (𝑚1) 𝐺ሺ𝑖,𝑐,𝑛ሻ→𝐵ሺ𝑖,𝑛ሻ (𝑚2) 𝐺ሺ𝑖,𝑐,𝑛ሻ→𝑈(𝑛,𝑐) (𝑚3) 𝐵ሺ𝑖,𝑐ሻ∧𝑈ሺ𝑛,𝑐ሻ→𝐵ሺ𝑖,𝑛ሻ

`

Incremental insertion

GB

(3, 2)(1, 3)U (2, 5)(3, 5, 2)

+

(1, 3, 3)+

(3, 5)+

m1

m1

m2

m3

m2

m3

9

(3, 3)+

This graph represents the provenance information that ORCHESTRA maintains

(𝑚1) 𝐺ሺ𝑖,𝑐,𝑛ሻ→𝐵ሺ𝑖,𝑛ሻ (𝑚2) 𝐺ሺ𝑖,𝑐,𝑛ሻ→𝑈(𝑛,𝑐) (𝑚3) 𝐵ሺ𝑖,𝑐ሻ∧𝑈ሺ𝑛,𝑐ሻ→𝐵ሺ𝑖,𝑛ሻ

Incremental deletion

GB (3, 5)(3, 2)(1, 3)

U (2, 5)(3, 3)m3

+ +(3, 5, 2)

+

(1, 3, 3)+

m1

m1

m2

m2

m3

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• 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

+

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

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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 m4 if n ≠ 2”

• Local curation: user can also manually accept/reject updates, or introduce new ones...

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Local curations• Extra tables for local insertions and deletions:

• Contribution: conforms to data exchange paradigm by using internal mappings with local insertions/deletions:

∆Pc ∆PfLocal curation

+

−Candidate updates

Final updates

(Mappings,trust policies,

etc.)

(𝑚2) 𝐺ሺ𝑥,𝑦,𝑧ሻ→𝑈(𝑥,𝑦)

(𝑚2′ ) 𝐺𝑓ሺ𝑥,𝑦,𝑧ሻ→𝑈𝑐(𝑥,𝑦) (𝑚𝑈+) 𝑈+ሺ𝑥,𝑦ሻ→𝑈𝑓(𝑥,𝑦) (𝑚𝑈−) 𝑈𝑐ሺ𝑥,𝑦ሻ∧¬𝑈−ሺ𝑥,𝑦ሻ→𝑈𝑓(𝑥,𝑦)

↦

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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++ code14

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

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Non-incrementalIncremental

DRed

Incremental deletion algorithm yields significant speedup

Parameters: 5 peers, full acyclic mappings, string data, 1 GB database16

Tim

e to

pro

paga

te d

eleti

ons

(sec

)

System scales to realistic #s of peers

0

20

40

60

80

2 5 10 20Number of peers

Tim

e (s

ec)

1% insertions (DB2)

10% insertions (DB2)

1% insertions (Tukwila)

10% insertions (Tukwila)

Parameters: full acyclic mappings, integer data, up to 1 GB database

10% insertions (DB2)

1% insertions (DB2)10% insertions (Tukwila)

1% insertions (Tukwila)

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Tim

e to

pro

paga

te in

serti

ons

(sec

)

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 settings18

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], …

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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 / data20

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

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Bioinformatics mappings example

(𝑚1) 𝐺ሺ𝑖,𝑐,𝑛ሻ→𝐵ሺ𝑖,𝑛ሻ (𝑚2) 𝐺ሺ𝑖,𝑐,𝑛ሻ→𝑈(𝑛,𝑐) (𝑚3) 𝐺ሺ𝑖,𝑐,𝑛ሻ→∃𝑐 𝑈ሺ𝑛,𝑐ሻ (𝑚4) 𝐵ሺ𝑖,𝑐ሻ∧𝑈ሺ𝑛,𝑐ሻ→𝐵ሺ𝑖,𝑛ሻ 23

Delta rules for insertions

• As in DRed [GuptaMumick95]:

(𝑚4) 𝐵ሺ𝑖,𝑐ሻ∧𝑈ሺ𝑛,𝑐ሻ→𝐵ሺ𝑖,𝑛ሻ (𝑚4′) 𝐵+(𝑖,𝑐) ∧𝑈ሺ𝑛,𝑐ሻ→𝐵+ሺ𝑖,𝑛ሻ (𝑚4′′) 𝐵𝜈ሺ𝑖,𝑐ሻ∧𝑈+ሺ𝑛,𝑐ሻ→𝐵+ሺ𝑖,𝑛ሻ (𝑚𝐵+) 𝐵+ሺ𝑖,𝑐ሻ→𝐵𝜈ሺ𝑖,𝑐ሻ (𝑚𝐵) 𝐵ሺ𝑖,𝑐ሻ→𝐵𝜈ሺ𝑖,𝑐ሻ

24