An Annotation Management System for Relational Databases

Laura ChiticariuUniversity of California, Santa Cruz

Joint work with Deepavali Bhagwat, Wang-Chiew Tan, Gaurav Vijayvargiya

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A system that is able to propagate meta-data along with the data as the data is being moved around

Main motivation To trace the provenance and flow of data

Many other uses

Annotation Management System

transformationa2a1 a2

a1

b2

b1

b3

b2b1 b3a1 a2

a3

transformation step: a query, an ETL rule, etc.

transformation

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Peacock Alley

Bull & Bear

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Zip

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Our Vision

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Other Applications

Keep information that cannot be otherwise stored in the current database design

Highlight wrong data Erroneous data may be copied around but the

comment that it is wrong goes along with it

Security and quality metric Annotate security or quality levels of data items

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Some Related Work

Idea is not new though propagation of annotations was never explicitly stated as provenance-based: Wang & Madnick [VLDB 90], Lee, Bressan & Madnick [WIDM 98], Bernstein & Bergstraesser [IEEE Data Eng. 99]

Superimposed Information. Maier and Delcambre [WebDB 99]

Annotations of Web documents Annotations on genomic sequences Why-Provenance

Cui, Widom, & Wiener [CWW00]

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Outline

pSQL queries Semantics

CUSTOM propagation schemeDEFAULT propagation schemeDEFAULT-ALL propagation scheme

ImplementationSystem architectureExperimental results

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pSQL – an extension of SQL

A pSQL fragment:

SELECT DISTINCT selectlist

FROM fromlist

WHERE wherelist

PROPAGATE DEFAULT

| DEFAULT-ALL

| r1.A1 TO B1, …, rn.An TO Bn

A pSQL query is a union of pSQL fragments

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The CUSTOM Scheme

SELECT DISTINCT AFROM R rPROPAGATE r.A TO A

UNION

SELECT DISTINCT A FROM R rPROPAGATE r.B TO A

A B

1 2

2 3

3 5

R

a

b

c

h

1

2

3

Result

Propagate annotations according to user specification

1

2

3

Result1

a

b

1

2

3

Result2

c

h

annotationUNION

a

b

c

h

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The DEFAULT Scheme

Propagate annotations according to where data is copied from

r.B TO B

s.B TO B

A B

1 2

2 3

3 5

R

a

b

c

h

2

3

4

5

Result

c

e

f

h

A B

4 2

5 3

6 4

S

d

f

g

e

g

SELECT DISTINCT BFROM R r PROPAGATE DEFAULT

UNION

SELECT DISTINCT BFROM S sPROPAGATE DEFAULT

natural semantics for tracing the provenance of data

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SELECT DISTINCT r.A, r.B, s.CFROM R r, S sWHERE r.B = s.BPROPAGATE DEFAULT

versus

SELECT DISTINCT *FROM R NATURAL JOIN SPROPAGATE DEFAULT

=a

Annotation Propagation under the DEFAULT Scheme

A B

1 2

R

a

1 2 3

Ans1

B C

2 3

S

b

a

1 2 3

Ans2

a b

equivalent queries,

but different

annotated output

Q1:

Q2:

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The DEFAULT-ALL scheme

Propagate annotations according to where data is copied from according to all equivalent formulations of the given query

User Query Q:

Compute the results of Q on a database D – idea: E(Q) denotes the set of all queries that are equivalent to Q

(more precisely, (*)). Execute each query in E(Q) on the database D under the

DEFAULT scheme, then combine the results under a.

SELECT DISTINCT r.A, s.B, s.CFROM R r, S sWHERE r.B = s.BPROPAGATE DEFAULT-ALL

(*) the SQL query corresponding to Q

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Computing the results of a DEFAULT-ALL query

Question:Given a pSQL query Q with DEFAULT-ALL propagation scheme and a database D, can we compute the result of Q(D)?

Problem: There are infinitely many queries in E(Q). It is therefore impossible to execute every query in E(Q) in order to obtain the result of Q(D).

Solution: Compute a finite basis of E(Q) first

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A Query Basis of Q

A query basis of Q, denoted as B(Q), is a finite set of pSQL queries (with default propagation scheme) such that:

Ua q(D) =a Ua q(D)

Given B(Q), we can execute each query in B(Q) and combine the results to obtain the result of Q(D).

Question: Given Q, does B(Q) always exist and how can we compute B(Q)?

qB(Q) qE(Q)

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Generating a Query Basis of Q Given R(A,B) and S(B,C) User query Q:

Representative Query Q0 :

Propagations under the default propagation scheme

Additional propagation due to the equality

r.B = s.B

Ans(x,y,z) :- R(x,y), S(y,z).

The representative query propagates annotations according to where data is copied from or equivalently copied from.

SELECT DISTINCT r.A, s.B, s.CFROM R r, S sWHERE r.B = s.BPROPAGATE DEFAULT-ALL

SELECT DISTINCT r.A, s.B, s.CFROM R r, S sWHERE r.B = s.BPROPAGATE r.A TO A, s.B TO B, s.C TO C, r.B TO B

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Generating a Query Basis of Q

Auxiliary Queries:

Q1:

Q2:

Ans(x,y,z) :- R(x,y), S(y,z), R(x,w).

SELECT DISTINCT r.A, s.B, s.CFROM R r, S s, R r’WHERE r.B = s.B, r’.A = r.APROPAGATE r.A TO A, s.B TO B, s.C TO C, r.B TO B, r’.A TO A

Ans(x,y,z) :- R(x,y), S(y,z), S(w,z).

SELECT DISTINCT r.A, s.B, s.CFROM R r, S s, S s’WHERE r.B = s.B, s’.C = s.CPROPAGATE r.A TO A, s.B TO B, s.C TO C, r.B TO B, s’.C TO C

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Generating a Query Basis of Q

Auxiliary Queries:

Q3:

Q4:

Ans(x,y,z) :- R(x,y), S(y,z), R(w,y).

SELECT DISTINCT r.A, s.B, s.CFROM R r, S s, R r’WHERE r.B = s.B, r’.B = r.BPROPAGATE r.A TO A, s.B TO B, s.C TO C, r.B TO B, r’.B TO B

Ans(x,y,z) :- R(x,y), S(y,z), S(y,w).

SELECT DISTINCT r.A, s.B, s.CFROM R r, S s, S s’WHERE r.B = s.B, s’.B = s.BPROPAGATE r.A TO A, s.B TO B, s.C TO C, r.B TO B, s’.B TO B

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Correctness of the Algorithm

For the example, a query basis of Q consists of Q0, Q1, Q2, Q3, and Q4.

Theorem:

Given a pSQL query Q with DEFAULT-ALL propagation scheme, the algorithm generates a query basis of Q.

Proof Idea: Every query in B(Q) is an equivalent query of Q

Every equivalent query of Q is annotation-contained in Ua q(D) qB(Q)

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Outline

pSQL queries

SemanticsCUSTOM propagation schemeDEFAULT propagation schemeDEFAULT-ALL propagation scheme

ImplementationSystem architectureExperimental results

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System Architecture

Translator Module Input: a pSQL query Q Output: an SQL query Q’ written against the naïve storage

scheme

Q’ is sent to the RDBMS and executed

Postprocessor Module Input: sorted tuples (returned by the RDBMS) Output: An annotated set of tuples.

Annotations for the same output location are collected together Duplicate tuples are removed

PostprocessorTranslatorUSER pSQL

querySQL

querysortedtuples

finalresultRDBMS

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For every attribute of every relation there is an additional attribute for storing the annotations

Conceivably, there are other possible storage schemes

A Naïve Storage Scheme

A B

1 2

3 4

a

b

cd

R A A’ B B’

1 a 2 c

1 d 2 -

3 b 4 -

R’

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The Translator module

Generate a Query Basis

pSQL querydefault-all scheme

set of pSQL querieswith custom scheme

Translatedefault pSQL

to custom pSQL

pSQL querydefault scheme

pSQL querycustom scheme

Translatecustom pSQL

to SQL

SQL query

SELECT DISTINCT r.A AS A, r.B AS BFROM R rPROPAGATE DEFAULT

SELECT DISTINCT r.A AS A, r.B AS BFROM R rPROPAGATE r.A TO A, r.B TO B

default pSQL query custom pSQL query

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Experiments

Goals

compare the performance of pSQL queries under different propagation schemes (DEFAULT, DEFAULT-ALL, or no propagation scheme)

compare the performance of pSQL queries when the number of annotations in a database is varied

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Experimental setup

Implemented on top of Oracle 9i Datasets

100MB, 500MB, 1GB TPCH database Unannotated database on original schema 30%, 60%, 100% annotations on naïve schema buffer size: 256Mb

Test queries SPJ queries Varied the number of joins (0 to 4 joins) Varied the number of selected attributes (1,3 or 5 attributes)

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100MB dataset – 100% annotated

Qi(j) denotes a query with i joins and j output attributes.

SQL vs. pSQL DEFAULT vs. pSQL DEFAULT-ALL

0.01

0.1

1

10

100

1000

Q0(1) Q1(1) Q2(1) Q3(1) Q4(1) Q0(3) Q1(3) Q2(3) Q3(3) Q4(3) Q0(5) Q1(5) Q2(5) Q3(5) Q4(5)

seco

nds

(log

sca

le)

SQL on Unannotated DB pSQL DEFAULT pSQL DEFAULT-ALL

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500MB dataset – 100% annotated

Qi(j) denotes a query with i joins and j output attributes.

SQL vs. pSQL DEFAULT vs. pSQL DEFAULT-ALL

0.1

1

10

100

1000

10000

100000

Q0(1) Q1(1) Q2(1) Q3(1) Q4(1) Q0(3) Q1(3) Q2(3) Q3(3) Q4(3) Q0(5) Q1(5) Q2(5) Q3(5) Q4(5)

seco

nds

(log

sca

le)

SQL on Unannotated DB pSQL DEFAULT pSQL DEFAULT-ALL

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1GB dataset – 100% annotated

Qi(j) denotes a query with i joins and j output attributes.

SQL vs. pSQL DEFAULT vs. pSQL DEFAULT-ALL

0.1

1

10

100

1000

10000

100000

Q0(1) Q1(1) Q2(1) Q3(1) Q4(1) Q0(3) Q1(3) Q2(3) Q3(3) Q4(3) Q0(5) Q1(5) Q2(5) Q3(5) Q4(5)

seco

nds

(log

sca

le)

SQL on Unannotated DB pSQL DEFAULT pSQL DEFAULT-ALL

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100MB dataset annotated in various degrees

pSQL Default on 30%, 60% 100% annotated DBs

0.01

0.1

1

10

100

1000

Q0(1) Q1(1) Q2(1) Q3(1) Q4(1) Q0(3) Q1(3) Q2(3) Q3(3) Q4(3) Q0(5) Q1(5) Q2(5) Q3(5) Q4(5)

seco

nds

(log

sca

le)

SQL on Unannotated BD 30% Annotated DB 60% Annotated DBt 100% Annotated DB

Qi(j) denotes a query with i joins and j output attributes.

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Contributions

an annotation management system for carrying annotations along as data is being transformed

based on provenance

pSQL query language for propagation annotations CUSTOM – user defined DEFAULT – where data was copied from? DEFAULT-ALL – invariant under equivalent queries

Generate-Query-Basis algorithm

an initial implementation

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Future work

Performance of our annotation management system on other storage schemes

pSQL extensions Aggregates Bag Queries

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END

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The CUSTOM Scheme - Example

SELECT DISTINCT BFROM R rPROPAGATE r.A TO B, r.B TO B

A B

1 2

2 3

3 5

R

a

b

c

h

2

3

5

Result

a

b

c

h

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Terminology

A location is a triple (R, t, A)

Definition:

A query Q1 is annotation contained in a query Q2 if:• Q1 Q2

• for every database D, the set of annotations attached to every output location in Q1(D) is a subset of the set of annotations associated with the same location in the output of Q2(D).

A B

1 2

R

aThe annotation “a”

is attached to the

location (R,(1,2),B)

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Ans(x,y,z) :- R(x,y), S(y’,z), y = y’. { x ! 1, y ! 2, y’ ! 2, z ! 3 }Ans(x,y,z) :- R(x,y), S(y,z). { x ! 1, y ! 2, z ! 3 }

Annotations of values that reside in different source locations but are bound to the same variable are unioned together.

Ans(y) :- R(x,y).Ans(y) :- S(y,z).

Ans(2 ).

Annotations that belong to the same output location are unioned together.

In a More Concise Notation

a b

a b

a b

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Containment vs. annotation-containment

A B C

1 2 3

1 4 5

1 8 4

8 9 5

R

a

b c

d

1 5

Ans1

c

1 5

Ans2

c d

a b

b

Q1

Ans(x,v) :- R(x,y,u), R(x,z,v), R(t,w,z). Q2

Ans(x,v) :- R(p,q,v), R(x,z,v), R(t,w,z).

Q1 Q2 but…Q1 a Q2 and Q2 a Q1

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Translating a CUSTOM pSQL to SQL

Q1:SELECT r.A, NULL, s.B, s.B’, s.C, s.C’FROM R r, S sWHERE r.B = s.B

Q2:SELECT r.A, NULL, s.B, r.B’, s.C, NULLFROM R r, S sWHERE r.B = s.B

SELECT DISTINCT *FROM ( Q1 UNION Q2 ) tORDER BY t.A, t.B, t.C

SELECT DISTINCT r.A, s.B, s.CFROM R r, S sWHERE r.B = s.BPROPAGATE s.B TO B, s.C TO C, r.B TO B

custom pSQL query:

SQL query: