A Relational Multi-Schema
Data Model and Query Language
for Full Support
of Schema Versioning
Fabio Grandi
Alma Mater Studiorum – Università di Bologna
SEBD 2002
1
Introduction

Schema Evolution


Automatic recovery of extant data
after schema changes
Schema Versioning
Maintenance of past schemas
(e.g. for legacy application support)
 Two design options for the
implementation of data repositories

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Design Options for Extant Data

Single-pool solution
Roddick, Roddick & Snodgrass [TSQL2 ’95]


All schema versions associated with
a unique shared data repository
Multi-pool solution
De Castro, Grandi & Scalas [TDB ’95, IS ’97]

Each schema version associated with
a different private data repository
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Single-pool - Example
create schema SV1
create table R(A int, B int)
A
B
123
create schema SV2
alter table R add column C int
A
B
C
125
SV1: R(A,B)
SV1: R(A,B)
SV2: R(A,B,C)
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Single-pool - Example (2)
create schema SV3
alter table R drop column B
A
B
C
125
SV1: R(A,B)
SV2: R(A,B,C)
SV3: R(A,C)
create schema SV4
alter table R alter column A char
A’
125
B
C
A’’
xy
SV1: R(A,B) <= R(A’,B)
SV2: R(A,B,C) <= R(A’,B,C)
SV3: R(A,C) <= R(A’,C)
SV4: R(A,C) <= R(A’’,C)
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Multi-pool - Example
A
DP1:
B
A
DP2:
123
SV1: R(A,B)
DP3:
A
C
125
55
B
C
125
33
SV2: R(A,B,C)
DP4:
SV3: R(A,C)
A
C
xy
44
SV4: R(A,C)
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Single-pool vs Multi-pool

Single-pool:
Simple to implement with the
completed schema solution
 Reduces to a quite “trivial” view mechanism


Multi-pool:
More flexible and complex
 Thus, potentially more useful, but…

 Is
it feasible ?
 And what’s for ?
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Multi-pool – what’s for?

From a conceptual perspective
different schema versions
correspond to different points of view
SV1
SV4
SV2
SV3
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Application
Domain
8
Schema Versions as Viewpoints
US Market

EU Market
IT Market
Different viewpoints on the same data involve


different structures at intensional level (schema)
different values at extensional level (data)
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A Concrete Example

The “Lark 2.5” car model in the global market
( diff. viewpoints involve diff. marketing strategies )
US Market
EU Market
IT Market
Lark 2.5
26 K$
Lark 2.5
32 K€
Euro4
Lark GT
31 K€
Euro3
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A Multi-schema Query Language


Full potentialities of a multi-pool
schema versioning approach
can be exploited only by means of
a query language which allows
users/developers to express
Multi-schema Queries
that is involving data belonging to
different data pools
MSQL - Multi-schema SQL extension
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The MSQL Query Language




MSQL basic construct:
contextualization of names and
data references to schema versions
[SV: X] denotes the conceptual entity
named “X” in schema version “SV”
SV: X denotes the extension wrt schema
version “SV” of the conceptual entity “X”
the two mechanisms can be combined
(e.g. as in SVi: [SVj: X] )
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The MSQL Query Language (2)

Examples:

select * from [SVj: R]
retrieve the table called R in SVj
select * from SVi: R
retrieve the contents wrt SVi of R
select * from SVi: [SVj: R]
retrieve the contents wrt SVi
of the table called R in SVj


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The MSQL Query Language (3)

Examples:

select [SVj: A] from R
retrieve the column called A in SVj of R
select SVi: A from R
retrieve the contents wrt SVi of R.A
select SVi: [SVj: A] from R
retrieve the contents wrt SVi
of the column called A in SVj of R


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The MSQL Query Language (4)

Example:

select SVi: [SVj: A] from SVk: [SVl: R]
retrieve the values wrt SVi
of the column called A in SVj
in the tuples belonging wrt SVk
to the table called R in SVl
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The MSQL Query Language (5)

Example:
set schema USMKT;
select NAME
from EUMKT:CAR as EC, ITMKT:CAR as IC
where IC.PRICE < EC.PRICE
retrieve the American names
of all the cars also sold in Europe
such that the Italian price
is lower than the price applied
in the rest of Europe
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The Logical Storage Model



Provides a simple implementation scheme
for a Multi-pool schema versioning database
(on top of a traditional relational DBMS)
Is used to formally define the semantics
of the MSQL language
Representation of the SVi data pool:
RNamei(RID,Name)
ANamei(AID,Name)
RSchemai(RID,AID)
RExti(RID,TID)
AExti(TID,AID,Value)
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Conclusions




Defense of the Multi-pool approach
(from a conceptual design standpoint)
Introduction of the
Multi-schema Query Language (MSQL)
Introduction of the
Logical Storage Model
Future Work
 development of implementation strategies
 study of formal properties
(e.g. correctness of a schema evolution process
under the multi-pool approach)
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