D&-I-AB&%SE
fiN
EXPERT
INRICS,
BOUZEGHOUB,
Elisabeth
Projet
SABRE
Institut
de
4, place
VI,
and
78153
Le Chesnay
Programmation
Jussieu
Cedex
is far
design
applications.
process
is
an
iterative,
long
and tedious
task.
It is
a
certain
characterized
by
the
way of choosing
indetermination
in
stuctures
and
constraints.
Several
data
schemas
may describe
the
same
different
also
process
is
The
design
reality.
intuitive
and
by
an
characterized
empirical
methodology.
Consequently,
the
obtained
i s
schema
the
quality
of
database
the
dependent
on
heavi 1 y
and
insight
exper
i ence
administrator
“s
in
the
database
the
more
many
semantics
of
preciseness
the
researchers
(relational
accurate
difficult
i5
real
and
have
conceptual
tools
Proceedings of VLDB 85, Stockholm
to
large
models
make
the
design.
produces
a
if
a
method01
ogy
not
schema,
it
is
conceptual
it
into
a
al 1 to translate
trivial
at
physical
database
schema.
The conceptual
physical
schema
mapping
is dependent
to
from
both
the
user
application
(e.g.
the
transactions)
with
data
Even
“good”
end-user.
capture
France
Using
semantic
from
being
sufficient
for
process
easy
design
Indeed
the
Today,
relational
technol
ogy
is
wide1 y
spread.
Many
users
are
designing
their
databases
with
the
rel at i onal
model.
However,
using
the
relationnal
model
as
a
design
tool
is
conceptual
somewhat
controversial
CKENT793.
Indeed,
domain ,
the
relational
concepts
of
attribute,
relation,
referential
functional
and
mu1 t i valued
constraint
,
dependencies
neither
simple
to use
are
nor
sufficient
to
capture
the
semantics
of
user’s
applications.
To enhance
the
the
semantics,
integrity
constraints
may
expression
i 5 not
but
their
be
used,
nor
natural
for
the
al ways
easy
world
naturalness,
75230
several
so-called
semantic
data
models,
CSMIT773,
SHN+
such
as
SHN
CBROD811,
SDM CHANM801,
RN/T
CCODD791,
TAXIS
LAURA
tBROW831,
NORSE
~MYL0817,
CBOUZ83al.
are
generally
Objects
some
kind
of
assemb
1 ed
together
using
semantic
from
constructs
borrowed
CIrtif
icial
networks
used
in
Intelligence.
Except
some
differences
in
the
way
of
formalization
and
the
these
expressing
certain
constraints,
models
offer
similar
concepts
of
object,
aggregation,association,
classif
ication,
generalization.
and
INTRODUCTION
To
GARDARIN
proposed
CIBSTRCICTt In this
paper,
we report
on
the
implementation
of
SECSI,
an expert
for
database
design
written
in
system
Starting
from
an application
Prolog.
description
given
with
either
a subset
natural
language,
or
a formal
the
of
graphical
interface,
language,
or
a
system
generates
a
specific
the
network
i c
portraying
the
semant
application.
Then,
using
a
set
of
rule5,
design
it
completes
and
the
semantic
network
up to
simplifies
normalized
relations.
All
flat
reach
the design
is interactively
done
with
the
end-user.
The
system
is
the
sense
that
it
also
evolutive
in
offers
an interactive
interface
which
al 1 ows
the
database
design
expert
to
modify
or add design
rules.
1.
George5
METAIS
MASI,
Paris
BP.105
0
FcPPROFcCH
SYSTEM
Mokrane
Laboratoire
Univer5it#
1-OOI-S
DESIGN
8:
and
the
database
system
and
Consequent
1y )
an
,networI::).
efficient
internal
schema
and
a good
from
produce
to
automat
i c
without
schema
human
interactions.
Sever
design
al
have
tools
alrrady
been
proposed
CCERI83,
DAVI83,
WAS882,
TAHN84,
DAENS4 3
for
database
COBB84,
design.
Some
of
them
are
attractive
and
original,
but
most
of
them
suffer
from
the
f 011 owing
shortcomi
rigs:
They
are
not
completly
integrated;
(1)
constitute
a
they
do not
in other
words,
of
sparse
set
complete
system
but
a
each
interface
which
rare1 y
programs
other.
(2)
They
are not evolutive;
some
change
implies
of ten
rules
the
design
in
reprogramming
the
whole
tool.
a
that
it is easy to integrate
new
way
dasi gn
rules
and
to
update
the
existing
ones
as
soon
as
the
know1 edge
progresses.
SECS I
is not
designed
as a
black
box
providing
useful
services
but
as
an
open
system
which
is
able
to
and
to
transfer
its
expertise
to
explain
the
end-user
and
to
1 earn
new
know1 edge.
The
present
implementation
organized
as
the
introduce
At
beginning
analysis
the
1982,
of
of
art,
on
architecture
starting
database
the
we proposed
a
of
the
state
expert
systems
based
new
approach
This
approach
is
CBOUZ83bl.
techniques
original
tool
called
supported
by
an
(an
acronym
for
Systeme
Expert
en
SEC8 I
SystOmes
d’Informrtions1
Conception
de
implemented
in PROLOG,
on
which
has been
database
relational
top
of
SADRE,
a
system
’
The
system.
management
based
on a semantic
data
mod::
strongly
technology
and
CBOUZ83a1, the relational
this
from
design
artificial
as expert
certain
techniques
expert
which
a
the
system.
The
power
friendly
end-user
of
characterized
know1
work
human
edge
as
by
base
efficiently
expert
is
is
we
2,
object
i ves
and
the
SEC31 . In section
3, we
various
external
interfaces
The
section
4 is
devoted
to
network
both
section
process
based
OBJECTIVES
AND CIRCHITECTURE
OF SECSI
OBJECTIVES
SECSI
integrated
systems.
program
base,
and
This
section
the
representation
of knowledge
semantic
on a specific
and
production
rules.
They
are
represented
by Prolog
clauses.
In
5, we detail
the
logical
design
which
is currently
implemented.
which
2.1.
to
and
paper
of
present
the
of
SECS I.
the
internal
2.
is
paper
intelligence
is an intelligent
system
is devoted
to
a specific
which
and
where
there
application
domain
of
enough
knowledge
to infer
one
or
exists
but
where
there
does
solutions,
several
performant
precise
or
exist
not
any
the
same
performs
which
algorithm
This
approach
is chatacterized
results.
which
architecture
original
by
an
distinguishes
between
:
contains
base
which
a
know1 edge
rules
and
skills,
concepts,
facts,
an inference
engine
which
is a set
of
of
the
knowledge
techniques
management
An
of
this
architecture
of
SECSI.
follows.
In
purpose
the
external
interface
interacts
with
is
system
expert
an
the
content
of
and
its
capabilities
as
CLAUR81,
possible
by
the
its
like
to
a
HAYE833.
to
have
the
same
intended
SECSI
is
the
expert
systems,
characteristics
of
the
not designed
to replace
but
it
is
base
is
Its
know1 edge
expert.
human
as modules
of
rules;
a set
of
organized
level
of
abstract
compose
an
modules
going
down
the
levels
offers
C::nowl edge;
know1 edge.
Thus
refinement
of
gradual
the
knowledge
base
is specified
in such
the
user
database
directed
objectives
(1)
To
has
been
intelligent
in
design.
by
composed
al gor i thms
theory
and
area.
This
designed
as
an
for
helping
tool
the
tedious
process
of
design
of
SECSI
was
The
the
following
specific
:
constitute
of
all
a
useful
knowledge
concepts
the
relational
base
and
in
semantic
data
models
will
be especially
helpful
for
common
designer
who
are
not
necessar
i 1y
expert
in
database
design
theory.
This
knowledge
base
may also
include
some
experimental
and
specific
rules
related
to the
user’s
experience
in
database
design
and
to a specific
domain
of
application
(banking,
reservation,
medicine...).
(2)
To
define
an
interactive
methodological
environment
which
permits
to
perform
as far
as possible
the design
steps
with
incomplete
specifications,
and
which
permits
to
backtrack
to
any
step
in
order
to
some
change
5pecif
ications
or
to
integrate
new
information.
(3) To identify
for
each
design
step
the
general
or
specific
principles
of
reasonning,
to provide
as detailed
and
explanations
as
possible
about
these
principles,
the models,
and
the
rules
on
which
they
are
based
(4)
To
open
system
of
tools
build
an
in one hand
to integrate
which
enables
developped
in the
schema
rel at i onal
composed
of
a set
of
relations
with
their
keys,
a
permanent
virtual
relations
derived
from
set
of
queries,
and
a set
the
formerm
by given
integrity
constraints.
The
integrity
of
theoretical
and
design
concepts
and
in
hand
to
the
other
transf
ert
its
via
its
experti
se
both
use
usual
and
and
via
explanations
justifications
of
its
results.
To
(5)
facilitate
interaction
with
the
human
offering
designer
by
him
a
easy
semantical
1y
and
rich
to
use
new
rules,
constraints
include
domains,
referential
inclusion
constraints.
It should
and
to more
general
constraints
extended
the
interface.
is qua1 if
This
too1
system
in
the
eens that:
-- it
offers
an evolutive
ied
as
an
A
steps.
aystem
expert
knowledge
base,
accepts
incomplete
specifications,
- it
“- it
justifies
and
explains
its
results,
permits
to
backtrack
to any design
-- it
order
to
change
specifications
step
in
or to ask for
explanations.
Of
far
the
all
these
thoroughly
implementation.
of
SEW1
course,
being
from
current
architecture
pursue
objectives
reached
SECSI
to
them.
The
2.2.
SYSTEH
ARCH I TECTURE
of
SECSI
is
most
expert
SECSI
specification
user
:
the
refered
address
expert
here
specialist
applications
of
in
(shortly
two
in
as
the
ref ered
here
as
the
end-user
).
The
of
the
creation
is
responsible
expert
the
modification
of
and
the
base
of
rules.
The
end-user
is in charge
design
of
the
creation
and
the
modification
of
describing
the
base
of
facts
the
is
around
an
expert
knowledge
rules
composed
of
a set
of
design
and
a set
of
facts
(BF).
The
set
of
captures
the
design
methodology
the
set
of
facts
describes
the
’s
application.
In
the
current
rules
while
interfaces
experts
database
design
(shortly
the
the
expert
1
and
organiqed
base
(BR)
external
different
general
architecture
in
figure
1. Like
CLAUR82,HAYE841,
The
portayed
systems
organized
in
is
a step
is activated,
the
ask
complementary
for
and the
end-user
may ask
for
Whenever
a schema
has
been
by
user
SECSI ,
the
can
session
Whenever
may
generated
with
the
result.
In this
case,
desagree
the
session
may be restarted
at
any
of
steps
according
to
the
user
the
design
request.
As soon
as the
schema
satisfies
the
user’s
needs,
the
design
process
is
termi
nated
and
the
schema
is stored
in
the
Sabre
meta-base.
the
us
future.
information
explanations.
are
with
However,
allows
near
be
in
application.
explain
interfaces
of SECSI
following,
the
detai
1s
the
corresponding
In
in
more
and
the
(Figure
we
i ous
process
var
2).
I
Fiq.
1:
version
inference
the
set
engine
process.
General
architecture
of
SECSI.
Fiq.2:
Pro1 og
acts
as the
SECSI,
the
system.
Using
engine
of
of
design
rules,
this
inference
deduction
out
the
carries
It first
generates
a normalized
of
Interfaces
and
orocesses
of
SECSI
LEARN
function
to
offers
the
SECS I
expert
and
the
ACCEPT,
RUN and
HELF
end-user
(see
figure
functions
to
the
expert
to
the
enables
2).
LEARN
the
84
and
design
rules.
update
the
rules
are
introduced
using
by
graphical
interface
or production
rules.
In
the
first
version
they
are
directly
written
in
Prolog.
ACCEPT
enables
the
end-user
to introduce,
to
list
and
to
update
the description
of
an
application.
Three
languages
are offered
introduce
Such
either
to
the
end-user
by the
ACCEPT
process
:
a
restricted
natural
1 anguage
(ACCEPT-NATURAL),
a
simple
declarative
1 anguage
(ACCEPT-SHORT)
and
a graphical
interface
(ACCEPT-GRAPHICS).
The
DESIGN
process
yields
a normalized
relational
xhema
from
an application
description
(RUN)
and
brings
out explanations
about
the
produced
schema
and
the
applied
rules
(EXPLAIN).
HELP
informs
and
assists
the
end-user
about
the
model
used,
the
applied
design
rules
and
the
functioning
of
the
system
itself
(HELP-DESIGNER).
Also,
we plan that
this
module
help students
learning
data
may
models
and
database
design
(HELP-STUDENT)
from
predef
i ned t-u1 es and
To specify
application,
THE LIHITS
The
both
be
design
methodology
complementary
phases:
may
(1)
(2)
(3)
view
specification,and
integration
logical
schema
design
and
physical
schema
design.
The
first
version
of
SECS I
which
is
described
in
this
paper
is
only
concerned
by
the
second
phase
(i.e.
logical
design)
including
some
aid in
schema
specification
and
consistency
verification.
The
objective
of
this
first
version
is to learn
expert
systems
and
to
show
through
one
design
phase
how
do
they
database
design.
apply
to
We are
specifying
a second
and
a third
version
for
view
integration
and
physical
design.
declarative
language
is derived
programming
language
type
declarations
1 anguage
and
the
of
rules,
system
data
that
the
only
structure
the
helps
functional
example
CSHIP811).
It
is
defined
by a
very simple
grammar
which
is illustrated
the
example
3. This
given
figure
by
grammar
permits
to
declare
IS-A
rel at i onshi ps:
STUDENT
: PERSON,
associations
between
entities:
n-ary
for
DAPLEX
ENROLLED(STUDENT,COURSE),
including
and
in the
hierarchies
hierarchical
as in the
model
network
EMPLOYEE(DEPARTMENT),
that
basic
characterise
types:
NAME(PERSON):
constraints
some
entities
TEXT,
as functional
and
mu1 ti val ued
dependencies:
NAMEtDEPARTMENT)
-> ADDRESStDEPARTMENT).
EtPLDEE:fTRm.
STUDENT: PERSDN.
STWF : EWLOYEE.
TEAafR:EWLOYEE.
NNEF’ERSN) : TEXT.
RDDREssIDEPKfTlENr) : TEXT.
ME(-)
= WVH,DB).
!mmFwEE) : INTEER.
sNMYEwLDYEE) : REAL.
TELuExtER) : INiE6ER.
IwsTRucToR:TEIy)ER.
-:TGXtER.
tE&D-W-SECTloN : SWF.
DI~H~F-L~WWIT
: STNF, TEAMI?.
EsKlNsIu~mm,wuIsE).
6IvEN~BY
miss, IHsTRucToR)
.
ENa.LEDcinumT,couRsE).
EmDYEEmEPiwmT).
f-Wt!3SMWElt)
: TEXT.
FREE-GIFT(STWF) : RE#.
NBm?(sNDEKI) : INmER.
DATE(W)
: INTE6ER.
NmER(anss) : INTEEI?.
)(RIEXDlRSE) : TEXT.
DaYKzw?sE) : TEXT.
HmRaImsI3 : INTE6ER.
l?aM(wuIsE) : INTEER.
in
and
is
passive
components
of
an information
system.
It
is of no help for
the
transaction
design
phase.
However,
SECSI
does
not ignore
the
influence
of potential
transactions
both
on the
conceptual
and
the
physical
sjtructure
of
the
database.
Indeed,
the
physical
design
process
should
integrate
information
about
transaction
frequencies,
volumes
and required
level
of
response
time
for
the
main
transactions.
from
(5ee
constructs
cu\ss(cmF(sE).
Currently,
the
design
integrity
of
an
choose
from
OF THE SYSTEH
database
as three
data
structures
end-user
may
ween
three
types
of
interfaces:
a
simple
but
formal
declarative
language,
a
restricted
subset
of
the
natural
1 anguage
and a graphical
interface.
He
use
two
or
all
of
them.
may also
and
The
seen
the
the
bet
attributes
with
their
examples.
2.3.
3. END-USER
AND EXPERT INTERFACES
3.1.
HOW TO DESCRIBE
AN APPLICATION
WE(DEpARTfENl) -> W(D).
SSNEWLDYEE) -1 lWEW’PLDYEE1, Sk.W(EtFlBYEE).
wEKllu?%)
-) RcKw(wuIsE), DaYuxmE), HouI(wuAsEI.
Example
Fio.3:
lansuacle
The
offers
French
readable
fact,
to
natural
language
based
interface
a
very
restricted
subset
of
which
makes
the
specification
communicable.
In
and
easily
it
appeared
quickly
not
feasible
start
specifications
that
natural
very
of the declarative
descrintion.
complex
with
very
complex
in French.
One reason
is
language
understanding
is
constitutes
a vast
and
domain of research
in itself;
another
io
system designer
is
that
an information
an expert
who has
his own jargon
and
who
needs
synthetic
and
unambiguous
powerful
tool 5 instead
of
subject-verb-complement
sentences.
Hence,
we limited
our
natural
interface
language
to
a
strict
translation
in
a more
natural
form
of
declarative
our
1 anguage.
An
example
corresponding
to
a
the
sample
of
pr eviou5
language
interface
declarative
is given
in figure
4.
graphical
interface
may be either
implementation
of
the semantic
utilize
to
represent
the
we
one
of
the
knowledge,
or
The
a direct
network
internal
traditional
data
Entity
Relationship
Codasyl
of
a
network
secstion
4.
such
model 6
model
data
model.
fin
HOW TO
As
powerful
DEPm
lwwfss MO NlyEs KE TEXTS.
EJPLOYEE’SSII
IS Aw INTESER.
EtPLOYEEWMY IS A RERL.
for
based
AaASSIs61~BYANINSTRucTaR.
ASTUDENTISENUXlEDINMWlIRECMSES.
between
offer
the
IF
experts
declarative
statements
CONDITION
natural
1 anauaqe.
-
AGGREGATION
CLCISS OF /
and
mappings
network.
1 anguage
of
THEN
need
their
We
types
language
espressing
semantic
a
should
form:
the
CICTION.
a relationship
condition
expresses
two
objects.
between
relationships
are:
descriotion
of
two
statements
The
example
experts
speci+y
design.
: a declarative
if -then
too1
for
two types
of
The
accept
RULES
the
end-user,
1 anguages
to
in
database
on
graphical
AKFMTKNTtYYEDETERNIM3~UEPMMNT-.
ANEmmEE’ssN DETERHIMMl EWLrwEE’SM.
I ErPLmEE’ssNDETERtlIlEsA !%wW.
DESIGN
SPECIFY
the
expertise
envision
to
interfaces
of
AF%lFESSORISRE9WSI&EaAalu?X
fin
in
the
semantic
network
corresponding
to
the
previous
example
in figure3
is given
semant
i cs
of
the
in
figure
5.
The
in
will
be espl aned
different
arcs
3.2.
Fio.4:
as
and
the
example
F’ossible
OF / ATTFiIBUTE
INSTANCE
OF,
OF,
- GENERALIZATION
OF / SFECIfiLISATION
OF,
- ASSOCIATION OF / FARTNER OF,
-- ERUIVALENT TO.
suppose
we have to specify
For
example,
inheritance
design
rule
the
as
a
in
generalization
the
property
be written
as the
hierarchies;
it
can
production
rule
portrayed
in figure
6.
IF X IS A GMERIILIZI~TION
OFXl
MDAISANaTTRIWTEff x
MDXISAPMTKROFR
TWIIISfwATTRIWlEa
xi
tWDXl IS R FWTM OFR.
Fio.6:
The
convenient
bet
ween
As
the
question
schema
Fiu.5:
An
examole
of
interface.
the
An example
of
in a declarative
a rule
expressed
form.
interface
is
a very
to express
mapping
rules
types
of semantic
network.
two
modeling
is often
a
conceptual
graphical
tool
schema
of
this
mapping,
representation
latter
facility
and
ic;
&Je shall
see later
that
very
important.
rules
are mapping
design
most
of
the
facilities
to
having
thus
rules,
probably
rules
will
these
visualize
increase
the friendliness
of the system.
rules
from
examples
may also
Generating
However,
many
issue.
attractive
be
an
qraphical
86
cannot
rules
transformations;
rules
be
in
should
expressed
case
this
for
Fiq.7:
two
The
of
rule
transformation.
graph
compiled,
generated.
which
is
interfaces
rules
are
clauses.
4.
example
An
a
111A2 w
expressed
as
interfaces
will
be
preceding
processible
rules
will
be
and
In the
first
version
of SECSI
currently
running,
these
two
are
not
implemented;
yet
directly
represented
as Prolog
INTERNAL
are
model.
as
a
be used.
41 AZ A3
now
going
to
present
a more
definition
of
this
semantic
data
semantic
network
is defined
Our
where
NC stands
triple
(NC,AC,IC)
We
formal
by
graph
production
the
category
we have
and
rules.
two
types
To represent
of
we use a combination
of
models:
semantic
two
networks
to
facts
and
production
rules
to
represent
represent
application
constraints
and
design
rules.
The
following
sub-sections
deal with
these
two kind of models.
INTERN&L
the
- f4gqreqation
arc
denoted
a(X,Y)
that
X is a part
of
Y or that
the
property
X. This
arc
links
an
atomic
object
to
a molecular
object.
For
example,
using
the
application
portrayed
figure
5,
we can write
a(NAME,PERSON),
a(&DDRESS,PERSON).
REPRESENTATION
OF FCICTS
specifies
i mp 1 emen t
the
base
of
facts,
we
a specific
kind of semantic
network
of
privileged
position
of
the
this
too1
between
database
models
and
natural
1 anguages.
Our
semantic
network
presented
hereafter
contains
most of the
concepts
of
semantic
data
models like
aggregation,
generalization
and
classification.
differences
The
main
with
these
models
are,
first,
the
with
formalization
a
few
basic
constructs
(a,
r,
c,
9);
second,
the
categorization
of
the
different
nodes
and
arcs
and
the
distinction
between
two
(aggregation
of
types
of
aggregation
and
attributes
called
aggregation,
aggregation
of
entities
called
Moreover,
several
association).
constraints
may be added
on each
kind of
To
use
because
nodes.
arc
that
association
molecular
instances).
relationship
may be written
denoted
r(Y,Z)
is
involved
Y
in
the
Z.
fin association
connects
objects
(entities
or
For
example,
the
binary
ENROLLED(STUDENT,COURSE)
as:
r(STUDENT,ENROLLED),
r(COURSE,ENROLLED).
- Classification
arc
denoted
c(X,Y)
is an element
of
the
class
Y.
Classification
are
not
recursive
can
only
link
a value
to
and
attribute
or an instance
to
class
its
entity-class.
For
example,
we have
its
specifies
and
AC
specifies
Y
has
- Association
arcs
nodes,
REPRESENTCITION OF KNOWLEDSE
As stated
before,
knowledge:
facts
this
knowledge,
4.1.
of
category
of
arcs
and
IC the category
of
such
that
for
each
element
constraints,
f of CIC, there
exists
an application
:
f:
NC X NC ----->
CTrue,False>
such
that
f (ni ,nj)
is
true
if there
exists
an arc
of
class
f between
ni and
false
otherwise.
The
elements
of
nj , and
NC can
be classified
in two ways :
(1)
Atomic
objects
(attributes
and
values)
and
molecular
obiects
(entities
and
instances)
.
(2) Classes
(attributes
or entities)
and
elements
of
classes
(values
or
instances).
these
different
The
elements
of
are
connected
by the
categories
of nodes
following
categories
of arcs:
that
X
c(PfiRIS.ADDRESS)
c((COMPUT-SCE
P&IS>,DEPARTMENT)
where
<COMPUT.
SC.
PARIS>
is
a tuple
representing
an instance
of
DEPARTMENT.
- Generalisation
specifies
that
corresponds
arc
.- Eouival
specifies
This arc
to
ence
that
is
g (Y ,Z)
a sub-class
of
Z.
It
we1 1 -known
the
is-a
may be used
recursively
It
relationship.
in
a hierarchy
of
transitivity
property.
have
’
g(STUDEN+:PERSON)
denoted
Y is
the
objects
and
For
has
example,
application,
the
we
an~l~~~ROF,TEACHER).
arc
two
especially
denoted
e(Zl,Z2)
nodes
are equivalent.
useful
when
it
is
important
different
to
see
ways.
are
assume
that
More
sport.
equivalent
assertions
generally,
to
g(X,Y)
previous
The
in the reverse
particularization
instantiation
and equivalence
the
and
two
g(Y,X).
example,
cardinal
we
one
e(X,Y)
is
following
- Functional
consider
between
as
(01,
(s)
have
to
be added
to
nodes
to
enhance
the
and
semantics
of
the
preceding
network.
Most
may be expressed
by additional
of
them
nodes
and/or
arcs,
or
by
appropriate
expressi
on5
of
predicates.
We
list
hereafter
some
type6
of
these
arcs
constraints
Intersection
intersection
X2 when
4.2.
INTERNAL
constraint:
between
There
two
it
classes
class
rules
the
OF RULES
constraint:
to
a
class
includes
rules
and
rules
which
network.
of
design
consistency
structural
act upon the
Consistency
enable
the
system
to
rules
to maintain
the
consistency
and
verify
the
conceptual
model
described
with
of
Structural
semantic
network.
the
the
system
enable
rules
transformation
the
semantic
network
in a
transform
to
optimized
relational
and/or
normalized
an
and
such
and
the
schema.
of
rules
co1 lects
class
second
this
category
First,
know1 edge.
the definition
of
the
types
of
nodes
of
the
semantic
network
and
arcs
these
propert
i es of
the
general
and
the
contains
also
it
Second,
types.
of
relational
concepts
(i.e.
definition
domain functional
attribute,
relation,
(i.e.
properties
and
dependency)
rules
and
normal
inference
firmstrong
‘a
as we manipulate
sets
forms).
Finally,
and lists
of
objects,
general
knowledges
1 ists
are also
and
set
theory
about
included
in this
second
class.
The
general
includes
with
hierarchy.
It
specifies
whether
the
union
of all
specialization
c 1 asses
is equal
or not
to
the root
class
of the hierarchy.
Let
Xl ,..Xn
be
the
subclasses
of
X and
let
Ii be repecti\elly
the elements
of X
I,
and
Xi, then
if $J Ii = I, X is called
a
completely
specialized
class,
otherwise
X
is
called
a partially
specialized
class.
respect
REPRESENTATION
enforcement
is
Xl
first
transformation
semant
ic
g(STUD-INSTR,INSTRUCTOR).
- Union
We
dependencies
-->ADDRESS(DEPfiRTMENT).
important
classes
been
distinguished.
have
The
enforcement
exists
a third
X3
predicates
g (X3,X1)
g(X3,X2)
hold.
For
example,
with
university
application,
the
two
classes
STUDENT
and
INSTRUCTOR
intersect
because
g(STUD-INSTR,STUDENT)
and
functional
:
Three
that
constraint:
deoendency
here
N&ME(DEPARTMENT)
- Domain constraint:
Each
attribute
has
domain which
is extensionally
defined
enumerating
its
val ues,
or
EY
intensionally
defined
as
a basic
data
(integer,
real,
or text).
Moreover,
type
data
type
values
can be constrained
by
any predicate.
-
this
the
attributes
of
universal
composed
of
relation
schema
all
the
attributes
of
a semantic
network.
As in
semantic
network
we do not
assume
our
the
uniqueness
of
attribute
names,
we
attribute
qualify
each
by the
name
of
entity.
For
its
example,
a possible
dependency
is :
functional
constraints
Some
these
r(STUDENT,ENROLLED)
(1,4) 'I
student
at least
has
at
most
4 enrollments.
has
the
cardinality
and
arcs can be interpreted
direction
respectively
(P) 9 partnership
(i),
specialization
(el arcs.
if
i ty
has
the
means
that
a
one
enrollment
and
If p (TEACHER,TEL)
(C),2) then
it means
that
a teacher
may have
zero,
one
or
two
telephone
numbers.
In
general,
the
relevant
values
are
0 or
1 for
m and
1
or N for
n (with
N>l).
object
in
example,
same
For
e(STUDENT,SPORTSMAN)
STUDENT,
SPORTSMAN
equi val ent
classes
if
al 1 students
practice
e(STUDENT,PUPIL),
that
specify
PUPIL
the
expressed
generalization
rules
is composed
meta-rul
es which
control
the
sequence
of
design
steps
and
rules
to apply
at each
step
the
se1 ect
which these
rules
over
the
facts
and
- Cardinalitv
constraint:This
constraint
is
assoc i at ed
with
r arcs and a/p arcs
(keep
in
mind that
p is
the
reverse
of
a).
Cardinalities
are represented
by a
pair
of
values
(m,n) which specifies
on
the
one
hand
whether
the
relationship
is
total
(m>U)
or partial
(m=(J),
and
on the
other
hand
whether
the
relationship
is
functional
(n>ll.
(n=l)
or
For
not
The
of
a
third
class
hierarchy
of
of
operate.
Al 1
encoded
a8
these
Prolog.
in
cl asses
Figure
rules
of
8 portrays
are
the
description
of
an
a sound
conceptual
schema
a semantic
stored
as
network
with
associated
constraints.
Then, a fourth
normal
form
relational
schema
with
associated
integrity
constraints
is
produced.
The
global
process
is divided
in
step5
which
are
more
precisely
described
below.
from
inheritance
rule
in
Prolog.
g and
specify
respectively
and
aggregat
i on
of
figure
a are
the
inheritance
<- gC*Xl,*X),
inheritance
<- g(SXl,tX),
r
refers
to
an
a(tA,tX),
insert~clause(a~*A,*Xl~~,
delete-clause(a(*fi,*X)).
r (tX,tR),
ins-ert-clause(rI*Xl,SR)),
delete-clause(r
(tX,tR)).
Fiq.
_.
8 : The inheritance
rule
in
Proloq
is a meta-rule
which
depth-first
strategy
to
r;earch
and
generalisation
suppress
figure
9).
hierarchies
(see
s is the
arc
specialization
of
the
semantic
network,
x, y, z, w are Prolog
variables
the
node
of
standing
for
the
generalization
hierarchy;
transform
is a
Another
example
describes
a
structural
transformation
depth(tx)
<-
depthttx)
5.1.
rule.
An
example
pxpressed
of
STEPS
I
(ty,lrz)).
a
in
meta-rule
Proloq.
and
to
where
the
each
leaves
rules
we have
of
path
from
corresponds
have
some
choosed
to
premises
these
the
root
to
a
down
given
rule.
5.
THE
The
THE
second
relational
interactive
and
the
The
normalisation
carried
last
out
dependencies
initial
the
step
step.
using
of
the
the
orms
constraints
normal
form
is
called
the
Normalization
is
both
the
functional
attributes
given
in
between
specification,
LOGICCIL DESIGN PROCESS
The
logical
dependencies.
composed
iS
generates,
called
pert:
relations.
such
as
intersecti
on
and
union
of
classes,
cardinalities
of
relationships
(aggregation
and
assoc
i at i on 1
and
functional
dependencies
between
attributes
are
acquired.
Normal
form
rel ati ons are constructed
by suppressing
generalization
hierarchies
and
separating
multivalued
attributes.
of
system
process
is
step
step.
It
acquisition
choice
of
first
Constraints
cardinalities
the
allow
functional
design
HETHODOLOGY
first
The
we1 1 -known
principle
of
expert
One
system
design
is
that
the
modularity
and
independence
of
rules
greatly
the
the
evolutivity
of
the
system.
enhance
phi 1 osophy.
But
This
is
a
good
we have
a large
base
unfortunately
, when
this
important
principle
of
knowledge,
the
performances
of
decreases
the
when
the
Prolog
especially
-,ystem,
provide
a
not
does
interpreter
strategy.
That
is
search
sophisticated
some
cases
we have
turned
aside
why
in
this
principle.
Indeed,
as in some
from
several
design
steps
overlapping
premises,
built
trees
composed
OF
step
is
called
the
step.
It
performs
the
of
the
application
in order
to
generate
a sound
and
consistent
conceptual
schema.
In
controls,
this
addition
to the syntactic
step
checks
and
solves
the
problem
of
homonymous
and
synonymous
i nf ormati ons.
It
also
detects
generalization
cycles.
The
system
tries
to
evacuate
the
possible
inconsistencies
with
the
end-user’s
help.
insert-clause(father(Sw,tx)),
Fiq . 9:
THE
yerification
validation
description
(ty).
transform(*x).
depth($x)
<- father(tw,Sx),
depth
(tw)
depth(Sx)(-delete-clause(father
external
This
process
is
performed
in
a
combination
of
a forward
and
a backward
chaining.
The
general
principle
is to
successively
transform
a
given
5pecification,
trying
all
the
rules
until
no rule
is applicable.
This is the
definition
of
the
forward
chaining.
Hut
at
each
design
step,
we
use
a
may
backward
chaining
to
enforce
a
consistency
constraint
for
example,
or
to
verify
that
a given
information
is
not
redundant
(i.e.
not derivable
from
another
information).
This is especially
the
case of functional
dependencies.
The
s(tx,ty),
s(*y,*z))
11%
depth
sItw,tx),
<-
an
application,
expressed
predicates
which
generalization
arcs.
association.
7
and
and
of
normalization
two
the
which
some
mu1 t i -val ued
associations
infere
to
phases
:
process
partial
normalization
using
local
functional
(between
attributes
of
Sdme entity),
and
total
normalization
global
I..Isi n g
functional
dependencies
attributes
(between
of
different
dependencies
order
of
the
logical
design
is
a set of 4NF relations
with
keys
their
and
multiple
(both
unique
keys),
a set of virtual
relations
with
their
deriving
relational
queries,
and
a
including
domain
set
of
constraints
constraints
constraints
and
inclusion
integrity
referential
(in
particular,
method01
ogy
is
constraints).
The
sequence
of
steps
characterized
by
a
alternatively
require
algorithmic
which
verification
(e.g.
tasks
and
normalization)
and
human
decisions
(e.g.
acquisition
of
constraints
and
choice
of
and
relationships).
entities
The
paragraphs
describe
.following
in more
how
steps
two
details
and
three
are
implemented
to
produce
a
normalized
relational
schema.
result
5.2.1
5.2
PRODUCTION
RELATIONAL
The
different
with
a
sound
semantic
the
production
of
a normalized
schema
is
relational
performed
during
relational
and
the
normalization
the
stated
steps,
as
above.
Each
step
is
composed
of
three
actions.
The
relational
step
encompasses
the
actions
supression
getting
functional
Indeed,
a
is valid
for
advantage
and
The
suppression
hierarchies
OF A NORMALIZED
SCHEHA
Starting
network,
following
h'l) The
the
of
more
precisely.
dependency
which
TEACHER
the
attributes
is
not
necessarily
valid
for
the
PERSON
attributes.
For
example,
we may have
NAME (TEACHER)
-->ADDRESS
(TEACHER)
and
not
NAME(PERSON)-->ADDRESS(PERSON).
It
is
the
same
problem
for
cardinalities
which
hold
at
the specialization
levels
may
not
at
and
the generalization
levels.
But
changing
the
action
order
could
improve
attributes
performances
because
not
are
dupl i cated
by
inheritance
properties
and
dialogue
of
the
the
constraints
would
be
acquisition
shorter.
In the
second
version
of
SECSI,
implement
we
some
meta-rules
to decide
wether
it
is
interesting
to begin by
step
Rl,
R2 or HZ.
These
meta-rules
are
essentially
based
on
the
number
of
attributes
and
specialization
entities.
'The next
sub-sections
detail
each
of
the
preceding
actions.
entities).
The
process
has
cardinalities
dependencies
functional
the
hierarchy
possible
be
replaced
virtual
problem
nodes
which
generalization
of
is
to
of
node(s)
relation(s)
either
relations,
by
choose
between
generalization
a
and
new
must
be kept
as
which
one
must
or
attributes,
integrity
or
:
of
generalization
the
hierarchies.
R2)
The
acquisition
of
aggregation
(cardinalities)
and
the
of
multivalued
attributes
to
separation
obtain
1NF relations.
HZ)
acquisition
of
functional
The
dependencies
between
attributes
of
each
1NF relation.
constraints
normalization
step
includes
the
actions
:
A partial
normalization
process
Nl)
synthesizing
a
simplified
using
algorithm
CBEER791.
acquisition
of
association
N2)
The
and
constraints
(cardinalities)
the
suppression
of
the
association
arcs.
N3)
process
A complete
normalization
algorithm
using
the
decomposi
t i on
CFAG177, ZANIGll.
The
following
In
s i x
order.
three
the
first
actions
However,
actions
version
are
may
of
processed
the
order
be changed.
SECSI,
in
of
the
the
The
Fio.
10:
jzransformations
Examples
of
of
structural,
oeneraliratio~
hierarchies.
The
general
principle
is
to
constraints.
“more
semantically
referenced”
I:: eep
the
are
which
nodes
the
nodes
(i.e.
greatest
nk.kmber
of
surraounded
by
the
main
criterias
used
are
the
arcs).
The
these
given
first
chosen
90
number
number
node 7
specialization
of
of
the
specific
intersection
constraints,
hierarchy.
and
nodes,
attributes
and
the
depth
the
of
multivalued
independency
each
union
the
of
attributes
the
different
detected
ex amp 1 e:
For
IF X HA!?HLRETIW 3 SPECIAIZ~~TIDI ENTITIES
MD THESESPECIlyIsIlWNS HAVENOSPECIFICATTRIBUTES
kNDTHESESPECIIyIZA~IoNsDOMJTPMTICIPAlE TOWY f6SOCIATIoN
ANDTHEREIS Ml IHlERsECTIoN
BETWEEN
THESESFECIIYJSAWMS
ANDTH w~ac DFSPECIALIZATIMCLRssEsIS EBW. m TH
SENEfWIZATION
CLASS
MN ADDA NEW&~IBUlE NMED“ROLE’TO THE %5REEAUJN OF X
WHICH
DCMN IS THESEWNCEOFWtES ff THE
SPECIALIZATION
ENTITIES,
DELETETH spECI#IZ6~IoN ENTITIESOF X.
‘This
rule
5.2.2.
illustrated
is
The
acquisition
cardinalities
certain
First,
given
specification
quest
i on-answer
.following
one
of
figure
or
i ng
dialogue
dependencies
However,
approach
is
the
possible
dependencies
two
not sufficient
multivalued
to
detect
ali
dependencies.
lob.
aggregation
cardinalities
the
by
in
as
an
sequences
of
(or
as
but
two
merged
objects),
this
phenomenon
is
during
the
previous
and
solved
non-trivial
and
multivalued
not
hold
further.
does
see
later
that
this
we
shall
of
end-user
are
in
acquired
dialogue
such
his
by
as
a
the
:
SECSI) CCULD
MY Tm
USER !YES.
SECSI 1 CtUD ANYm
USER ( YES.
SEeSI )IsMPHM-DEPEWW
tkW WERAL ADDRESSES?
F==.
11:
tW’E SEVERALPHaE WBE!W
Exam
les
jzransformation
of
Iqqreqati
structural
of
on
DNWEIWGS?
USER ! No.
5.2.3.
SECSI? MID INVERSO-Y?
USER<YES.
SECSI>FOR~RDDRESSISMRE~ORSEMR#TEIWLRS!
USER < B#k
.s......**.
constraints
cardinality
Some
other
functional
from
the
may
be
inferred
example,
if
the
For
dependencies.
functional
dependency:
NAME ( DEPARTMENT
1 -- ::4DDRESS
(DEPARTMENT)
in
the description
and
if
the
is
given
then
only
one
name,
has
departement
department
has
the
that
SECS I
infers
nnly
one address.
At
end
of
this
the
has
transformed
the
the
semantic
network)
normal
form
the
first
transformation
applying
illustrated
in
figure
11.
system
(i.e.
dialogue,
the
base
of
facts
and
provides
relations
rules
as
dialogue
is
prevents
it
by
those
very
some
because
dependencies
to
occur.
In a
prepares
the
schema
certain
sense,
it
it
may appear
as a
.for
being
in 4NF. But
“normal
ixe
in
approach
to
surpri5ing
form
normal
first
4NF I’
the
during
we interprete
the
Indeed,
if
process.
The
:i mportant
mu1 tivalued
previous
The
acquisition
dependencies
Functional
acquired
(1)
the
application
from
of
functional
dependencies
four
different
user
‘5
description
can
sources:
be
of
explicitely
the
specifies
certain
functional
dependencies,
(2) the cardinalities
of
the
aggregation
enable
arcs
the
system
to
inf ere
functional
dependencies.
For
example,
if
only
one SSN and only
an
EMPLOYEE
has
and
for
each
SSN there
is
ADDRESS,
one
infers
then
SECS I
the
one
EMF’LOYEE,
dependency
SSN-::.ADDRESS.
f r~nctional
direct
application
of
the
This
is
a
transitive
dependencies
plays
a
in,ference
if we
role
of
rule
of
assume
that
attribute
an
SSN --I::.
EMPLOYEE
and
EMPLOYEE--?
then
SSN -->
ADDRESS,
(3) a dialogue
with
the
end-user
cardinalities,
for
possible.
As
asks
questions
of
the
form
:
functional
EMF’LOYEE
:
if
ADDRESS
is
also
SECSI
!ECsI > IKES TIE WE ff EWLOYEEDETEPMtEHIS SkiMY?
USER <Ml.
SEC’3 > Ml rylK IWD ADDRESS
OF EtWYEE DETERJGNHIS SALARY?
.*.......*
the
system
is
this
dialogue,
During
5.2.4.
by Armstrong"s
inference
rules
directed
der i ve
SECSI
to
new
which
enable
.functional
dependencies
from
those
given
The
system asks questions
user.
tJY the
for
those
functional
dependencies
only
derive.
However,
even
in
not
could
it
this
dialogue
phase
may
case,
this
tedious
and
very
appear
as
somewhat
Thus
user.
the
for
tiring
instead
process
is considered
as partial
it concerns
only the attributes
functional
dependencies
of a unique
entity
and
it does not handle
functional
dependencies
between
attributes
of
different
entities
which are not already
acquired.
This
process
is also called
partial
as
it
is
only
applied
for
entities
which do not appear
as targets
of
association
arcs
(r-1.
of
This
on
TEACHER
the
tuples
of
that
the
infers
SEC31
functional
dependencies
do not
-->
ADDRESS,
ADDRESS
-->
TEL,
-->
NAME,
NAME -->
TEL,
-->
ADDRESS.
of
possible
candidate
number
reduced.
However,
from
is
estension
of
the
relation
TEACHER,
can
say
nothing
about
SSN-->NAME,
avoid
way
to
>ADDRESS,
. . . Another
5.2.5.
MNIW COVERING
ff
Acquisition
Dependencies.
of
produces
above
cardinalities
SECS I
infers
The
LEER’S tCULENTW
INFLRWTION
12:
attributes
very high.
Acquisition
cardinalities
is
of
not
the
the
not
association
5ECSI)I#YEMHf'RWES4IRDERESPDNSIBLEffoKYaE
msEvERALwusEs?
USEN(SMIWL.
sEcsI>IyIYExHcRRsEWEoKYMcRWEWLRE5aNsIBLEs?
MN (ON.
SEC51
>WESfWCtWEEXISTYITHOUTAREPDNSIW?
USER<Ml.
... .......
determines
the
(m,n)
This
dialogue
values
from
which
SECS I
coup 1 es
of
functional
and
multivalued
inf et-s
some
dialogue
For
dependencies.
ex amp 1 e , the
!
Fis.
remains
of
language
algorithms,
acceptable
as
of an entity
is
Association
cardinalities
are either
in the initial
description
of
the
application
or
interactively
acquired
from
end-user
with
the
following
the
dialogue:
FWCT. DEwmENc.
EXW!B
Prolog
type
of
this
given
tVSTMM'S
MEREWERUB
b
the
to
generally
SECSI
TEL-combinatory
explosion
in functional
the
to
reduce
acquisition
is
dependencies
left
in the
attributes
number
of
the
functional
dependencies.
side
of
hand
does not practically
appear
Indeed,
it
as an important
constraint
to limit
this
attributes.
five
to
four
or
number
functional
the
Figure
12 synthesizes
dependency
acquisition
principle.
I=
of
dialogue
is based
of
the
CREER791.
phase,
ho1 ds ,
keys.
Al though
adapted
efficiency
number
NAME
ADDRESS
(SSN,NFItlE)
Thus
the
dependencies
r-L
algorithm
previous
a
applies
possible
these
ENTITYMlRImITES
CARDINkITIES4
process
version
second
functional
dependency
the
membership
algorithm
which
consists
of
testing
whether
a
functional
dependency
is implied
or
not
existing
in the base
of
by those
already
facts.
Then
the
minimal
covering
is
progressi
vel y built
third
and
normal
form
relations
with
all
their
deduced
whenever
SECS I
(5 TUPLES
ATMISTl?
2224775
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(1234 DWrBT tk%EILLE 662532
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( .
this
normalization
the
synthesinzing
the
During
OF THE REL4TIoN TDW3t!%N,~,~,TEL),
hold:
process
This
sEc.SI~W~SE,~XUDYW~I~E~~~EEX~SWTUPLES
From
normalization
because
and
functional
possible
for
searching
we try first
to
search
for
dependencies,
is done
This
dependencies.
impossible
with
the
help of some examples
Of tUpleS
es amp 1 e ,
For
end-user.
given
by the
SEC!31
asks the following
questions:
relation,
,following
Partial
couples
two
(O,N)
and cl,11
from
functional
dependencies:
I-- 3~ ey ( PROFESSOR)
.
1::ey (COURSE
variable
key
corresponding
the
normalization
becomes
system,
Functional
couple5
92
a
he
of
is
later
I:: eys
process.
familiar
introduce
may
cardinalities
little
replaced
found
If
in
the
with
directly
to avoid
of
which
by
the
user
the
his
the
dialogue.
preceding
on
the
considered.
deci si on
of
suppressing
arcs
depends
on the
number
of
arcs
involved
in each
association,
the
cardinality
of each
association
arc
r ,
and
the
number
of
attributes
of
this
association.
When
associations
are
organized
into
a hierarchy,
a meta-rule
specifies
the
strategy
to
search
this
hierarchy.
Figure
13
shows
some
transformation
rules
depending
on the
cardinalities
of
r.
Whenever
an arc
r is
~1 i mi nated,
a referential
constraint
is
created
between
the
association
and
the
involved
entity,
or
between
the
involved
The
5.3.
13:
Examples
of
5.2.6.
Complete
of
When
which
in
process
A set
dependencies
are
partial
the
the
efficiency
as
than
generally,
two
or
three
results
design
process
terminated,
results:
we
descr
obtain
i bed
the
basic
relations
in 4NF and
keys
of
relations.
these
Figure
14
shows
the
normalized
relational
schema
produced
from
the
university
example
portrayed
in the
same
.f igure.
Notice
that
in the
results
some
new
attributes
appears
(e.g.
TEACHER.ROLE
and
STAFF.ROLE)
which
were
not
in
initial
They
the
description.
have
been
created
to
replace
specialization
entities
which
have
been
suppressed
during
the
action
5.2.1
of
.t h e
design.
Some
other
attributes
are
dupl i cated
in
different
relations;
they
replace
the
association
arcs
r that
have
deleted
in the
design
action
5.2.6
been
These
attributes
prefixed
are
by the
.f irst
three
characters
of
the
name
of
the
entity
from
where
they
have
been
derived
(CLA.NUMBER,COU.NQME,STU.NUMBER)
nr
by
the
association
which
has
caused
the
attribute
migration.
Also
in the
same
ex amp 1 e , there
are
some
surprising
names
of
relations
FREE-GIFT-STAFF,
ADDRESS-TEL-TEACHER
coming
from
the
normalization
process.
These
will
later
be
renamed
with
the
user’s
help
(for
example
put
LOCATION
instead
of
ADDRESS-TEL-TEACHER.
The
key(s)
of
each
relation
are specified.
As for
relation
attributes
composing
names,
some
the
C::eys may be prefixed
by entity
names.
(1)
the
The
of
association
arcs
suppression
attributes
from
moves
one
entity
to
another
introduces
new functional
and
and
multivalued
dependencies
that
make
some
relations
not
normalized.
Thence,
to
proceed
anot her
SECS I
has
the
normalization
process
based
on
CFAG177,
decomposition
algorithm
This
process
concerns
all
the
ZANI811.
entities
which
are not
yet
normalized
by
partial
normalization
process
(i.e.
the
of
r
are
the
targets
entities
which
arcs).
principle
The
eliminate
to
and
functional
the
left
whose
relation.
of
the
but
the
relation
the
is
,following
structural
associations.
normalization
final
The
above
entities.
FiQ.
in
AS
normalization
process,
remains
acceptable
have
not
more
entities
dozens
of
attributes.
association
transformations
order
of
various
(2)
A
de-f inition
set
virtual
relations
and
the
corresponding
which
permit
to
relational
database
real
the
der i ve
relations
virtual
relations.
correspond
entities
given
in
the
have
which
and
initial
description
the
design
process.
during
disappeared
with
respect
to
the
user,
these
However,
F’ERSON,
EMPLOYEE
) which
(e.g.
objects
real
world
must exist
in
exist
in
the
schema
exactly
as other
the
conceptual
that
Notice
(STUDENT,COURSE).
objects
entities
are nnt
transformed
all
the
virtual
replaced
necessar
i 1y
by
some
of
them
are
replaced
by
relations;
INSTRUCTOR,
(e.g.
attributes
role
sometimes,
both
However,
PROFESSOR).
these
algorithms
is
the
pro-j ecti
on
all
multivalued
dependencies
hand
side
is not
the
key
The
process
is finite
schemas
obtained
depend
of
by
93
of
of
queries
them
from
These
to some
the
virtual
necessary
relations
to
capture
world
(e.g.
real
the
ex amp1 e
the
relational
represented
by
and
the
portrayed
queries
roles
semantics
are
generated
In
HEAD-OF-LABO).
figure
14,
are
simply
operators.
relational
wNsm1NTs
keyEtRCUED) : CiJ+MBlcws(IyESkey(TERDER) : SSN
key CiWUW : SSN
key(SNDMI) : NUtl68?
keyUMtSE) : WE
keyKLAS9 : CW-WE NWBER
key(DEPMtiENT) : WE
keyWREE-RIl+STRFF) : !iTA-SSN FREE-GIFT
keyMDDFESS-EL-TERCtERI : TECSSN I\wREss
(3)
A
set
)
Domain
for
relevant
during
roles).
other
the
new
design
Referential
like
general
the
&ND
FURTHER
described
the
main .featut-es
database
design.
written
in PROLOG and
at
INRIA.
The
main
of the system
are
:
does
integrate
a
complete
ogy
for
database
design,
from
a naive
description
of
the
and
using
intensively
with the
end-user.
for
strongly
which
is far from
being
the
system
points
have
to
be
Many
including
the
graphical
interface,
expert
the
the
design
algorithms
and
the
explanation
of
Further
steps
which
are
the decisions...
addressed
in
the
current
not
Yet
are the view integration
implementation
New versions
design.
physical
and
the
these
aspects
are
currently
integrating
in
specification.
However,
run
results
al ready
substantial
The
achieved
with
the
first
version
of
SEES1
lead
us to
state
that
expert
systems
are
to
database
design.
They
suitable
very
in the
style
design
new
introduce
a
dialogues,
the
directing
of
manner
i nconsi stencl
es
and
the
correcting
also
results.
They
the
justifying
domains
semantic
constraints
attributes
world
essential
rel at i on al
be
done
complete.
improved
interface,
lEMm.ssw
constraints
and
real
based on a semantic
is
implemented
as a
semantic
network
in the system.
(3)
It
encompasses
most
of the *simple
database
theory
about
design
(e.g.
normalization,
dependency
inference
rules
. . . 1 which
is expressed
as PROLOG
clauses.
(4)
It is evolutive
in
the
sense
that
we
can
add
new design
rules
in the system.
(5)
It
is
a too1
integrated
in the
relational
SABRE
in
DBMS
order
to
facilitate
database
design
and
creation.
STIYFINAI’EI I
examole
of
aaplication
with
SECSI.
of
have
expert
system
system
is
on
MULTICS
starting
application
dialogues
(2)
It
is
data
model
t) oTtER!zwW1cccWSTRfWrs
cam!iE.NI-ssN= TEmER.ssNMIDTEKtER.RaE= 'PROFESSOR'
aASs.TEn-SSN=TEIY)IER.SSNANDTEMxR.RaJ='INSTRUCmR'
referential
constraints.
an
method01
ttREmMn1llLfWD IKIIJSIONcMn?AINTS
EwaLED.= cLAss.NIlER
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ENmLED.= !amNT.m
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sTIyF.DEP* = DEFRRT?ENT.FyyIE
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(1)
It
STIYFXILE = ( DIR-OF-L&R0 HEAD-IF-SECTI~
)
TEi3lXR.RU-E = ( DIR-DF-LARD INST!ilXToR PROFESSOR)
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Fis.14:
REMARKS
DIRECTIONS
This
tt lKMINCWNWINlS
q
&CONCLUDING
RESEARCH
runs
! TEMtER.RoLE
= 'DIR-DFJf&Ul~
)
RDDRESS-TEL-TEmER.TER-SSA
be
We
= REST( JOIN1 STWF TERCtER
I STIyF.RU = TEKtER.RfU
are
which
cannot
database
without
tables
efficiently
cannot
of
VIRTW REUTIONS
DIR-ff-L&U
They
integrity
maintained.
Semantic
constraints
are
all
other
constraints
composed
of
a conjunction
or disjunction
of
predicates
and
which capture
a given
semantics
graphically
expressed
in the
semantic
network
or
in
the
user
’5
application
in
general.
All
these
are
expressed
in a specific
constraints
described
in
CSIMO841,
that
is
1 anguage
the
language
of the SFIBRE system.
EJaluED(cLIHuneER-s-IwlTE)
TEfmER(DEp-NcyL~YIwE~~)
SWfF(W-‘-Nf#E%UU?Y~SSNST~)
.5WDENT(NWNuneERf
lJltX!Z(TEA-SSNRWnDRYHRIE:HOUR)
cuIss(MmER-TEft~1
DEPMENT(ADLRESSWdE)
FREE-RIFT-STIYF ( STR-523 FREE-GIFT )
ADDRESS-TEL-TEmEFi( TEHSN TEL RDDRESS1
PERW= LMlN( STlJDENTLtWEl~IWNEI
EmmE= WJN( SWF lEKzli3)
the
replace
to
associations.
information
joins
of
of
are
generated
procesjs.
(especially
are
dependencies
94
:i. nt reduce
restructuring.
mpen
new
teaching.
new capabilities
Expert
for
systems
possibilities
in
database
may also
database
BARR
CBADA811
Representation
of
AI, Barr
of
$ Fei
Stanford
DEER I
J.
DAVIDSON
(in Handbook
ed. , Comp.
See
A.
Knowlidae
genbaum
U;ivercity)
. 1
BERNSTEIN
P. A.
related
to the
of
normal
form
relation
schemes”
d2i
qn
vol4,nbl,
Databases,
ACM
Transact
On
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