policy that satisfies the customer's needs and has an affordable premiun. ... relieve a bottleneck which seriously impedes service quality,. QI the other hand, such a ... In a rule-based expert system, the "inference-engine" uses a set. o f r u l e s .... execution of a progran involves a depth-first search with backtracking on these ...
COUPLING EXPERT SYSTEMS WITH DATABASE MANAGEMENT SYSTEMS
Matthias J a r k e Yannis V a s s i l i o u
May 1983
C e n t e r f o r Research on Information Systems Computer A p p l i c a t i o n s and Information Systems Area Graduate School o f Business A d m i n i s t r a t i o n New York U n i v e r s i t y
Working Paper S e r i e s CRIS #54 GBA #83-53(CR)
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
ABSTRACT
The combined u s e o f Database Management Systems (DBMS) and A r t i f i c i a l I n t e l l i g e n c e - b a s e d Expert Systems (ES) i s p o t e n t i a l l y v e r y v a l u a b l e f o r modern b u s i n e s s a p p l i c a t i o n s . The l a r g e body o f f a c t s u s u a l l y r e q u i r e d i n b u s i n e s s i n f o r mation systems can b e made a v a i l a b l e t o an ES t h r o u g h an e x i s t i n g commercial DBMS. Furthermore, t h e DBMS i t s e l f can b e used more i n t e l l i g e n t l y and o p e r a t e d more e f f i c i e n t l y i f enhanced w i t h ES f e a t u r e s . However, t h e implementation o f a DBMS-ES c o o p e r a t i o n i s v e r y d i f f i c u l t . We e x p l o r e p r a c t i c a l b e n e f i t s o f t h e c o o p e r a t i v e u s e of DBMS and ES, a s w e l l a s t h e r e s e a r c h c h a l l e n g e s i t p r e s e n t s . S t r a t e g i e s f o r p r o v i d i n g d a t a from a DBMS t o an ES a r e given; complementary s t r a t e g i e s f o r p r o v i d i n g i n t e l l i g e n c e from an ES t o a DBMS a r e a l s o p r e s e n t e d . F i n a l l y , we d i s c u s s a r c h i t e c t u r a l i s s u e s such a s d e g r e e o f c o u p l i n g , and combination with q u a n t i t a t i v e methods. A s an i l l u s t r a t i o n , a r e s e a r c h e f f o r t a t New York Univers i t y t o i n t e g r a t e a l o g i c - b a s e d b u s i n e s s ES w i t h a r e l a t i o n a l DBMS i s d e s c r i b e d .
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 2 1 .0 INTRODUCTION
I n t h e m i d - s i x t i e s t h e world watched w i t h f a s c i n a t i o n off
the
fishing
of
town
Surtsey, o u t o f its ashes. particular
creation
Heimaey,
Iceland,
create
its
Such t h i n g s had happened b e f o r e , and
process
creation
but
volcano
a new i s l a n d ,
went on f o r four y e a r s .
seemed
determined to s t a y .
this
B u t , u n l i k e on
p r e v i o u s o c c a s i o n s , t h e new i s l a n d d i d n o t v a n i s h i n t h e after
a
sea
shortly
S t i l l , it i s a
r a t h e r t i n y l i t t l e i s l a n d made v i s i b l e t o t h e m r l d m a i n l y b y t h e
big
v o l c a n i c c l o u d s t h a t accompanied its c r e a t i o n . Some p e o p l e began t o p o p u l a t e t h e arose
new i s l a n d
and
the
problem
how t o p r o v i d e them w i t h t h e n e c e s s a r y t h i n g s o f l i f e f'rom t h e
n e i g h b o r i n g is1a n d s ( I c e l a n d being t h e most i m p o r t a n t ) , a'l t h e hand,
researchers
from
other
a l l o v e r t h e world f l o c k e d i n t o u n r a v e l t h e
hidden t r e a s u r e s o f t h i s new p i e c e o f l a n d i n t h e hope t o c l a r i f y and r e s o l v e some o f t h e problems i n o t h e r a r e a s .
Currently, fishing boats
manage t h e t r a f f i c b u t soon something more s o l i d w i l l be r e q u i r e d cope w i t h t h e growing i n t e r a c t i o n .
L
What d o e s a l l t h i s h a v e t o d o w i t h e x p e r t s y s t e m s and The
reader
might
have
noticed
a
strong
Once
catch the
a
the
past
few
o f f s h o t o f a r t i f i c i a l i n t e l l i g e n c e , t h e y now
small
spotlight
databases?
resemblance o f S u r t s e y t s
d e v e l o p e n t w i t h t h e emergence o f e x p e r t s y s t e m s o v e r years.
to
of
publicity
after
a
prolonged
period
of
1a b o r a t o r y e x i s t e n c e .
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Much r e s e a r c h h a s been n e c e s s a r y t o commercial
feasibility.
improve
expert
systems t o
With t h e a c t u a l i n t r u s i o n o f e x p e r t systems
i n t o t h e b u s i n e s s m r l d , however, t h e f u r t h e r question a r i s e s how t o i n t e g r a t e them i n t o t h e e x i s t i n g manag anent i n f o r m a t i o n systems Can b r i d g e s ( o r a t l e a s t f i s h i n g boats) be subsystems
that
built
to
( MIS).
the
other
s u p p l y t h e e x p e r t system with t h e n e c e s s a r y b u s i n e s s
to
d a t a and a l l o w them
use
corporate
planning
models
and
other
mathematical models a s a human e x p e r t m a d ? Can, v i c e v e r s a , t h e existing
MIS w i t h
more
expert
system
intelligence
idea
be
used
to
enhance
i n systems d e s i g n , u s a g e , and
operation? Researchers kiho have addressed t h e s e p r o b l a n s t y p i c a l l y looked a t c e r t a i n a s p e c t s o f one o f t h e t w o q u e s t i o n s .
In t h i s p a p e r , we t r y t o
view them t o g e t h e r , w i t h a primary f o c u s on t h e i n t e r a c t i o n o f systems
with
large
existing
p i c t u r e , we t r y t o o u t l i n e a allows
for
two-way
traffic
databases. b r i d g e between
expert
To remain i n our o r i g i n a l the
t
systems t h a t
enhancing t h e qua1 i t y and e f f i c i e n c y o f
b o t h t y p e s o f systems.
'
?he p a p r w i l l analyze on a h i g h l e v e l o p p o r t m i t i e s and s o l u t i o n
s t r a t e g i e s f o r g e t t i n g b u s i n e s s d a t a k o m a commercial d a t a b a s e system i n t o an e x p e r t system, and f o r p-oviding e x p e r t knowledge t o systems.
For more t e c h n i c a l d e t a i l s o f t h e first problem, t h e r e a d e r
i s r e f e r r e d t o a companion paper [ V a s s i l i o u e t a l .
mrk
in
database
19831 and to
some
t h e Japanese F i f t h Generation Computer p - o j e c t [ K u n i f u j i and
Yokota 19821.
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 4 C e r t a i n a s p e c t s o f t h e second problan a r e addressed in [ J a r k e and Koch 19831, others
[ ~ a r k e and
( e .g
C ~ i n g19791,
.,
Shalev 19831, a s well a s i n t h e m r k o f many
C Chang 1 978 I ,
[ R e i t e r 1 978 1,
[~enschen e t
a l . 19821).
[Kellogg 1 981 I ,
However, t h e c o n t r i b u t i o n o f
t h i s paper i s t o m i f y a l l these approaches i n a common franemrk
of
we
what
architectural
b e l i e v e might b e t h e paradigm of forthcoming
advanced d e c i s i o n support systems. For an i l l u s t r a t i o n , we w i l l use from time t o time exanples an
ongoing
project
at
York
New
from
University i n which a logic-based
business e x p e r t system is developed and i n t e r f a c e d
with
an
existing
r e l a t i o n a l database and mathematical subroutine l i b r a r i e s . ?he p a p r i s organized a s review o f major
follows.
subsystems and
Section
requirements
gives a
2
in
brief
a business,
In
sub sequent s e c t i o n s , we study t h e i n t e r a c t i o n between these components focusing
on
knowledge stage-wise
the
b a s e s and
inference
approach t o
e x p e r t system. system
re1ationships
the
between
d a t a b a s e s and e x p e r t system
engines.
systems.
Finally,
technical
problans o f
the
section the
3
presents
a
question how t o g e t d a t a f o r a b u s i n e s s
Section 4 d e a l s with v a r i o u s
technology t o
Section
design,
5
use,
addresses
coup1 ing
applications o f and some
between
expert
operation o f d a t a b a s e architectural expert
and
managanent systems t h a t have t o b e solved r e g a r d l e s s o f t h e
and
database directior!
of the interaction.
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 5 REVIEW OF BUSINESS SUBSYSTEMS AND REQUIRMENTS
2.0
i s l a n d st' to b e connected i n a b u s i n e s s w i l l
In t h i s s e c t i o n , t h e be
identified,
type
of
current
transaction
business
processing,
systems i s characterized
governed
databases.
Ihe
by
backbone
knowledge
required
for
centralized
or
building
and
working with such systems i s t y p i c a l l y hidden i n procedural cannot
be
easily
applications. office
changed
nor
carried
Modern t r a n s a c t i o n processing
automation
systems,
make
One
rules o f what can b e c a l l e d a
by
b u s i n e s s program, and t y p i c a l l y c e n t e r e d around l a r g e distributed
.
and t h e i r i n t e r a c t i o n requirements i n v e s t i g a t e d
these
form* and
to o t h e r
over system
types,
r u l e s more
similar such
explicit
formalize t h e n o t i o n o f d o c m e n t s a s a c e n t r a l c a r r i e r o f
as and
information
t o b e handled [ T s i c h r i t z i s 19821. Another supporting
type
of
decisions
business on
s y s t e m s d e a l s more levels.
various
d i r e c t l y with
Decision s u p p o r t s y s t e m s
o f t e n have t h e i r knowledge b u i l t i n mathematical formulas which
and
models
a r e handled by model managanent a l g o r i t h m s o r b y t h e u s e r v i a a
f l e x i b l e and m w e r f u l user i n t e r f a c e [ S t o h r and White 19821. F i n a l l y , t h e r e i s a l a r g e nunber o f ex per i e n c e databases
and via
hunan
specialists
rjho
use
ex per t i s e t o g e t h e r w i t h f a c t u a l knowledge gained from
query
1ang uag es
to
develop
explanations i n t h e i r area o f .expertise
.
recommend a t i o n s
Recently, e x p e r t s y s t e m s t r y
t o e f f i c i e n t l y s u p p o r t o r p a r t i a l l y r e p l a c e such s p e c i a l i s t s . systems t y p i c a l l y o r g a n i z e
their
knowledge
r u l e s and perform m e form o f p a t t e r n
and
matching
Expert
i n t h e form o f i f - t h e n
to
find
out
which
rules apply.
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 6 To sunmarize t h i s d i s c u s s i o n , it can be said t h a t businesses use: l a r g e databases,
(1)
mathematical
(2)
formulas and
(3)
models,
business r u l e s and forms (docunents), ( 4 ) experience and expertise various
human
specialists.
w i l l be another type o f
of
If e x p e r t systems a r e introduced, t h e r e
subsystem
which
interacts
with
the
hunan
d ec ision-maker s t o complement t h e i r e x p e r t i s e . In t h i s kind of s e t t i n g it i s n o t hard to see t h a t much e f f o r t i s
duplicated i f a l l these systems operate independently o f each o t h e r . Today, most mathematical and expert manag anent
data
facilities.
subsystems have
This
own
If d a t a from a database system are used
they a r e t y p i c a l l y extracted a s snapshots before the session.
their
beginning
of
a
approach causes p r o b l a n s i f t h e amountof d a t a t o b e
extracted is l a r g e o r cannot e a s i l y b e determined in advance,
If
the
snapshots a r e kept over an extended period of time, t h e r e i s a problem of keeping them c o n s i s t e n t w i t h t h e main database. Database ( e.g
.,
managanent
aggreg ation)
systems u s u a l l y o f f e r
capabil i t i e s i n
e l anentar y r ule-based
techniques
their
However,
operations.
sophisticated mathematical user. built
retrieval
( integrity
often
facilities
operations on
the
their
the
which
data
some mathematical
user
i n t e r f a c e s and
constraints) user perform
muld
to
check
like
more
reasoning
or
before presenting them to the
A higher l e v e l o f semantic knowledge and deductive c a p a b i l i t i e s
into
the
database
system
muld
not
o n l y make
it
more
user-friendly but a l s o s a f e r and more e f f i c i e n t t o operate.
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 7 F i n a l l y , on a higher
it
level,
can
observed
that
system
e f f o r t i s d u p l i c a t e d or! a l a r g e s c a l e i n t h e a n a l y s i s and
developnent
d e s i g n o f b u s i n e s s i n f o r m a t i o n systems. anong
be
This happens within b u t
also
systems o f t h e same t y p e a r e developed s a i n
o r g a n i z a t i o n s *en
and g a i n without t a k i n g e x p l i c i t advantage o f knowledge from previous ex per i e n c es. Some r e s e a r c h h a s been done t o i n t e g r a t e t h e s e p a r a t e subsystems. Decision
support
systems h a v e
c a p a b i l i t i e s on cost-effective.
solid
t o b a s e t h e i r mathematical modelling manag anent
database
technology
to
the
become
By analogy, i t can b e argued t h a t many e x p e r t systems
which use a l a r g e population o f s p e c i f i c f a c t s need channel
to
a
communication
Going a s t e p f u r t h e r , we argue
corporate databases,
t h a t f u t u r e d e c i s i o n s u p p o r t systems w i l l have t o i n t e g r a t e a l l components:
d a t a b a s e , mathematical subsystem, and knowledge b a s e .
A s an e x a n p l e f o r consulting The
system
insurance
three
expert
system
develops, policies
such
for
a
system,
currently
recommends, a
life
insurance
under d e v e l o p n e n t i n our group.
and
customer.
a
consider
explains
customized
life
The system w i l l a s s i s t a s a l e s
agent by ( a) e x t r a c t i n g customer information from t h e
corporate
database
and from i n t e r v i e w s t o deduce t h e n e e d s f o r l i f e i n s u r a n c e c o v e r a g e i n d i f f e r e n t s t a g e s o f t h e l i f e o f a customer; (b) c l a s s i f y i n g t h e r e q u i r e m e n t s types o f
in
terms o f
basic
actuarial
i n s u r a n c e , and re1 a t i n g them to appl i c a b l e a c t u a r i a l models J
i n t h e mathematical subsystem;
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 8 (c) computing a premiun f o r t h e customized p o l i c y , reducing it b y
already
existing
coverage from previous i n s u r a n c e , and canparing t h e
remainder to t h e premiun paying c a p a b i l i t i e s o f t h e customer; ( d l analyzing t h e f e a s i b i l i t y o f t h e proposed p o l i c y i n terms o f l e g dl r e q u i r e m e n t s and c o r p o r a t e o b j e c t i v e s ; ( e l i n t e r a c t i n g with t h e customer to come up with policy
that
satisfies the
customer's
needs
an
acceptable
and h a s an a f f o r d a b l e
premiun. C u r r e n t l y , customized p o l i c i e s a r e f e a s i b l e o n l y f o r major policies
since
there
is
process a s o u t l i n e d above. choose
among
( insurance
a
limited
products)
.
a
lack
group
o f a c t u a r i a l experts t o support a
I n d i v i d u a l customers can e s s e n t i a l l y o n l y nunber
o f p r e p a c k a g e d p o l i c y combinations
When o p e r a t i o n a l , t h e ex p e r t system
will
thus
r e l i e v e a b o t t l e n e c k which s e r i o u s l y impedes s e r v i c e q u a l i t y , QI t h e o t h e r hand, s u c h a system needs:
( a ) information about customers and a c t u a r i a l t a b l e d a t a
from
a
d atab ase system; (b) a mathematical sub system to
execute
actuarial
canputations
efficiently; (c) an ( ex t e n s i b l e ) knowledgebased ex pert subsystem information,
to
ex t r a c t
t o c l a s s i f y insurance needs, t o check t i m e v a r y i n g l e g a l
and c o r p o r a t e c o n s t r a i n t s , and to e x p l a i n p r o b l a n s and a l t e r n a t i v e s .
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 9 We a r e implementing Prolog,
a
system
using
the
logic
language
database system, and a mathematical subroutine
Tnis p r o j e c t i s meant t o
library. typical
relational
such a
serve
as a
demonstration
of
a
we expect f o r upcoming knowledge-based business
architecture
d e c i s i o n support systems.
In t h e remainder o f t h i s paper, we s h a l l focus on t h e i n t e r a c t i o n of
t m
of
the
subsystems discussed
knowledge-based e x p e r t
systems and
in
this
databases.
section, First,
we
naanely, explore
a l t e r n a t i v e s o f e x t r a c t i n g d a t a fiom l a r g e and/or e x i s t i n g d a t a b a s e s g e t t i n g supply t o t h e i n h a b i t a n t s o f t h e new i s l a n d . turn
our
attention
the
other
way:
Later
on,
-
we
how can e x i s t i n g subsystems,
e s p e c i a l l y database management systems, b e improved when e x p e r t system technology becomes a v a i l able?
3.0
DATA FOR EXPERT SYSTEMS I n a rule-based
of
rules
(the to
(database)
expert system, t h e "inference-engine"
base) knowledge simulate
the
and
behaviour
uses a
set
a collection o f specific facts of
a
hman
expert
in
a
system
of
s p e c i a l i z e d problem domain. For i n s t a n c e , t h e l i f e
insurance
consulting
expert
Section 2 u s e s r u l e s 1ike :
i s a customer o f a c e r t a i n age and when t h e customer d i e s a c e r t a i n m o u n t becomes payable THEN t h e customer i s s a i d to have a *ole l i f e insurance b e n e f i t . IF
C
and a database containing d a t a about a c t u a l customers,
insurance
and
annuity b e n e f i t s , m o r t a l i t y v a l u e s , e t c .
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 10 A database i s represented
variety the
and population.
different
reflect
logic
in
two
terms o f
basic
dimensions:
For example, i n a logic-based representation
predicates
(ncustomern, "covered
by",
etc.)
t h e v a r i e t y , and the i n s t a n c e s o f t h e s e p-edicates ("Smith i s
a customert1, "Smith i s covered by term a n n u i t y t , e t c .) r e f e r
to
the
po pul a t i o n .
b s t ex p e r t system databases e x h i b i t a 1arg e v a r i e t y o f In
contrast,
variable;
the
population
of
ranging from a m a l l
collection
of
Tnus,
facts.
in
facts
such databases i s m o r e
"laboratory" expert
facts.
set
system
to
a very
large
databases d i f f e r from
t r a d i t i o n a l commercial databases i n t h a t they tend t o b e more
"wide1*
and l e s s "deepI1.
In [Vassiliou e t a1 19831 we system
database
representation
exanined and
summaryof our r e s u l t s .
We s t a r t with
in
Then,
approach
Section 3.1.
the
retrieval. an
for
an
outline
problan We
of
expert
now p r e s e n t a
of
a
four-stage
we
illustration
d e s c r i b e i n Section 3.2 a w r k i n g implementation o f t h i s
.
briefly
approach
in
the programming language Prolog.
3.1
Of Database Managanent Developent Four-Stages -
Four s t r a t e g i e s between been
the
deductive
i d en ti fied
retrieval
for
.
establishing and
Starting
a
cooperative communication
d a t a canponents o f an e x p e r t system have kom
e l an en t a r y
f a c il i t i e s
for
data
we progress t o a generalized DBMS within the e x p e r t system,
t o a 'loose1 coupling of t h e ES with
an
existing
cmmercial
DBMS,
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 11 finally,
to
a
t i g h t t coupling w i t h an external DBMS.
Expert system
designers may opt for one configuration over another depending on d a t a volune, m u l t i p l i c i t y o f d a t a use, d a t a v o l a t i l i t y c h a r a c t e r i s t i c s (how frequently
i s changed),
data
requirements.
or
data
protection
,
and
security
Regardless, i n a c a r e f u l design these enhancements a r e
incremental, allowing for a smooth t r a n s i t i o n kom a l e s s
to
a more
sophisticated environment.
-t a g e 1: 3. 1. 1 S
-
Elementary Data Managanent Within The ES
I n the simplest c a s e , a l l d a t a i s kept most1 y ad-hoc data s t r u c t u r e s ,
in
core
and
in
stored
Appl i c ation-specific r o u t i n e s f o r d a t a
r e t r i e v a l and modification a r e implemented.
3.1.2
-Stage
2:
Generalized Data Management Within The ES
When the expert system database i s l a r g e enough n o t core,
elementary d a t a
managanent i s n o t s u f f i c i e n t .
needed for external f i l e managanent directory,
.
e t c .)
(e.g
.
to
fit
in
Techniques a r e
secondary
indexes,
data
These techniques should preferably be a p p l i c a t i o n
independent, s i n c e a s m a l l
change
in
application
d e s c r i p t i o n s may
r e q u i r e an altogether d i f f e r e n t mechanism. Furthermore, depending on the m u l t i p l i c i t y o f the
extent
fact
database managanent f a c i l i t i e s may b e needed.
Such f a c i l i t i e s i n c l u i e
database
required
and
the ES, general purpose
dynamic
variety
use
for
tlviewslt, o r
of
database
windows,
available
in
most
mdern
r e l a t i o n a l D B M S CSQL, INGRES, ORACLE].
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 12 Another f a c i l i t y i s an i n t e g r a t e d d a t a d i c t i o n a r y t h a t allows f o r q u e r i e s about
the
DBMS i n t e g r a t e d
in
database s t r u c t u r e .
In a n u t s h e l l , a generalized
the
may b e
expert
system
necessary t o
deal
e f f e c t i v e l y with 1arg e d atab ase s. This s t a g e o f enhancing database manag anent f a c i l i t i e s be
the
norm
seems t o
for e x p e r t systems t h a t d e a l with l a r g e d a t a b a s e s , even
though n o t a l l such systems e x h i b i t t h e same l e v e l o f s o p h i s t i c a t i o n .
-Stage
3.1.3
3:
Often, t h e need f o r
consulting
existing
very
l a r g e databases
Such d a t a b a s e s w i l l normally b e managed by a commercial DBMS.
arises.
In a t y p i c a l exanple, [Olson and E l l i s 19821 r e p r t
PROBWELL,
experience
with
an e x p e r t system used to determine problems with o i l wells.
A v e r y i m p r t a n t source o f information f o r
1arg e
-
Loose Coupling Of The ESWith An Existing DBMS
database
is a
such d e t e r m i n a t i o n
stored md er the IMS database system.
Un'for t m a t e 1 y,
t h i s database cannot b e made a v a i l a b l e t o the ES i n a t i m e l y manner. Existing e x t e r n a l d a t a b a s e s a r e
typically
v o l a t i l e , and used by s e v e r a l a p p l i c a t i o n s . maintaining
consistency may p r o h i b i t
very
large,
highly
Costs o f s t o r i n g d a t a and
the
duplication
of
such a
database for the sake o f t h e e x p e r t system alone. b o x coupling of an ES w i t h presence o f
a
communication
an
channel
existing
DBMS r e f e r s t o
between t h e t w o systems a i c h
allows f o r d a t a ex t r a c t i o n kom t h e e x i s t i n g d a t a b a s e , and storage o f
this
wsnapshottt a s an
the
expert
system
subsequent
database.
Data
ex t r a c t i o n s occur s t a t i c a l l y b e f o r e t h e a c t u a l o p e r a t i o n o f t h e ES.
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 13 managsnent
We n o t e t h a t t h e a v a i l a b i l i t y o f generalized database facilities process.
within
the
expert
system
may g r e a t l y
facilitate this
After the i n t e r n a l expert system database has been
expanded
w i t h an e x t e r n a l database mapshot, i t can b e accessed a s i n s t a g e 2.
S -t a g e 4:
3.1.4
Tight Coupling Of An ES With An Existing DBMS
The main disadvantage o f loose coupling is t h e
in
cases
where
automated
dynamic
portion i s r e q u i r e d , a r e necessary. different
portions
different
times;
the
addition,
if
external
the
of
the
decisions,
-
non-applicability
a s t o which database
Daring the same ES s e s s i o n ,
external
database may b e
requirements may n o t database
be
many
required a t
In
p-edictable.
i s continuously
updated
, for
instance in databases f o r commodity t r a d i n g or t i c k e t r e s e r v a t i o n , t h e snapshots become r a p i d l y obsolete. Tight coup1 ing of an ES w i t h an e x t e r n a l DBMS r e f e r s t o of
the
communication
channel
use
between t h e t w o systems i n such a way
t h a t t h e external database appears t o the ES a s an own.
the
extension
of
its
Clearly, t h e most important consideration for the implanentation
o f t i g h t coupling is t h e " i n t e l l i g e n t n managanent o f t h e communication channel
-when and how t o
use the channel.
Our suggested s t r a t e g y , which Section 3.2.5,
assumes t h e
mechanism w i t h i n the ES requests
for
data
- an
while
we
describe
in
more
detail
in
e x i s t e n c e o f a high-level, s o p h i s t i c a t e d expert system i t s e l f
- that
collects
simulating the ES deduction process.
expert e x t r a c t s and t r a n s l a t e s t h e
necessary d a t a .
The
ES This
facilities
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 14 described
in
Stages 2
and 3 a r e n e c e s s a r y f o r t h e implementation o f
t i g h t coupling. This completes our general d i s c u s s i o n o f g e t t i n g d a t a i n t o e x p e r t
In
systems.
t h e following s u b s e c t i o n , we d e s c r i b e an a p p l i c a t i o n o f
t h i s stage-wise approach t o the problan o f d a t a s t o r a g e and for
a Prolog-based
systems.
retrieval
ex p e r t system using re1 a t i o n a l database manag anent
Most o f t h e mechanisms described below have been implemented
and
t e s t e d i n experimental . Prolog programs presented in [ V a s s i l iou e t
al.
19831.
Illustration
- Data ----
For A Prolog-based
B r i e f Introduction To Prolog
3.2.1
Expert System
-
Prolog is a progranming language based on a s u b s e t o f f i r s t - o r d e r logic,
the
brn-clauses.
Rowhly,
to
dropping
d i s j u?c t i o n from l o g i c a l consequents, and t a l k i n g only about
definite
an teced ent-consequent r e l a t i o n s h i p s .
this
amounts
There a r e t h r e e b a s i c statements
i n Prolog [Nau 1983 1: :- P.
m ean s
P i s a goal to b e paved
P.
m ean s
P i s an a s s e r t i o n
P :- Q,R,S
m ean s
Q and R and S imply P
A Prolog program is a sequence o f
considered
to
be
clauses
universally quantified.
d e e l a r a t i v e and a procedural i n t e r p r e t a t i o n
.
A
aose
variables
clause
are
has b o t h
a
nus,
P :- Q , R,S
can b e read d e c l a r a t i v e l y :
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 15 P i s true i f Q and R and S a r e true
o r , procedurally: To s a t i s f y P f i r s t s a t i s f y Q and R and S. Since more than one clause may be needed to (goal)
, there
define
a
predicate
i s a corresponding AND/OR graph for each predicate.
The
execution o f a progran involves a depth-first search with backtracking on
these
graphs,
and
uses
the
unification
process
based on the
resolution principle [Robinson 19651. A knowledge base can be represented i n first-order l o g i c formulas
a r e suitably interpreted.
the knowledge representation. that
it
if
the
m e r e f o r e , Prolog may be used for
Furthermore, Prolog has
the
advantage
already has a very powerful inference engine i n place.
unification algorithm used i n Prolog
is more
powerful
than
The
simple
pattern matching algorithms common i n production systems. %
-
Elementary Data Managanent
3.2.2 On
the
enhancements,
simplest
level
s t a g e 1,
represented d i r e c t l y
the
a s the
of
---- Naive
Use Of Prolog
database mole
expert
managanent
population system
-
of
facilities
facts
database.
can
While
be this
approach i s f e a s i b l e for any expert system, using Prolog can take us a step further. progrms,
Since Prolog does n o t
i t can
distinguish
between
data
and
be used both a s a ( r e l a t i o n a l ) data representation
and as a query 1anguage.
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 16 Relational databases can be represented d i r e c t l y i n Prolog 1isting
of
all
instantiated
tuples.
For instance, consider
small
portion
of
a
to r e l a t i o n
predicates corresponding a
as
the
insurance
d atab ase :
customer(3163, s m i t h , 1935, london) customer(3154, jones, 1942, a t l a n t a )
...
covered by( 3 1 63, * term i n sur ancel ) covered-by(3163, *whole l i f e annuity1 )
... -
Q
While general r e l a t i o n a l schemas a r e defined only the
predicate
nmes,
define "generalized viewst1 via additional rules. DBMS t o allow f o r more f l e x i b l e data access.
v a r i a b l e s they respect t o the programming
can
[Date 19821 i n
Views
are
used
in
Prolog r u l e s d i f f e r kom that
with
the
use
of
accept par m e t e r s making them equiv a1 ent i n t h i s
selector
languages
by
can be used a s a pwerful mechanism to
Prolog
t r a d i t i o n a l view mechanisms
implicitly
language
[Mall
et
construct
proposed
.
Consider the following
a1
19831.
for
database
exanples:
-
covered-by-many( Custom-id) : covered by( Custom i d , Benefit1 ) coveredmby(Custom-id, Benefit21, not( Eienefitl = ~ e n e f i t 2 ) . special-customer( C-id) :- not( covered by many(C i d ) ) or ( custom erTc i d ,N 1, Y?, 1ondon) , customer( C-id - ,N2,Y2,paris) 1
.
lhe f i r s t of these Prolog statements can b e read as: with
customer
" A customer
i d Custom-i d ( a variable) i s covered by many b e n e f i t s ,
i f he/she i s covered by a t l e a s t t w o b e n e f i t s Similarly, the second exanple reads:
aich
are
different1*.
-
* * A customer i s special i f he/she
.
is not covered b y many b e n e f i t s and l i v e s i n Paris o r bndonT*
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 17
I n a d d i t i o n t o representing d a t a and view d e f i n i t i o n s , Prolog can also
d i r e c t l y r e p r e s e n t q u e r i e s about base d a t a o r views.
A query i s
simply a g o a l : :- covered-by-many(C-i d ) .
which when executed w i l l r e t u r n in C-i d the customer id o f a
customer
who is covered by many i n s u r a n c e b e n e f i t s .
-- -A -DBMS
Generalized Data Managanent
3.2.3
-
Implemented In Prolop
A f u r t h e r s t e p towards i n t e g r a t i n g the d e d u c t i v e c a p a b i l i t i e s
Prolog
with
of
DBMS c a p a b i l i t i e s can b e taken by implementing a g e n e r a l
purpose DBMS d i r e c t l y i n Prolog
-
s t a g e 2 i n our approach This can
be
done q u i t e e a s i l y , and provides a means o f adding f l e x i b l e and general d a t a access mechanisms t o t h e i n f e r e n c e engine without t h e need f o r
a
compl i c ated i n t e r f a c e t o e x t e r n a l database f i l e s . In order to e f f e c t t h i s s t a g e
i s the
requirement
r e 1 a t i o n a l database. define
definition
in
of
Given such a
ES-DBMS an
coupling,
first
the
internal representation o f a
representation
scheme,
one
can
any nunber o f generalized o p e r a t i o n s t o provide t h e f a c i l i t i e s
o f a DBMS.
Our approach [ V a s s i l i o u e t a l ,
19831
provides
a
simple
way t o s p e c i f y g ener a1 ized re1 a t i o n a l o p e r a t o r s a c t i n g on any r e l a t i o n and s e t
of
attributes.
user-oriented
view
Prolog
programs
map
from
of
logical
simpler,
o f t h e o p e r a t i o n s t o t h e i r implanentation f o r t h e
p a r t i c u l a r database and r e p r e s e n t a t i o n scheme chosen. degree
this
data
This p r o v i d e s a
independence a s i n t h e t r a d i t i o n a l l e v e l l e d
a r c h i t e c t u r e o f DBMSs [Date 19821.
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 18 A more a p p l i c a t i o n - s p e c i f i c
Yokota 19821.
s o l u t i o n i s proposed in [ K u n i f u j i and
[Kowalski 1981 I d e t a i l s t h e use o f Prolog f o r i n t e g r i t y
c o n s t r a i n t s , d a t a b a s e upd a t e s and h i s t o r i c a l . d a t a b a s e s . Another efficiency. s t r a t e g y ( e .g schemes,
advantage
is
It
.,
of
possible
B-Trees)
hashing,
a
etc.
,
and
generalized
t o d e v i s e a more s o p h i s t i c a t e d s t o r a g e perhaps
to
use
19781.
[Tarnlund
a m il i a r y
Ihe
work
indexing
appl i c a t i o n s ,
read
into
the
internal
database
can
be
However, t h e s e s t r a t e g i e s a r e n o t e a s i l y g e n e r a l i z a b l e .
Loo se Co up1 ing Conceptually t h e existing
in
schemes t h a t g u i d e d e c i s i o n s about a i c h p o r t i o n s o f
e x t e r n a l f i l e s should b e devised.
indexing
reported
[ P e r e i r a and Porto 1 982 1 demonstrates t h a t f o r s p e c i f i c clever
is
Prolog
DBMS within
With -
Prolog- based simplest
to
solution
the
--
Re1 a t i o n a l DBMS problem
of
using
d a t a b a s e s i s t o e x t r a c t a snapshot o f t h e r e q u i r e d d a t a from
the DBMS b e f o r e t h e ES b e g i n s t o m r k on a s e t o f r e l a t e d s t a g e 3 of t h e approach.
problems
-
The p o r t i o n o f t h e d a t a b a s e is s t o r e d in t h e
i n t e r n a l database o f t h e ES a s d e s c r i b e d p r e v i o u s l y with Prolog. For t h i s s c e n a r i o to m r k , t h e following mechanisms a r e r e q u i r e d :
1.
Link t o a DBMS w i t h unload f a c i l i t i e s ;
2,
Automatic g e n e r a t i o n o f an ES d atab ase ;
3.
Knowing i n advance which p o r t i o n o f t h e d a t a b a s e is r e q u i r e d f o r ex t r a c t i o n ( s t a t i c d e c i s i o n )
database
kom
the
extracted
.
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 19
3.2.5
T i g h t Coup1 ing o f-a Prolog-based For t h e f o u r t h and f i n a l
implenented
with
Prolog,
stage
-
ES w i t h a R e l a t i o n a l BMS
A - -
of
our
approach
as
has
been
we c o n s i d e r a v e r y l a r g e e x i s t i n g database
s t o r e d under a r e l a t i o n a l commercial
DBMS.
comm~n?ication channel
ES
between
the
The
use
of
the
and t h e DBMS w i l l a s s m e t h e
r e d i r e c t i o n o f a l l ES q u e r i e s , on p r e d i c a t e s
representing
re1 a t i o n s ,
Any such approach i s bound
t o t h e DBMS f o r stored database r e l a t i o n s .
to f a c e a t l e a s t t m major d i f f i c u l t i e s :
naive
t h e nmber o f database c a l l s
w i l l b e p r o h i b i t e v e l y many ( each Prolog goal corresponds t o a s e p a r a t e
DBMS c a l l )
, and
impossible
to
recursion)
it
t h e complexity o f Prolog g o a l s ( q u e r i e s ) may make translate
them
directly
to
DBMS q u e r i e s
(e.g.
.
These d i f f i c u l t i e s can b e executing
database
whenever r e q u i r e d approach o f
by
Prolog
calls the
overcome
rather ES.
than
In
is replaced
by
executing
essence, by
collecting
the
and
them pure
jointly
separately depth-first
a combination o f a d e p t h - f i r s t
reasoning and a b r e a d t h - f i r s t d a t a b a s e c a l l execution [ R e i t e r 19781.
I n p r a c t i c e , we use an analganation o f t h e ES l a n g u a g e om
meta-lang uag e ,
[Weyhrauch 19801. requiring
based
on
the
' r e f 1 ec t i o n
its
pr i n c i p l e'
This allows f o r a d e f e r r e d e v a l u a t i o n o f p r e d i c a t e s
database c a l l s , w h i l e a t t h e same time t h e i n f e r e n c e engine
(theorem prover) o f t h e ES i s m r k i n g
.
Since a l l i n f e r e n c e s a r e per formed a t t h e meta-level of
with
object-level
proofs)
, we
( simulation
a r e a b l e t o b r i n g t h e complex ES q u e r i e s
i n t o a form where some o p t i m i z a t i o n and d i r e c t t r a n s l a t i o n t o a s e t o f
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 20
is
DBMS q u e r i e s
feasible.
Note,
that
t h i s p o i n t i t w u l d be
at
d e s i r a b l e t o have an i n t e l l i g e n t DBMS t h a t c o l l e c t i v e l y o p t i m i z e s execution
of
all
the
queries.
We
shall
discuss
such
the
ideas in
s e c t i o n 4 . 3 , below. The q u e r i e s a r e d i r e c t e d to the DBMS, answers
to
transformed The
ES
can
invocation
are
obtained
and
the format accepted by t h e ES f o r i n t e r n a l d a t a b a s e s ,
continue
i t s reasoning
at
the
object
level.
Each
o f p r e d i c a t e s corresponding to database r e l a t i o n s w i l l now
amount t o an ES database g o a l , r a t h e r than a c a l l to an e x t e r n a l DBMS. By t i g h t coupling, we a r e now
able
to
use
an
existing
large
r e l a t i o n a l database a s an extension o f any i n t e r n a l Prolog database.
4.0
EXPERTS FOR DATABASE SYSTEMS When compared to requirements f o r advanced business
such
as
decision
support
for
managerial
users,
we a
c u r r e n t database
In
managanent systems d i s p l a y a nunber o f weaknesses.
applications
this
section,
h i g h l i g h t t h e s e problans and i d e n t i f y some s t r a t e g i e s how t h e y can
b e overcome by t h e use o f rule-based detail
techniques.
We s h a l l not g o i n t o
about o t h e r a r t i f i c i a l i n t e l l i g e n c e approaches such a s n a t u r a l
language modelling
inter faces techniques
[ H a r r i s 1977, [Brodie
and
Wahlster 1981 I
or
conceptual
Z i l l e s 1980, Brodie e t a l .
19831
which c o n t r i b u t e t o b e t t e r d a t a b a s e d e s i g n and usage.
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 21 4, I
Problems With Current Database Systems With t h e advent o f d e c i s i o n s u p p o r t systems and t h e p - o l i f e r a t i o n
of
computers and d a t a b a s e s i n g e n e r a l , t h e t a r g e t user p o p u l a t i o n f o r
d a t a b a s e query l a n g u a g e s h a s user s t s u c h
changed :
there
a r e more
( potential)
a s managers and a p p l i c a t i o n s p e c i a l i s t s rd?o have a h i g h
d e g r e e o f a p p l i c a t i o n knowledge b u t l i t t l e p a t i e n c e
to
acquire
much
f a m i l i a r i t y w i t h programming c o n c e p t s EJarke and V a s s i l i o u 19821, For such user s t h i g her- l e v el query 1ang uag es a r e r e q u i r e d
which
a l l o w powerful o p e r a t i o n s without denanding major i n t e r a c t i o n s k i l l s . B e s i d e s t h e more ergonomic approach t a k e n generation
[Vassiliou
languages
by
and
the
so-called
J a r k e 19831,
second
reasoning
c a p a b i l i t i e s embedded i n t h e system could a l l o w f o r a wider r a n g e
and
more c o n c i s e formulation o f q u e r i e s . A second problen a r i s e s i f u s e r s want f a i r l y
to
be
performed
language. language
on
Currently, [Schnidt
et
the
data
they
which
can
only
complex
a r e n o t provided b y t h e q u e r y
a
use
database
This i s
not
easy
s p e c i a l i s t s and o f t e n also p r e v e n t s t h e use o f DBMS
non-computer
report
progrmming
a l . 19821 i n s t e a d o f an ad-hoc q u e r y l a n g u a g e ,
and program t h e o p e r a t i o n s on t h e d a t a e x p l i c i t l y . for
operations
generation
f a c i l i t i e s burdening
the
user
with
even
more
programming t a s k s . A t h i r d d i f f i c u l t y w i t h most c u r r e n t
for
operations
performed
in
a context.
p a r t n e r model o f t h e user CWahlster 1981 I.
DBMS i s t h e lack o f
support
The system d o e s n o t have a Neither d o e s it
recognize
t h a t m u l t i p l e u s e r s m r k i n g on t h e system a t t h e same time a s k q u e r i e s
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 22 t h a t could b e a n s w r e d c o l l e c t i v e l y [ J a r k e e t a1 user
who
wishes
to
issue
. 19821.
Finally,
a
a query t h a t i s based on t h e r e s u l t o f a
previous query h a s t o s t o r e t h a t r e s u l t e x p l i c i t l y . Besides n o t being a s user-supportive a s t h e y could systems could
be
improved
be,
database
towards more s a f e t y o f t h e d a t a and more
e f f i c i e n c y o f t h e execution o f read and w r i t e t r a n s a c t i o n s . Safety constraints, control.
i s guaranteed and
in
a
database
by maintaining
it
only rather
to
i s hard
combinations o f c o n s t r a i n t s e f f i c i e n t l y . support
enforcing
consistency through
Howver, many DBMS s u p p o r t
i n t e g r i t y c o n s t r a i n t s because
by
integrity concurrency
simple test
Also, most
types o f
more
complex
systems d o
not
t h e resubmission o f (sequences o f ) t r a n s a c t i o n s a f t e r f a i l u r e
o r user e r r o r s [Gray 1981 I. Efficiency mainly r e f e r s t o queries.
Many systems d o
the
time
response
evaluating
n o t recognize a l l s p e c i a l c a s e s o f query
s t r u c t u r e o r c o n t e n t t h a t permit t h e use o f e f f i c i e n t algorithms.
for
special-pur pose
Neither d o they recognize sequences o f q u e r i e s where t h e
output o f one can b e used a s t h e input o f t h e n e x t
(e.g.,
recursion,
.
focusing , e t c .)
In t h e n e x t t t m s u b s e c t i o n s , we e x p l o r e how rule-based technology can
b e used to improve on t h e s e problans.
A s i n s e c t i o n 3 , we t a k e a
stag +wise approach t o t h e problem going kom t h e t h e system towards i t s i n t e r n a l o p e r a t i o n s . o f t h e problans a r e
related
to
each o t h e r
user
interface
of
It w i l l b e seen t h a t many and
allow f o r
common
solution str ateg i e s .
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 23 Again
as in
s t r a t e g i e s can
s e c t i o n 3,
be
the
question
arises
whether
these
used only i n newly developed database systems, o r
whether an e x t e r n a l e x p e r t system can b e used v i a a b r i d g e on an
existing
database
system.
top o f
We s h a l l not address t h i s question i n
t h i s s e c t i o n but postpone it t o s e c t i o n 5
where
discuss general
we
i s s u e s common t o b o t h d i r e c t i o n s o f i n t e r a c t i o n .
4.2
Expert Application 1: I n t e l l i g e n t Database Usage In t h e past few y e a r s , much r e s e a r c h h a s been
making
database
i n t e r f a c e s more
intelligent.
directed ?he
towards
main t h r u s t i s
towards more d e d u c t i v e c a p a b i l i t i e s while l e s s i s known about enhanced f u n c t i o n s t o b e applied to r e t r i e v e d d a t a . Ihe f i r st DBMS allowed access o n l y t o t h e stored f i l e s ,
and model
f i e l d s ( r e l a t i o n s , t u p l e s , and a t t r i b u t e v a l u e s i n t h e r e l a t i o n a l of
data
[Codd 19701).
mechanisns t h a t
More r e c e n t
called
systems
provide
view
a l l o w t h e user to name windows through which o n l y a
s u b s s t o f t h e database i s v i s i b l e . often
records,
virtual
relations.
In t h e r e l a t i o n a l model, views a r e
For
exmple,
a virtual relation
"whole l i f e insurance bearerst1 can b e defined t h a t c o n t a i n s customers who have a whole 1i f e insurance policy. T r a d i t i o n a l view mechanisms a r e somewhat 1imited exanple,
a
separate
In
the
above
view o f customers m u l d have to b e defined f o r
each type o f i n s u r a n c e b e n e f i t . [Mall e t a1
.
?he s e l e c t o r
concept
. 1982 1 allows t h e d e f i n i t i o n o f views
introduced
in
with parameters.
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 24 In our exanple, a s e l e c t o r
with-b e n e f i t (T ) for customer-re1 ation
can
be
defined
where
T can
.
insurancell or "term a n n u i t p
assme values
such a s
The user can now request o r
"whole l i f e alter
the
value o f a selected v a r i a b l e
customer[ w i t h-b e n e f i t ( "term annuity'!) I
which
defines
a
moving
window whose focus depends on t h e parameter
chosen. For querying facilities
in
(not
the
updating)
definitions,
and
the stored d a t a
can
offers
similar
view" r u l e s presented i n section 3 .
"generalized
The Prolog inference engine
Prolog
purposes,
perform
deductions
on
these
view
thus answer q u e r i e s n o t immediately answerable kom
.
A nunber o f r e s e a r c h e r s have inv e s t i g ated these po s s i b il i t i e s and
some
implemented
systems e x i s t .
The s o l u t i o n s can b e c l a s s i f i e d in
dr
much t h a same way a s t h e
[Warren 1981 I;
There
are:
l o o s e l y coupled systems where t h e ES d o e s a l l i t s mrk
the
DBMS
is
called
[Chang 19781,
[ R e i t e r 19781, CHenschen and Naqvi 19821, and
s e c t i o n 3.
in
systems containing both t h e ES and the DBMS EMinker 19781,
integrated
before
s t a g e s given
finally
the
tightly
coupled
i n t e r l e a v e s deduction i n the ES with
system
[Fishman
DADM
partial
E C r i s h m a n 19781,
and
Naqvi 19821;
EKellogg 19821 t h a t
search
in
the
DBMS.
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 25 Besides offering
the
user
a
systems can a l s o s u p p o r t o t h e r
d e d u c t i v e formal query language, s u c h high-level
user
interfaces
such
as
n a t u r a l language [Sagalowicz 19771. Other approaches a i c h a d d i t i o n a l l y a d d r e s s i n t e l l i g e n t mechanisms w i l l b e described in s e c t i o n 4.3.2,
updating
below. .)
4.3
Ex p e r t Application 2: In t e l l i g en t Database Operation The a d d i t i o n o f g e n e r a l
rules
and
i n f e r e n c e mechanisms t o
a
d a t a b a s e system may improve t h e user i n t e r f a c e , b u t i t a l s o p r e s e n t s a c h a l l e n g e t o t h e database implenentationresearcher goal
.
i s the
Not only
to execute d e d u c t i v e processing e f f i c i e n t l y b u t t h e new approach
can b e e x p l o i t e d a s we11 to improve t h e execution o f more database o p e r a t i o n s . optimization
We i n v e s t i g a t e t m a r e a s :
methods,
and
deduction-based
traditional
deduc tion-based
integrity
checking
query for
upd a t e t r an sac t i o n s .
4.3.1
Query Optimization
-
I t i s o f foremost importance t o a DBMS t h a t it c o n t a i n s evaluation
subsystem
which
identifies
To t h i s purpose, a query
simplified,
transformed
fast
special-pur pose
Simplification
and
in
algoriths
query
t h e f a s t e s t way t o execute a
submitted query. and
a
is
usually
standardized,
a way t h a t makes t h e a p p l i c a t i o n o f feasible
[J a r k e
and
Koch 19821.
s t a n d a r d i z a t i o n can b e q u i t e ccanplex i f d i f f i c u l t
q u e r i e s a r e asked.
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 26 A d e d u c t i v e component may use meta r u l e s t h a t
guide
among the many a p p l i c a b l e query transformation r u l e s . and
[ R e i t e r 19781 d e s c r i b e
simplification
applications
C Warren 1981 ]
step.
transformation heuristics o f t e s t i n g
range n e s t i n g howver,
that
combines b o t h
i s deterministic
compiler.
An
and
to
extension
to
deduction
the
f i r st, and
.
generalized ideas.
contained
compare
heuristic
called
The deduction mechanism, in
t h i s mechanism
g e n e r a t e a l t e r n a t i v e s t r a t e g i e s and
[Grishman 19781
sharp r e s t r i c t i o n s
a
choice
implements t h e well-known query
separating detachable sub quer i e s i n log i c [ J a r k e and Koch 19831 develop
of
the
the
query
language
w i l l use meta r u l e s t o their
evaluation
costs
based on knowledge about s t o r a g e s t r u c t u r e s and database s t a t i s t i c s . Another transformation s t r a t e g y t h a t u s e s deduction direct
use
of
[ R e i t e r 1978 1.
the
makes more
g e n e r a l r u l e s t h a t d e f i n e t h e database i n t e n s i o n
Semantic query processing [King 1979,
1981 1,
[Hammer
Zdonik 1980 1 a p p l i e s t h e i n t e g r i t y c o n s t r a i n t s o f t h e d a t a b a s e t o
and
s i m p l i f y t h e execution o f q u e r i e s . Assume, f o r exanple, t h a t t h e ES c o n t a i n s a l e g a l c o n s t r a i n t t h a t a
minor
may n o t
g e t more
than
$3000 annuity income.
database i s queried for customers with a n n u i t i e s o f more (or
$5000,
etc.)
, minors
can b e excluded from the search.
on t h e physical database s t r u c t u r e , t h i s may o r execution.
Thus,
this
strategy
appl i c ation o f c o n s t r a i n t s
may n o t
I f now t h e than
$3000
Depending speed
up
a l s o n e e d s m e t a rules t o g u i d e t h e
.
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 27 F i n a l l y , t h e simultaneous optimization o f m u l t i p l e q u e r i e s [Jarke et
al.
19821 can b e supported by an ES.
We s t r a t e g y i s t o raroanber
selected query r e s u l t s t o b e used l a t e r [ F i n k e l s t e i n 19821, another to recognize
common
subexpressions
i n a b a t c h o f queries.
A l a s t area
where e x p e r t knowledge i s h e l p f u l is t h e complex problgn o f
selecting
-
physical s t o r a g e s t r u c t u r e s and access paths [Paige 19821.
Transaction Managenent
4.3.2
-
Changes i n the database must obey t h e static
i . . ,
laws
defined
d a t a v a l u e , r e f e r e n t i a l ) and dynamic ( i . e , ,
change) i n t e g r i t y c o n s t r a i n t s . the
general
by
d a t a value
The DBMS h a s t o determine i s which
of
many general laws apply t o a s p e c i f i c operation on s p e c i f i c d a t a .
Ag ain
,a
pattern-matching
[Nicolas
oriented
deduction
p-ocess
and Yazdanian 19781, [Henschen e t a l , 19821.
b e used to determine the p s s i b l e d e l a y o f
can
be
Meta r u l e s can
i n t e g r i t y checking
the changed data i s a c t u a l l y needed [Lafue 1982 1.
used
until
While t h i s i d e a may
improve system e f f i c i e n c y it i s unclear whether it i s f e a s i b l e from managenent
standpoint
to
a
keep t h e i n t e g r i t y o f t h e database unknown
over an extended period of t i m e , A,second question a1 so a f f e c t s t h e user i n t e r f a c e o f t h e how t o
react
alternatives:
t o violations?
system:
[Nicolas and Yazdanian 19781 see t h r e e
r e j e c t the operation;
accept t h e o p e r a t i o n b u t d o
not
change the database u n t i l f u r t h e r o p e r a t i o n s a r e submitted so t h a t t h e t r a n s a c t i o n a s a whole l e a v e s t h e [Gray 1981 I;
or
trigger
database
automatic
changes
bring the database back t o a c o n s i s t e n t s t a t e .
in
a
consistent
state
t o other data i t e n s t o ,
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 28 The a p p r o p r i a t e c h o i c e depends on many f a c t o r s partially
controlled
knowledge-based
by meta
database
rules
and
architecture
which
can
be
p a r t i a l l y b y t h e user.
p r o p sed
EJarke
in
A
and
Shalev 1983 1 c o n t a i n s an input manag anent system t o avoid unnecessary resubmission o f t r a n s a c t i o n s i n such c a s e s . Language c o n s t r u c t s such a s t h e s e l e c t o r mechanism s e c t i o n 4.2 control. to
can
be
optimization candidate
and
update
physical
control.
access
paths
Second,
the
selector
defines
which may lend themselves t o "view of
answrs to
s e l e c t i o n p-edicate is s t o r e d a s a physical. access
parameterized
[ P a i g e 19821 d e s c r i b e s a method of " f i n i t e d i f f e r e n c i n g n
dynamically
used
a p p l i c a b l e i n t e g r i t y c o n s t r a i n t s f o r semantic query
indexing" [Roussopoulos 19821 in which t h e c o l l e c t i o n
path.
in
used for both query o p t i m i z a t i o n and t r a n s a c t i o n
F i r s t , t h e d e f i n i t i o n predicate o f t h e s e l e c t o r ean b e
identify the
the
described
generates
which
and m a i n t a i n s views f o r query o p t i m i z a t i o n and 1
i n t e g r i t y c o n t r o l purposes.
5.0
ARCHITECTURAL AND TECHNICAL ISSUES I N COUPLING To
smmarize
managgnent,
it
our
can
overview o f
are
very
similar.
proposed s t r a t e g i e s i n feasible.
(k,
the
database
b e said t h a t t h e techniques used both within and
between t h e t m proposed s t a g e s o f operation
ES a p p l i c a t i o n s t o
a
other
intelligent
use
and
intelligent
A system t h a t i n c o r p o r a t e s most o f t h e
uniform hand,
architecture such
systems
should muld
be c l e a r l y need t h e same
coupling mechanisms a s t h e ones described in s e c t i o n 3 which might
g r e a t l y b e n e f i t from more i n t e l l i g e n t d a t a b a s e s .
in
turn
'berefore, i n
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 29 t h i s l a s t s e c t i o n o f t h e paper we pose
some
looking
ES
at
the
interaction
between
architectural
and
questions
B M S from
a
higher
per spec t i v e. Cne might t h i n k t h a t t h e natural a r c h i t e c t u r e would be a system written i n and usable through one language:
integrated
an extended database programming language [Schnidt e t deductive
capabilities,
or
an
uniform either
a l . 19821 with
ES language with general programming
c a p a b i l i t i e s , such a s Prolog [Kowal ski 1981, Walker 1 9831. However,
not
only has
each o f
these
complex computations i n Prolog), but a l s o whole
methods.
purpose
own
it awkward to use i n c e r t a i n subsystems (e.g.,
i d i o s y n c r a s i e s making
the
its
languages
of
exploiting
We t h e r e f o r e have
to
this
existing
exclu3e
architecture defeats DBMS
this
and
quantitative
theoretically
elegant
alternative. Three coup1 ing
candidate of
a r c h i t e c t u r e s have been
independent
systems.
This
where processing t a k e s place and how t h e
identified
for
the
c a t e g o r i z a t i o n i s based on interaction
i s controlled.
We d i s c u s s each a r c h i t e c t u r e i n t u r n . Cne a r c h i t e c t u r e c a l l s f o r a t o t a l d i s t r i b u t i o n o f processing and control.
The
two
systems i n t e r a c t by exchanging messages, a s i n an
"actors approach CHewitt 1976, m a r 19831. master
to
s l a v e r e 1 a t i o n s h i p between t h e o r i g i n a t o r and the r e c e i v e r
o f t h e message, operated degree
but
both
independently. of
Each i n t e r a c t i o n assumes a
appl i c a t i o n
systems a r e
self-contained
and
can
be
An advantage o f t h i s a r c h i t e c t u r e is a l a r g e
and
system
independence,
allowing
for
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
transportability
to
other
introduce
The
the
duplication
dangers o f
How much each system each
capabilities is
other's
system has t o know about t h e consideration.
and D B M S .
ES
of
knowledge
imprtant
representations
may
inconsistency
and
namely
redundancy,
an
incmpatibil ity. A t t h e o t h e r extreme, concentration o f processing and c o n t r o l ,
system
integration
can b e envisioned.
Ckte o f t h e t m subsystems (ES
o r DBMS) may assume a more daninant r o l e . been
followed
a
This approach h a s n a t u r a l l y
by most r e s e a r c h e r s who focus on one d i r e c t i o n o f t h e
i n t e r a c t i o n between ES and DBMS, f o r exanple IChang 1978, Fordyce Kellogg19821.
Sullivan 1983,
v a r i a b l e d i s t r i b u t i o n o f labor
The
architecture
(e.g.
where
suggests
a
and more
is
query o p t i m i z a t i o n
done) than t h e t y p i c a l l y predetermined l a b o r s e p a r a t i o n o f d i s t r i b u t e d systems.
There
architecture, with
this
i s much
and
potential
a t t h e expense o f t r a n s p o r t a b i l i t y .
is the
solution
sub system s
flexibility
integration
of
in
such
an
Another d i f f i c u l t y
additional
external
.
F i n a l l y , i n a t h i r d a r c h i t e c t u r e , processing i s d i s t r i b u t e d control
is now t h e
supervisor progran. necessary
steps
translations)
, and
In for
responsibility o f essence,
the
interfacing
a
separate
supervisor
ES w i t h
the
subsystem,
performs the
manages t h e i n t e r a c t i o n between them.
but
all
DBMS
a the
(e.g
.,
This a p p e a r s
a s a compromise a r c h i t e c t u r e with t h e main advantage o f allowing f o r a snoother i n t e r a c t i o n with o t h e r subsystems. mathematical
Such
sub systems i n c l u d e
models and modules f o r knowledge a c q u i s i t i o n , g e n e r a t i o n
o f a l t e r n a t i v e s o l u t i o n s [Reitman 1982 I,
database
design,
and
user
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 31 interfaces.
?he
challenging
research
question
wuld
be
how t o
implement a s u p e r v i s o r t h a t makes f u l l use o f t h e c a p a b i l i t i e s o f
the
v a r i o u s subsystems without d u p l i c a t i n g t h e i r f e a t u r e s . Within e a c h a r c h i t e c t u r e , means must
be
to
provided
translate
h o w l e d g e r e p r e s e n t a t i o n s and t r a n s a c t i o n r e q u e s t s between sub systems. A s pointed o u t i n s m t i o n 3 , t h e s i m i l a r i t y
relational database
data models
optimization
model are
steps
makes t h i s used,
between
Prolog
and
t a s k r e l a t i v e l y easy.
however,
become n e c e s s a r y .
intermediate
the
If o t h e r
translation
and
Depending on t h e a r c h i t e c t u r e
c h o s e n , i t must b e decided which subsystem
is responsible
for
this
task.
6.0
CONCLUDING REMARKS S e v e r a l p r a c t i c a l b e n e f i t s from t h e
cooperative
use
systems with d a t a b a s e management s y s t e m s were i d e n t i f i e d .
of
expert
?he o v e r a l l
g o a l o f our m r k i s an advanced system e f f e c t i v e l y s u p p o r t i n g b u s i n e s s decisions.
Such
a system m u l d i n t e g r a t e d a t a b a s e managanent, model
managanent, and e x p e r t system technology within t h e same a r c h i t e c t u r e . A t New York U n i v e r s i t y , w e a r e e x p l o r i n g s e v e r a l
t h i s goal.
strategies
for
A s t a g e - w i s e a p p r o a c h r e q u i r i n g simultaneous r e s e a r c h o n
complanentary t o p i c s a s o u t l i n e d i n t h i s paper i s being
designed
and
implemented.
Center for Digital Economy Research Stem School of Business IVorking Paper IS-83-53
Page 32
The w o r k r e p o r t e d i n t h i s paper i s s u p p o r t e d in p a r t b y a j o i n t s t u d y w i t h I n t e r n a t i o n a l Ebsiness Machines Corporation. Jim C l i f f o r d desig ned and implemented t h e i n t e r n a l r e 1 a t i o n a l d a t a b a s e system in Prolog and co-developed the staged approach t o g e t t i n g d a t a i n t o t h e e x p e r t system. Taracad Sivasankaran d i d a major p a r t of+ t h e i n i t i a l d e s i g n and preliminary implementation o f t h e i n s u r a n c e e x p e r t system used a s an ex m p l e i n t h i s paper. We a1 so thank t h e o t h e r p r o j e c t members, Hank Lucas, Ted S t o h r , Norm White, and e s p e c i a l l y Walt Reitman f o r encour ag ement and many h e l p f u l d i s c u s s i o n s . F i n a l l y , we thank S i b y l l e Hentsch o f Hamburg U n i v e r s i t y f o r aquainting us with t h e i n t r i c a c i e s o f volcanoes o f f Iceland.
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