Decision Support System Architecture, Hardware, and Operating System Platforms.
Decision Support System Architecture, Hardware, and
Operating System Platforms
Radiant Vict or Imbar
St af Pengaj ar Jur usan S-1 Si st em Inf or masi
Fakul t as Teknol ogi Inf or masi
Uni ver si t as Kr i st en Mar anat ha
Jl . Pr of . Dr g. Sur i a Sumant r i No. 65, BANDUNG 40164
Email : radiant . vi@eng. maranat ha. edu
Abst rak
DSS (Deci si on Suppor t Syst em) / Si st em Pendukung Keput usan adal ah
si st em i nf or masi ber basi s komput er yang t uj uan ut amanya adal ah
menyedi akan i nf or masi yang bi sa menj adi dasar unt uk pengambi l an
keput usan. Seper t i sebuah per angkat l unak komput er ber ada dal am suat u
l i ngkungan t er i nt egr asi ant ar a per angkat ker as dengan si st em oper asi nya.
Begi t u pul a dal am per encanaan DSS, bagai mana membangun DSS yang
dapat membant u pengambi l an keput usan. Sel ai n i t u DSS memer l ukan
ar si t ekt ur komput er yang t epat dal am pengapl i kasi annya, mel i put i
per angkat ker as dengan si st em oper asi yang mendukung, memi l i h
kombi nasi yang t epat dan, at au dengan kat a l ai n, ar si t ekt ur komput er
yang t epat dapat membuat DSS ber j al an dengan ef ekt i f dan ef i si en dan
demi ki an pul a sebal i knya.
Kat a kunci : Komput er , DSS, Ar si t ekt ur .
1. Definition of DSS and History of DSS.
A DSS can be described as a comput er-based int eract ive human–comput er
decision-making syst em t hat :
1. support s decision makers rat her t han replaces t hem.
2. ut ilizes dat a and models.
3. solves problems wit h varying degrees of st ruct ure.
4. f ocuses on ef f ect iveness rat her t han ef f iciency in decision
processes (f acilit at ing decision processes).
DSS evolved early in t he era of dist ribut ed comput ing. The hist ory of such
syst ems begins in about 1965. At about t his t ime, t he development of t he
IBM Syst em 360 and ot her more powerf ul mainf rame syst ems made it more
pract ical and cost -ef f ect ive t o develop Management Inf ormat ion Syst ems
(MIS) in large companies (cf . , Davis, 1974). MIS f ocused on providing
managers wit h st ruct ured, periodic report s. Much of t he inf ormat ion was
f rom account ing and t ransact ion syst ems.
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By t he lat e 1970s, a number of researchers and companies had devel oped
int eract ive inf ormat ion syst ems t hat used dat a and models t o hel p
managers analyze semi-st ruct ured problems. These diverse syst ems were
called Decision Support Syst ems. From t hose early days, it was recognized
t hat DSS could be designed t o support decision-makers at any level in an
organizat ion. DSS coul d support operat ions, f inancial management and
st rat egic decision-making. DSS could use spat ial dat a in a syst em like
Geodat a Analysis and Display Syst em (GADS) (cf . , Grace, 1976), st ruct ured
mult idimensional dat a and unst ruct ured document s (cf . , Swanson and
Culnan, 1978). A variet y of models were used in DSS including opt imizat ion
and simulat ion. Also, st at ist ical packages were recognized as t ools f or
building DSS. Art if icial Int elligence researchers began work on
management and business expert syst ems in t he early 1980s.
In t he middle and lat e 1980s, Execut ive Inf ormat ion Syst ems (EIS) evolved
f rom single user model-driven Decision Support syst ems and improved
relat ional dat abase product s. The f irst EIS used pre-def ined inf ormat ion
screens and were maint ained by analyst s f or senior execut ives.
Beginning in about 1990, dat a warehousing and On-Line Analyt ical
Processing (OLAP) began broadening t he realm of EIS and def ined a
broader cat egory of Dat a-Driven DSS (cf . , Dhar and St ein, 1997). Nigel
Pendse (1997) claims t he f irst Execut ive Inf ormat ion Syst em product was
Pilot Sof t ware’ s Command Cent er. He not es bot h mult idimensional analysis
and OLAP had origins in t he APL programming l anguage and in syst ems like
Express and Comshare’ s Syst em W. Nigel Pendse of t he OLAPReport . com
has writ t en and updat es a much more det ail ed hist ory of t he origins of
OLAP product s.
A DSS can t ake many dif f erent f orms. Minimal ly we can say t hat a DSS is a
syst em f or making decisions. A decision is a choice bet ween alt ernat ives
based on est imat es of t he values of t hose alt ernat ives. Support ing a
decision means support ing t his choice by support ing t he est imat ion, t he
evaluat ion and/ or t he comparison and choice. In pract ice ref erences t o
DSS are usually ref erences t o comput er applicat ions t hat perf orm such a
support ing role.
Who is t he decision-maker? What kinds of dat a serve as input s t o t he
decision-making process? What does t he decision-making process it self look
like? What kinds of risks and const raint s are associat ed wit h t he decisionmaking process? How is t he out put of t he decision-making process – a
decision – evaluat ed, implement ed and t racked?
Ult imat ely, no mat t er where we ground ourselves in a discussion of
decision support syst ems, we have t o develop and empl oy a model of
decision-making: t he set of act ivit ies t hat DSS environment s support . The
key element s of t his model are f airly common, and include:
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Decision Support Syst em Archit ect ure, Hardware, and Operat ing Syst em Plat f orms
(Radi ant Vi ct or Imbar )
•
•
•
•
a decision-maker : an individual or group charged wit h making a
part icular decision
a set of inputs to the decision-making process: dat a, numerical or
qualit at ive models f or int erpret ing t hat dat a, hist orical experience
wit h simil ar dat a set s or simil ar decision-making sit uat ions, and
various kinds of cult ural and psychological norms and const raint s
associat ed wit h decision-making
t he decision-making process itself : a set of st eps, more or less
well-underst ood, f or t ransf orming t he input s int o out put s in t he
f orm of decisions,
a set of out puts from the decision-making process, including t he
decisions t hemselves and (ideally) a set of crit eria f or evaluat ing
decisions produced by t he process against t he set of needs,
problems or obj ect ives t hat occasioned t he decision-making act ivit y
in t he f irst place.
Figure 1. A Prot ot ypic Decision-Making Model (Part ial)
How do DSS environments support decision-making?
DSS environment s support t he generic decision-making model above in a
number of ways:
•
•
In decision preparat ion, DSS environment s provide dat a required as
input t o t he decision-making process. This is int erest ingl y enough,
about all most dat a mart and dat a warehousing environment s do
t oday.
In decision st ruct uring, DSS environment s provide t ools and models
f or arranging t he input s in ways t hat make sense t o f rame t he
decision. These t ools and models are not pivot t ables and ot her
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Jurnal Sist em Inf ormasi Vol. 2 No. 1 Maret 2007 : 41-50
•
•
•
•
aspect s of dat a present at ion f ound in query t ools. They are act ual
decision making t ools, like f ault t ree anal ysis, Bayesian l ogic and
model-based decision-making based on t hings l ike neural net works.
In cont ext development , DSS environment s again provide t ools, and
provide t he mechanisms f or capt uring inf ormat ion about a
decision’ s const it uencies (who’ s af f ect ed by t his decision),
out comes and t heir probabilit ies, and ot her element s of t he larger
decision making cont ext .
In decision-making, DSS environment s may aut omat e all or part of
t he decision-making process and of f er evaluat ions on t he opt imal
decision. Expert syst ems and art if icial int ell igence environment s
purport t o do t his, but t hey work only in very limit ed cases,
because of some f undament al f laws in t he t echnology (namely,
t heir inabilit y t o deal wit h non-binary, or f uzzy, choices, like “ it ’ s
more likel y t hat we’ ll lose market share t han win it , ” which is a
rule t hat no t radit ional AI-based syst em can code).
In decision propagat ion, DSS environment s t ake t he inf ormat ion
gat hered about const it uencies and dependencies and out comes and
drive element s of t he decision int o t hose const it uencies f or act ion.
In decision management , DSS environment s inspect out comes days,
weeks and mont hs af t er decisions t o see if (a) t he decision was
implement ed/ propagat ed and (b) if t he ef f ect s of t he decision are
as expect ed.
2. Defining The DSS Architecture.
An import ant issue t o consider bef ore planning individual syst ems is
developing an overall ent erprise inf ormat ion syst ems archit ect ure. The
archit ect ure of an inf ormat ion syst em ref ers t o t he way it s pieces are laid
out , what t ypes of t asks are allocat ed t o each piece, how t he pieces
int eract wit h each ot her, and how t hey int eract wit h t he out side world.
Despit e t he unique nat ure of most DSS appl icat ions, several considerat ions
t hat paralel t he development of any ot her large-scale sof t ware applicat ion
must be made. One of t he most import ant considerat ions is t he degree t o
which t he proposed DSS conf orms t o and int egrat es wit h t he exist ing
ent erprise inf ormat ion syst em archit ect ure. The archit ect ure of an
inf ormat ion syst em ref ers t o t he manner in which t he various pieces of t he
syst em are laid out wit h respect t o locat ion, connect ivit y, hierarchy, and
int ernal and ext ernal int eract ions.
When we speak of an inf ormat ion syst ems archit ect ure, we are not
ref erring t o t he exact make and model of comput er, disk drive, or monit or
in t he syst em. Rat her, we are f ocused on t hree specif ic higher level issues:
• Int eroperabilit y
DSS int eroperabilit y cont rols t o degree t o which inf ormat ion can be
delivered t o t he exact locus of a decision or point of user in an
ef f ect ive and ef f icient manner. It ensures t hat t he DSS under design
44
Decision Support Syst em Archit ect ure, Hardware, and Operat ing Syst em Plat f orms
(Radi ant Vi ct or Imbar )
•
•
can access and deliver it s inf ormat ion t o t he appropriat e end users
at what ever locat ion or t ime period t hey may need it .
Compat ibilit y
Compat ibilit y of syst ems, so t hat resources can be shared easily and
leveraged across t he organizat ion.
Scalabilit y
Expandabilit y of syst ems, so t hat limit ed single-f unct ion
component s do not creat e bot t lenecks t hat obst ruct t he growt h of
t he organizat ion.
The overall archit ect ure of a DSS should be laid out and underst ood bef ore
specif ic hardware and sof t ware sel ect ion decisions are made. The nat ure
of t his archit ect ure depends on t he DSS. To lay out a DSS archit ect ure must
consider t he spect rum of DSS t hat t he organizat ion will use :
• St rat egic, t act ical (management cont rol), and operat ional
decisions.
• Unst ruct ured, semist ruct ured, and st ruct ured decisions.
• All level of management and knowledge workers in t he
organizat ion.
• All maj or f unct ional, product or line of business, and geographic
divisions of t he organizat ion.
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Jurnal Sist em Inf ormasi Vol. 2 No. 1 Maret 2007 : 41-50
Direct usage of minicomputer by programmers only
Ethernet
UNIX-based minicomputer platform(s)
E-mail,
etc
Network gateway
External
database
input
Windows 2000 based platforms :
- Spreadsheets for personal financial models.
- All other models run on minicomputer.
- Lotus notes.
- Electronic mail.
User interface
Graphical query
tools, SQL
Relational
DBMS
Model management
system
Database
Model base
Query definition
Financial model development
Simulation model development
Figure 2. Specific DSS Archit ect ure
2. 1. DSS and Client/ Server Comput ing
Many organizat ions deal wit h t he af orement ioned disadvant ages by
providing individual users, via t heir deskt op comput ers, wit h access t o dat a
on t he cent ral comput er. The deskt op comput ers t hen handle t he
comput at ions and ot her processes of a DSS. These processes usually
include support ing t he usual element s of t oday’ s easy t o use graphical user
int erf aces, such as windows, pull-down menus, and t he use of a mouse or
ot her point ing device. This approach is ref erred t o as client / server
comput ing. The syst em st oring t he dat abase is called a server. The server
syst em act s as a dat a reposit ory. The client syst em, t ypicall y on t he user’ s
deskt op, runs t he applicat ion using dat a f rom t he server. The result is a
close mat ching of each part ner’ s capabilit ies t o it s role in t he overall
syst em.
The advant ages of using a client / server comput ing are client / server
comput ing allows each part of an overal l applicat ion t o run in a
hardware/ sof t ware environment opt imized f or t hat part and t hat part
alone, whet her it demands a large dat abase, high speed comput ing. This
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Decision Support Syst em Archit ect ure, Hardware, and Operat ing Syst em Plat f orms
(Radi ant Vi ct or Imbar )
opt imizat ion t end t o minimize t he overall cost of t he syst em. Anot her
advant ages is because t he client / server approach is increasingly popul ar,
many applicat ions and sof t ware development t ools are designed t o run in a
client / server environment . Organizat ions can of t en reduse development
t ime and cost by using t his.
The disadvant ages of using a client / server syst em are t he complexit ies of
applicat ion development and management are much great er in a
client / server syst em t han t hey are on a single comput er, securit y also
becomes a maj or concern. Securit y f eat ures, perhaps incl uding f irewalls t o
limit access f rom out side t he organizat ion.
2. 2. The Int ernet and Client/ Server Computing in DSS
The Int ernet is a giant worldwide inf ormat ion source. The World Wide
Web, especially, provides easy access t o inf ormat ion on a wealt h of t opics.
Much of t his inf ormat ion can be usef ul f or decision making. It is possible t o
t ake t his same t echnology and t his same int erf ace and use t hem t o access
a corporat e DSS. The Web archit ect ure, t he HyperText Markup Language
(HTML) f or developing Web pages, t he Java l anguage t hat most browsers
can int erpret , t he JavaScript language f or commands added t o HTML, and
t he st andards t hat Web browsers must f ollow def ine, in ef f ect , a plat f orm
f or applicat ions. DSS can use t his plat f orm so t hat any user wit h a Web
browser can access t hose DSS easily. The advant ages are it is accessible
f rom anywhere in t he indust rialized world and t he web can be accessed via
any t ype of hardware. The disadvant ages is web access can be slow
because a lot of grahics added t o web pages. The hardware and
communicat ion l inks needed t o provide f ast access may be more expensive
t han local area net work
2. 3. DSS Using Shared Data on a Separate System
It is not necessary f or t he server in a client / server syst em t o be t he cent ral
corporat e comput er t hat st ores t he live, operat ional dat abase. It is of t en a
good idea t o ext ract meaningf ul decision support dat a f rom operat ional
dat abase and load it int o a dif f erent comput er.
The linked-syst em approach is ef f ect ive when t he applicat ion alloes f or a
decision support dat abase t hat is separat e f rom t he f irm’ s t ransact ion
processing dat abase. As you know, such a dat abase is of t en called a dat a
warehouse.
Because corporat e dat a warehouses t end t o be large, t he servers required
are correspondingly large. Mult iprocessors, which harness t he power of t wo
t o several dozen processors t o a single overall t ask, are usef ul here
because t hey increase t he comput ing power of a syst em beyond t hat of a
single processor wit hout requiring t he expensive t echnologies used in
mainf rames or supercomput ers.
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A dat a warehouse is a read-onl y dat abase. It s users cannot updat e t he
“ live” organizat ional inf ormat ion. That limit s t he use of a dat a warehouse
t o sit uat ions where decision makers access corporat e inf ormat ion but do
not change it . If an applicat ion running in t his environment must updat e
t he main dat abase, it must be done in some ot her way. One approach is t o
have DSS comput er mimic a t ransact ion processing user and submit
t ransact ions.
The Advant ages of using Shared Dat a on a Separat e Syst em are DSS
hardware need not t o be shared wit h ot her applicat ions, so DSS response
t ime is preserved no mat t er what is going on elsewhere in t he organizat ion
and t he DSS hardware can be opt imized f or t hat purpose and t hat purpose
alone. The disadvant ages are t he need t o t ransf er dat a bet ween t wo
syst ems and users who access bot h syst ems may need t wo t ypes of
t erminals or may have t o f ollow complex net work logon procedures t o
access t he correct one. Swit ching f rom one t o anot her may be t ime
consuming.
2. 4. DSS on a Stand-Alone System
DSS t hat do not access a large cent ral dat abase are candidat es f or st andalone syst ems. A st and-alone DSS can be run on a comput er dedicat ed t o
t he DSS t ask or on a mult iple-user comput er used in t ime-sharing mode.
Being “ st and-alone” is a mat t er of degree. The absol ut ely, t ot ally, 100
percent st and-alone syst em is almost as rare as t he unicorn. Even users of
nominally st and-al one syst em exchange f iles via universal serial bus (USB)
or send e-mail via a dial-up modem link. What t he t erm really means here
is t hat t he operat ion of t he DSS doesn’ t depend on a regul ar connect ion t o
anot her comput er. Such a connect ion probabl y does exist and can be used
f or DSS-rel at ed purposes, such as sending it s result s t o a colleague f or
review and comment . The advant ages of using a st and alone syst em are
t he syst em can be t ot ally opt imized f or DSS and t he complexit y of sharing
a resource wit h ot her users is avoided. The disadvant ages of using a st and
alone syst em are any dat a t he syst em requires must be provided by it s
users and it may be dif f icult t o int egrat e a st and alone syst em wit h
corporat e applicat ions at a lat er dat e.
3. Choosing a DSS Hardware Environment
Here is a l ist of quest ions t hat can ask t o help make t he choice among t he
previousl y list ed opt ions. As a group, t hough, t heir answers will help point
out t he right DSS hardware approach :
1. Are t here any corporat e policies t hat must f ollow ? If t here are,
t hey may narrow t he choice by mandat ing one opt ion, or by
eliminat ing some opt ions.
2. How large and widespread will t he DSS user communit y be ? If it is
large and widespread, will it s members use exact ly t he same
applicat ion, variat ions on one appl icat ion, or dif f erent applicat ions
? When t hey use t he same applicat ion, do t hey use it
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Decision Support Syst em Archit ect ure, Hardware, and Operat ing Syst em Plat f orms
(Radi ant Vi ct or Imbar )
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
independent ly, or must t heir usage be coordinat ed t hrough a shared
dat abase or in any ot her way ? The more similarit y, t he more
sharing, t he more shoul d look t oward shared syst ems and LAN wit h
high-end servers. The l ess sharing and similarit y, t he more power
probabl y belongs on individual deskt ops.
Are most of t he prospect ive users already using a part icul ar syst em
? If so, see if it can be used as is or wit h modif icat ions such as
modest upgrade or int erconnect ion via a LAN.
Is t here a corporat e mainf rame wit h suf f icient capacit y, or t o which
suf f icient capacit y can be added at reasonable cost ?
Is anot her powerf ul server syst em avail able, linked t o t hat
mainf rame or not , wit h suf f icient capacit y ?
Do prospect ive users al ready have microcomput ers or workst at ions
t hat can handle t he applicat ion ?
If new syst ems are required, will t he exist ing cent ral syst em be
able t o share dat a wit h t hem ? t o shoul der an appl icat ion j oint ly
wit h t hem ?
Do t he necessary development t ools exist f or any of t hose syst ems ?
Are t hey already wit hin t he organizat ion , available f rom t he
syst em vendor, or available f rom t hird part ies ?
Wit h which of t hese syst ems and t ools, if any, are t he prospect ive
DSS developers already f amiliar ?
Does t he applicat ion require access t o a dat abase ? If so, does t hat
dat abase already exist ? If not , is it t o be derived f rom corporat e
dat a or f rom a separat e source ? If f rom corporat e dat a, how up t o
dat e should t he dat abase be f or t he applicat ion t o f unct ion ?
Does t he applicat ion’ s use of t he corporat e dat abase require onl y
t he abilit y t o read t he dat a, or must it also be able t o updat e t he
dat a ?
How much processing power does t he applicat ion require ? How
much dat a st orage capacit y ?
Are prospect ive users capable of perf orming (and willing t o
perf orm) basic syst em administ rat ion t asks, such as inst alling
sof t ware and backing up dat a f iles ?
Summary
DSS are inf ormat ion syst ems whose primary purpose is t o provide
knowledge workers wit h inf ormat ion. Common charact erist ics of most or
all DSS include t heir use by managers and ot her knowledge workers, t heir
use of a dat abase and t heir use of models. DSS are general ly used when a
comput er cannot be programmed t o make a decision f or all cases. They
support , but do not repl ace, human decision makers.
Bef ore embarking on t he development of any maj or DSS, we should have a
clear idea of bot h it s archit ect ure and t he overall DSS archit ect ure of t he
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organizat ion. Knowing t hese will help t o communicat e t he DSS vision t o
management and will help t o plan syst ems t hat will cont inue t o meet user
needs in t he f ut ure. DSS can run on a variet y of dif f erent plat f orms. Each
t ype has bot h advant ages and disadvant ages, which of t en make one bet t er
t han anot her in a specif ic sit uat ion. An exist ing mult iuser comput er may be
suit able when it has adequat e capacit y, prospect ive DSS users are already
using it , and rapid access t o cent ral corporat e dat a is required.
It is not necessary t o provide decision support dat a direct l y f rom t he live
dat abase. Inst ead, t he necessary dat a can be consolidat ed int o a
specialized decision support dat abase, of t en called a dat a warehouse.
While t his is not absolut ely necessary, dat a warehouse are of t en housed on
separat e syst ems f rom t he operat ional dat abase. In ot her sit uat ions,
model-orient ed or process-orient ed DSS of t en do not need a dat abase at
all. In t hese cases a st and-alone DSS, usuall y running on t he user’ s deskt op
syst em wit h no act ive connect ions t o t he out side world, may be t he best
solut ion.
BIBLIOGRAPHY
Mallach, Ef rem G (2000). Deci si on Suppor t and Dat a War ehouse Syst em:
McGraw-Hill.
St air, Ralph M. , and George W. Reynolds (1997). Pr i nci pl es of Inf or mat i on
Syst ems: A Manager i al Appr oach: Cambridge.
Mart in, E. Wainwright , Daniel W. DeHayes, Jef f rey A. Hof f er, and Wil liam
C. Perkins (1991).
Managi ng Inf or mat i on Technol ogy: What
Manager s Need t o Know : Macmill an, New York.
Demarest , Mark. , Technology and Policy in Decision Support
Syst ems, ht t p: / / dssresources. com/ papers/ f eat ures/ demarest 05/ dem
arest 07082005. ht ml . Tgl Akses 10 November 2006.
ht t p: / / en. wikipedia. org/ wiki/ Decision_support _syst em.
November 2006.
Access dat e 10
ht t p: / / elearning. dmst . aueb. gr/ mis/ Cases/ Nederlandse_Spoorwegn/ Case/
Training_Fil es/ iebmdssrwweb. pdf . Access dat e 10 November 2006
50
Operating System Platforms
Radiant Vict or Imbar
St af Pengaj ar Jur usan S-1 Si st em Inf or masi
Fakul t as Teknol ogi Inf or masi
Uni ver si t as Kr i st en Mar anat ha
Jl . Pr of . Dr g. Sur i a Sumant r i No. 65, BANDUNG 40164
Email : radiant . vi@eng. maranat ha. edu
Abst rak
DSS (Deci si on Suppor t Syst em) / Si st em Pendukung Keput usan adal ah
si st em i nf or masi ber basi s komput er yang t uj uan ut amanya adal ah
menyedi akan i nf or masi yang bi sa menj adi dasar unt uk pengambi l an
keput usan. Seper t i sebuah per angkat l unak komput er ber ada dal am suat u
l i ngkungan t er i nt egr asi ant ar a per angkat ker as dengan si st em oper asi nya.
Begi t u pul a dal am per encanaan DSS, bagai mana membangun DSS yang
dapat membant u pengambi l an keput usan. Sel ai n i t u DSS memer l ukan
ar si t ekt ur komput er yang t epat dal am pengapl i kasi annya, mel i put i
per angkat ker as dengan si st em oper asi yang mendukung, memi l i h
kombi nasi yang t epat dan, at au dengan kat a l ai n, ar si t ekt ur komput er
yang t epat dapat membuat DSS ber j al an dengan ef ekt i f dan ef i si en dan
demi ki an pul a sebal i knya.
Kat a kunci : Komput er , DSS, Ar si t ekt ur .
1. Definition of DSS and History of DSS.
A DSS can be described as a comput er-based int eract ive human–comput er
decision-making syst em t hat :
1. support s decision makers rat her t han replaces t hem.
2. ut ilizes dat a and models.
3. solves problems wit h varying degrees of st ruct ure.
4. f ocuses on ef f ect iveness rat her t han ef f iciency in decision
processes (f acilit at ing decision processes).
DSS evolved early in t he era of dist ribut ed comput ing. The hist ory of such
syst ems begins in about 1965. At about t his t ime, t he development of t he
IBM Syst em 360 and ot her more powerf ul mainf rame syst ems made it more
pract ical and cost -ef f ect ive t o develop Management Inf ormat ion Syst ems
(MIS) in large companies (cf . , Davis, 1974). MIS f ocused on providing
managers wit h st ruct ured, periodic report s. Much of t he inf ormat ion was
f rom account ing and t ransact ion syst ems.
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By t he lat e 1970s, a number of researchers and companies had devel oped
int eract ive inf ormat ion syst ems t hat used dat a and models t o hel p
managers analyze semi-st ruct ured problems. These diverse syst ems were
called Decision Support Syst ems. From t hose early days, it was recognized
t hat DSS could be designed t o support decision-makers at any level in an
organizat ion. DSS coul d support operat ions, f inancial management and
st rat egic decision-making. DSS could use spat ial dat a in a syst em like
Geodat a Analysis and Display Syst em (GADS) (cf . , Grace, 1976), st ruct ured
mult idimensional dat a and unst ruct ured document s (cf . , Swanson and
Culnan, 1978). A variet y of models were used in DSS including opt imizat ion
and simulat ion. Also, st at ist ical packages were recognized as t ools f or
building DSS. Art if icial Int elligence researchers began work on
management and business expert syst ems in t he early 1980s.
In t he middle and lat e 1980s, Execut ive Inf ormat ion Syst ems (EIS) evolved
f rom single user model-driven Decision Support syst ems and improved
relat ional dat abase product s. The f irst EIS used pre-def ined inf ormat ion
screens and were maint ained by analyst s f or senior execut ives.
Beginning in about 1990, dat a warehousing and On-Line Analyt ical
Processing (OLAP) began broadening t he realm of EIS and def ined a
broader cat egory of Dat a-Driven DSS (cf . , Dhar and St ein, 1997). Nigel
Pendse (1997) claims t he f irst Execut ive Inf ormat ion Syst em product was
Pilot Sof t ware’ s Command Cent er. He not es bot h mult idimensional analysis
and OLAP had origins in t he APL programming l anguage and in syst ems like
Express and Comshare’ s Syst em W. Nigel Pendse of t he OLAPReport . com
has writ t en and updat es a much more det ail ed hist ory of t he origins of
OLAP product s.
A DSS can t ake many dif f erent f orms. Minimal ly we can say t hat a DSS is a
syst em f or making decisions. A decision is a choice bet ween alt ernat ives
based on est imat es of t he values of t hose alt ernat ives. Support ing a
decision means support ing t his choice by support ing t he est imat ion, t he
evaluat ion and/ or t he comparison and choice. In pract ice ref erences t o
DSS are usually ref erences t o comput er applicat ions t hat perf orm such a
support ing role.
Who is t he decision-maker? What kinds of dat a serve as input s t o t he
decision-making process? What does t he decision-making process it self look
like? What kinds of risks and const raint s are associat ed wit h t he decisionmaking process? How is t he out put of t he decision-making process – a
decision – evaluat ed, implement ed and t racked?
Ult imat ely, no mat t er where we ground ourselves in a discussion of
decision support syst ems, we have t o develop and empl oy a model of
decision-making: t he set of act ivit ies t hat DSS environment s support . The
key element s of t his model are f airly common, and include:
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Decision Support Syst em Archit ect ure, Hardware, and Operat ing Syst em Plat f orms
(Radi ant Vi ct or Imbar )
•
•
•
•
a decision-maker : an individual or group charged wit h making a
part icular decision
a set of inputs to the decision-making process: dat a, numerical or
qualit at ive models f or int erpret ing t hat dat a, hist orical experience
wit h simil ar dat a set s or simil ar decision-making sit uat ions, and
various kinds of cult ural and psychological norms and const raint s
associat ed wit h decision-making
t he decision-making process itself : a set of st eps, more or less
well-underst ood, f or t ransf orming t he input s int o out put s in t he
f orm of decisions,
a set of out puts from the decision-making process, including t he
decisions t hemselves and (ideally) a set of crit eria f or evaluat ing
decisions produced by t he process against t he set of needs,
problems or obj ect ives t hat occasioned t he decision-making act ivit y
in t he f irst place.
Figure 1. A Prot ot ypic Decision-Making Model (Part ial)
How do DSS environments support decision-making?
DSS environment s support t he generic decision-making model above in a
number of ways:
•
•
In decision preparat ion, DSS environment s provide dat a required as
input t o t he decision-making process. This is int erest ingl y enough,
about all most dat a mart and dat a warehousing environment s do
t oday.
In decision st ruct uring, DSS environment s provide t ools and models
f or arranging t he input s in ways t hat make sense t o f rame t he
decision. These t ools and models are not pivot t ables and ot her
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Jurnal Sist em Inf ormasi Vol. 2 No. 1 Maret 2007 : 41-50
•
•
•
•
aspect s of dat a present at ion f ound in query t ools. They are act ual
decision making t ools, like f ault t ree anal ysis, Bayesian l ogic and
model-based decision-making based on t hings l ike neural net works.
In cont ext development , DSS environment s again provide t ools, and
provide t he mechanisms f or capt uring inf ormat ion about a
decision’ s const it uencies (who’ s af f ect ed by t his decision),
out comes and t heir probabilit ies, and ot her element s of t he larger
decision making cont ext .
In decision-making, DSS environment s may aut omat e all or part of
t he decision-making process and of f er evaluat ions on t he opt imal
decision. Expert syst ems and art if icial int ell igence environment s
purport t o do t his, but t hey work only in very limit ed cases,
because of some f undament al f laws in t he t echnology (namely,
t heir inabilit y t o deal wit h non-binary, or f uzzy, choices, like “ it ’ s
more likel y t hat we’ ll lose market share t han win it , ” which is a
rule t hat no t radit ional AI-based syst em can code).
In decision propagat ion, DSS environment s t ake t he inf ormat ion
gat hered about const it uencies and dependencies and out comes and
drive element s of t he decision int o t hose const it uencies f or act ion.
In decision management , DSS environment s inspect out comes days,
weeks and mont hs af t er decisions t o see if (a) t he decision was
implement ed/ propagat ed and (b) if t he ef f ect s of t he decision are
as expect ed.
2. Defining The DSS Architecture.
An import ant issue t o consider bef ore planning individual syst ems is
developing an overall ent erprise inf ormat ion syst ems archit ect ure. The
archit ect ure of an inf ormat ion syst em ref ers t o t he way it s pieces are laid
out , what t ypes of t asks are allocat ed t o each piece, how t he pieces
int eract wit h each ot her, and how t hey int eract wit h t he out side world.
Despit e t he unique nat ure of most DSS appl icat ions, several considerat ions
t hat paralel t he development of any ot her large-scale sof t ware applicat ion
must be made. One of t he most import ant considerat ions is t he degree t o
which t he proposed DSS conf orms t o and int egrat es wit h t he exist ing
ent erprise inf ormat ion syst em archit ect ure. The archit ect ure of an
inf ormat ion syst em ref ers t o t he manner in which t he various pieces of t he
syst em are laid out wit h respect t o locat ion, connect ivit y, hierarchy, and
int ernal and ext ernal int eract ions.
When we speak of an inf ormat ion syst ems archit ect ure, we are not
ref erring t o t he exact make and model of comput er, disk drive, or monit or
in t he syst em. Rat her, we are f ocused on t hree specif ic higher level issues:
• Int eroperabilit y
DSS int eroperabilit y cont rols t o degree t o which inf ormat ion can be
delivered t o t he exact locus of a decision or point of user in an
ef f ect ive and ef f icient manner. It ensures t hat t he DSS under design
44
Decision Support Syst em Archit ect ure, Hardware, and Operat ing Syst em Plat f orms
(Radi ant Vi ct or Imbar )
•
•
can access and deliver it s inf ormat ion t o t he appropriat e end users
at what ever locat ion or t ime period t hey may need it .
Compat ibilit y
Compat ibilit y of syst ems, so t hat resources can be shared easily and
leveraged across t he organizat ion.
Scalabilit y
Expandabilit y of syst ems, so t hat limit ed single-f unct ion
component s do not creat e bot t lenecks t hat obst ruct t he growt h of
t he organizat ion.
The overall archit ect ure of a DSS should be laid out and underst ood bef ore
specif ic hardware and sof t ware sel ect ion decisions are made. The nat ure
of t his archit ect ure depends on t he DSS. To lay out a DSS archit ect ure must
consider t he spect rum of DSS t hat t he organizat ion will use :
• St rat egic, t act ical (management cont rol), and operat ional
decisions.
• Unst ruct ured, semist ruct ured, and st ruct ured decisions.
• All level of management and knowledge workers in t he
organizat ion.
• All maj or f unct ional, product or line of business, and geographic
divisions of t he organizat ion.
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Direct usage of minicomputer by programmers only
Ethernet
UNIX-based minicomputer platform(s)
E-mail,
etc
Network gateway
External
database
input
Windows 2000 based platforms :
- Spreadsheets for personal financial models.
- All other models run on minicomputer.
- Lotus notes.
- Electronic mail.
User interface
Graphical query
tools, SQL
Relational
DBMS
Model management
system
Database
Model base
Query definition
Financial model development
Simulation model development
Figure 2. Specific DSS Archit ect ure
2. 1. DSS and Client/ Server Comput ing
Many organizat ions deal wit h t he af orement ioned disadvant ages by
providing individual users, via t heir deskt op comput ers, wit h access t o dat a
on t he cent ral comput er. The deskt op comput ers t hen handle t he
comput at ions and ot her processes of a DSS. These processes usually
include support ing t he usual element s of t oday’ s easy t o use graphical user
int erf aces, such as windows, pull-down menus, and t he use of a mouse or
ot her point ing device. This approach is ref erred t o as client / server
comput ing. The syst em st oring t he dat abase is called a server. The server
syst em act s as a dat a reposit ory. The client syst em, t ypicall y on t he user’ s
deskt op, runs t he applicat ion using dat a f rom t he server. The result is a
close mat ching of each part ner’ s capabilit ies t o it s role in t he overall
syst em.
The advant ages of using a client / server comput ing are client / server
comput ing allows each part of an overal l applicat ion t o run in a
hardware/ sof t ware environment opt imized f or t hat part and t hat part
alone, whet her it demands a large dat abase, high speed comput ing. This
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Decision Support Syst em Archit ect ure, Hardware, and Operat ing Syst em Plat f orms
(Radi ant Vi ct or Imbar )
opt imizat ion t end t o minimize t he overall cost of t he syst em. Anot her
advant ages is because t he client / server approach is increasingly popul ar,
many applicat ions and sof t ware development t ools are designed t o run in a
client / server environment . Organizat ions can of t en reduse development
t ime and cost by using t his.
The disadvant ages of using a client / server syst em are t he complexit ies of
applicat ion development and management are much great er in a
client / server syst em t han t hey are on a single comput er, securit y also
becomes a maj or concern. Securit y f eat ures, perhaps incl uding f irewalls t o
limit access f rom out side t he organizat ion.
2. 2. The Int ernet and Client/ Server Computing in DSS
The Int ernet is a giant worldwide inf ormat ion source. The World Wide
Web, especially, provides easy access t o inf ormat ion on a wealt h of t opics.
Much of t his inf ormat ion can be usef ul f or decision making. It is possible t o
t ake t his same t echnology and t his same int erf ace and use t hem t o access
a corporat e DSS. The Web archit ect ure, t he HyperText Markup Language
(HTML) f or developing Web pages, t he Java l anguage t hat most browsers
can int erpret , t he JavaScript language f or commands added t o HTML, and
t he st andards t hat Web browsers must f ollow def ine, in ef f ect , a plat f orm
f or applicat ions. DSS can use t his plat f orm so t hat any user wit h a Web
browser can access t hose DSS easily. The advant ages are it is accessible
f rom anywhere in t he indust rialized world and t he web can be accessed via
any t ype of hardware. The disadvant ages is web access can be slow
because a lot of grahics added t o web pages. The hardware and
communicat ion l inks needed t o provide f ast access may be more expensive
t han local area net work
2. 3. DSS Using Shared Data on a Separate System
It is not necessary f or t he server in a client / server syst em t o be t he cent ral
corporat e comput er t hat st ores t he live, operat ional dat abase. It is of t en a
good idea t o ext ract meaningf ul decision support dat a f rom operat ional
dat abase and load it int o a dif f erent comput er.
The linked-syst em approach is ef f ect ive when t he applicat ion alloes f or a
decision support dat abase t hat is separat e f rom t he f irm’ s t ransact ion
processing dat abase. As you know, such a dat abase is of t en called a dat a
warehouse.
Because corporat e dat a warehouses t end t o be large, t he servers required
are correspondingly large. Mult iprocessors, which harness t he power of t wo
t o several dozen processors t o a single overall t ask, are usef ul here
because t hey increase t he comput ing power of a syst em beyond t hat of a
single processor wit hout requiring t he expensive t echnologies used in
mainf rames or supercomput ers.
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A dat a warehouse is a read-onl y dat abase. It s users cannot updat e t he
“ live” organizat ional inf ormat ion. That limit s t he use of a dat a warehouse
t o sit uat ions where decision makers access corporat e inf ormat ion but do
not change it . If an applicat ion running in t his environment must updat e
t he main dat abase, it must be done in some ot her way. One approach is t o
have DSS comput er mimic a t ransact ion processing user and submit
t ransact ions.
The Advant ages of using Shared Dat a on a Separat e Syst em are DSS
hardware need not t o be shared wit h ot her applicat ions, so DSS response
t ime is preserved no mat t er what is going on elsewhere in t he organizat ion
and t he DSS hardware can be opt imized f or t hat purpose and t hat purpose
alone. The disadvant ages are t he need t o t ransf er dat a bet ween t wo
syst ems and users who access bot h syst ems may need t wo t ypes of
t erminals or may have t o f ollow complex net work logon procedures t o
access t he correct one. Swit ching f rom one t o anot her may be t ime
consuming.
2. 4. DSS on a Stand-Alone System
DSS t hat do not access a large cent ral dat abase are candidat es f or st andalone syst ems. A st and-alone DSS can be run on a comput er dedicat ed t o
t he DSS t ask or on a mult iple-user comput er used in t ime-sharing mode.
Being “ st and-alone” is a mat t er of degree. The absol ut ely, t ot ally, 100
percent st and-alone syst em is almost as rare as t he unicorn. Even users of
nominally st and-al one syst em exchange f iles via universal serial bus (USB)
or send e-mail via a dial-up modem link. What t he t erm really means here
is t hat t he operat ion of t he DSS doesn’ t depend on a regul ar connect ion t o
anot her comput er. Such a connect ion probabl y does exist and can be used
f or DSS-rel at ed purposes, such as sending it s result s t o a colleague f or
review and comment . The advant ages of using a st and alone syst em are
t he syst em can be t ot ally opt imized f or DSS and t he complexit y of sharing
a resource wit h ot her users is avoided. The disadvant ages of using a st and
alone syst em are any dat a t he syst em requires must be provided by it s
users and it may be dif f icult t o int egrat e a st and alone syst em wit h
corporat e applicat ions at a lat er dat e.
3. Choosing a DSS Hardware Environment
Here is a l ist of quest ions t hat can ask t o help make t he choice among t he
previousl y list ed opt ions. As a group, t hough, t heir answers will help point
out t he right DSS hardware approach :
1. Are t here any corporat e policies t hat must f ollow ? If t here are,
t hey may narrow t he choice by mandat ing one opt ion, or by
eliminat ing some opt ions.
2. How large and widespread will t he DSS user communit y be ? If it is
large and widespread, will it s members use exact ly t he same
applicat ion, variat ions on one appl icat ion, or dif f erent applicat ions
? When t hey use t he same applicat ion, do t hey use it
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Decision Support Syst em Archit ect ure, Hardware, and Operat ing Syst em Plat f orms
(Radi ant Vi ct or Imbar )
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
independent ly, or must t heir usage be coordinat ed t hrough a shared
dat abase or in any ot her way ? The more similarit y, t he more
sharing, t he more shoul d look t oward shared syst ems and LAN wit h
high-end servers. The l ess sharing and similarit y, t he more power
probabl y belongs on individual deskt ops.
Are most of t he prospect ive users already using a part icul ar syst em
? If so, see if it can be used as is or wit h modif icat ions such as
modest upgrade or int erconnect ion via a LAN.
Is t here a corporat e mainf rame wit h suf f icient capacit y, or t o which
suf f icient capacit y can be added at reasonable cost ?
Is anot her powerf ul server syst em avail able, linked t o t hat
mainf rame or not , wit h suf f icient capacit y ?
Do prospect ive users al ready have microcomput ers or workst at ions
t hat can handle t he applicat ion ?
If new syst ems are required, will t he exist ing cent ral syst em be
able t o share dat a wit h t hem ? t o shoul der an appl icat ion j oint ly
wit h t hem ?
Do t he necessary development t ools exist f or any of t hose syst ems ?
Are t hey already wit hin t he organizat ion , available f rom t he
syst em vendor, or available f rom t hird part ies ?
Wit h which of t hese syst ems and t ools, if any, are t he prospect ive
DSS developers already f amiliar ?
Does t he applicat ion require access t o a dat abase ? If so, does t hat
dat abase already exist ? If not , is it t o be derived f rom corporat e
dat a or f rom a separat e source ? If f rom corporat e dat a, how up t o
dat e should t he dat abase be f or t he applicat ion t o f unct ion ?
Does t he applicat ion’ s use of t he corporat e dat abase require onl y
t he abilit y t o read t he dat a, or must it also be able t o updat e t he
dat a ?
How much processing power does t he applicat ion require ? How
much dat a st orage capacit y ?
Are prospect ive users capable of perf orming (and willing t o
perf orm) basic syst em administ rat ion t asks, such as inst alling
sof t ware and backing up dat a f iles ?
Summary
DSS are inf ormat ion syst ems whose primary purpose is t o provide
knowledge workers wit h inf ormat ion. Common charact erist ics of most or
all DSS include t heir use by managers and ot her knowledge workers, t heir
use of a dat abase and t heir use of models. DSS are general ly used when a
comput er cannot be programmed t o make a decision f or all cases. They
support , but do not repl ace, human decision makers.
Bef ore embarking on t he development of any maj or DSS, we should have a
clear idea of bot h it s archit ect ure and t he overall DSS archit ect ure of t he
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organizat ion. Knowing t hese will help t o communicat e t he DSS vision t o
management and will help t o plan syst ems t hat will cont inue t o meet user
needs in t he f ut ure. DSS can run on a variet y of dif f erent plat f orms. Each
t ype has bot h advant ages and disadvant ages, which of t en make one bet t er
t han anot her in a specif ic sit uat ion. An exist ing mult iuser comput er may be
suit able when it has adequat e capacit y, prospect ive DSS users are already
using it , and rapid access t o cent ral corporat e dat a is required.
It is not necessary t o provide decision support dat a direct l y f rom t he live
dat abase. Inst ead, t he necessary dat a can be consolidat ed int o a
specialized decision support dat abase, of t en called a dat a warehouse.
While t his is not absolut ely necessary, dat a warehouse are of t en housed on
separat e syst ems f rom t he operat ional dat abase. In ot her sit uat ions,
model-orient ed or process-orient ed DSS of t en do not need a dat abase at
all. In t hese cases a st and-alone DSS, usuall y running on t he user’ s deskt op
syst em wit h no act ive connect ions t o t he out side world, may be t he best
solut ion.
BIBLIOGRAPHY
Mallach, Ef rem G (2000). Deci si on Suppor t and Dat a War ehouse Syst em:
McGraw-Hill.
St air, Ralph M. , and George W. Reynolds (1997). Pr i nci pl es of Inf or mat i on
Syst ems: A Manager i al Appr oach: Cambridge.
Mart in, E. Wainwright , Daniel W. DeHayes, Jef f rey A. Hof f er, and Wil liam
C. Perkins (1991).
Managi ng Inf or mat i on Technol ogy: What
Manager s Need t o Know : Macmill an, New York.
Demarest , Mark. , Technology and Policy in Decision Support
Syst ems, ht t p: / / dssresources. com/ papers/ f eat ures/ demarest 05/ dem
arest 07082005. ht ml . Tgl Akses 10 November 2006.
ht t p: / / en. wikipedia. org/ wiki/ Decision_support _syst em.
November 2006.
Access dat e 10
ht t p: / / elearning. dmst . aueb. gr/ mis/ Cases/ Nederlandse_Spoorwegn/ Case/
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