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. Definit ion of DSS and Hist ory 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 repl aces t hem. 2. ut il izes dat a and model s.
3. sol ves probl ems 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 acil it at ing decision processes).
By t he l at 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 model s t o hel p managers anal yze semi-st ruct ured probl ems. These diverse syst ems were cal l ed Decision Support Syst ems. From t hose earl y days, it was recognized t hat DSS coul d be designed t o support decision-makers at any l evel in an organizat ion. DSS coul d support operat ions, f inancial management and st rat egic decision-making. DSS coul d use spat ial dat a in a syst em l ike Geodat a Anal ysis and Displ ay Syst em (GADS) (cf . , Grace, 1976), st ruct ured mul t idimensional dat a and unst ruct ured document s (cf . , Swanson and Cul nan, 1978). A variet y of model s were used in DSS incl uding opt imizat ion and simul at ion. Al so, st at ist ical packages were recognized as t ool s f or buil ding DSS. Art if icial Int el l igence researchers began work on management and business expert syst ems in t he earl y 1980s.
In t he middl e and l at e 1980s, Execut ive Inf ormat ion Syst ems (EIS) evol ved f rom singl e user model -driven Decision Support syst ems and improved rel at ional dat abase product s. The f irst EIS used pre-def ined inf ormat ion screens and were maint ained by anal yst s f or senior execut ives.
Beginning in about 1990, dat a warehousing and On-Line Anal yt ical Processing (OLAP) began broadening t he real m of EIS and def ined a broader cat egory of Dat a-Driven DSS (cf . , Dhar and St ein, 1997). Nigel Pendse (1997) cl aims t he f irst Execut ive Inf ormat ion Syst em product was Pil ot Sof t ware’ s Command Cent er. He not es bot h mul t idimensional anal ysis and OLAP had origins in t he APL programming l anguage and in syst ems l ike 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 l y we can say t hat a DSS is a syst em f or making decisions. A decision is a choice bet ween al t ernat ives based on est imat es of t he val ues of t hose al t ernat ives. Support ing a decision means support ing t his choice by support ing t he est imat ion, t he eval uat ion and/ or t he comparison and choice. In pract ice ref erences t o DSS are usual l y ref erences t o comput er appl icat ions t hat perf orm such a support ing rol e.
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 sel f l ook l ike? What kinds of risks and const raint s are associat ed wit h t he decision-making process? How is t he out put of t he decision-decision-making process – a decision – eval uat ed, impl ement ed and t racked?
(Radi ant Vi ct or Imbar )
• a decision-maker: an individual or group charged wit h making a part icul ar decision
• a set of input s t o t he decision-making process: dat a, numerical or qual it at ive model s 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 cul t ural and psychol ogical norms and const raint s associat ed wit h decision-making
• t he decision-making process itself: a set of st eps, more or l ess wel l -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 put s from t he decision-making process, incl uding t he decisions t hemsel ves and (ideal l y) a set of crit eria f or eval uat ing decisions produced by t he process against t he set of needs, probl ems or obj ect ives t hat occasioned t he decision-making act ivit y in t he f irst pl ace.
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 al l most dat a mart and dat a warehousing environment s do t oday.
aspect s of dat a present at ion f ound in query t ool s. They are act ual decision making t ool s, l ike f aul t t ree anal ysis, Bayesian l ogic and model -based decision-making based on t hings l ike neural net works. • In cont ext devel opment , DSS environment s again provide t ool s, 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 probabil it ies, and ot her el ement s of t he l arger decision making cont ext .
• In decision-making, DSS environment s may aut omat e al l or part of t he decision-making process and of f er eval uat ions on t he opt imal decision. Expert syst ems and art if icial int el l igence environment s purport t o do t his, but t hey work onl y in very l imit ed cases, because of some f undament al f l aws in t he t echnol ogy (namel y, t heir inabil it y t o deal wit h non-binary, or f uzzy, choices, l ike “ it ’ s more l ikel y t hat we’ l l l ose market share t han win it , ” which is a rul e 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 el ement 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 impl ement ed/ propagat ed and (b) if t he ef f ect s of t he decision are as expect ed.
2. Defining The DSS Archit ect ure.
An import ant issue t o consider bef ore pl anning individual syst ems is devel oping an overal l 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 l aid out , what t ypes of t asks are al l ocat 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 worl d. Despit e t he unique nat ure of most DSS appl icat ions, several considerat ions t hat paral el t he devel opment of any ot her l arge-scal e sof t ware appl icat 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 l aid out wit h respect t o l ocat 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 l evel issues:
• Int eroperabil it y
(Radi ant Vi ct or Imbar )
can access and del iver it s inf ormat ion t o t he appropriat e end users at what ever l ocat ion or t ime period t hey may need it .
• Compat ibil it y
Compat ibil it y of syst ems, so t hat resources can be shared easil y and l everaged across t he organizat ion.
• Scal abil it y
Expandabil it y of syst ems, so t hat l imit ed singl e-f unct ion component s do not creat e bot t l enecks t hat obst ruct t he growt h of t he organizat ion.
The overal l archit ect ure of a DSS shoul d be l aid 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 l ay out a DSS archit ect ure must consider t he spect rum of DSS t hat t he organizat ion wil l 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.
• Al l l evel of management and knowl edge workers in t he organizat ion.
Database
Direct usage of minicomputer by programmers only
Ethernet
UNIX-based minicomputer platform(s)
E-mail, etc
Network gateway User interface
Graphical query tools, SQL
Relational DBMS
Model management system
Query definition
Windows 2000 based platforms :
- Spreadsheets for personal financial models. - All other models run on minicomputer. - Lotus notes.
- Electronic mail.
Financial model development Simulation model development
Model base External
database input
Figure 2. Specif ic 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 handl e t he comput at ions and ot her processes of a DSS. These processes usual l y incl ude support ing t he usual el ement s of t oday’ s easy t o use graphical user int erf aces, such as windows, pul l -down menus, and t he use of a mouse or ot her point ing device. This approach is ref erred t o as cl ient / server comput ing. The syst em st oring t he dat abase is cal l ed a server. The server syst em act s as a dat a reposit ory. The cl ient syst em, t ypical l y on t he user’ s deskt op, runs t he appl icat ion using dat a f rom t he server. The resul t is a cl ose mat ching of each part ner’ s capabil it ies t o it s rol e in t he overal l syst em.
(Radi ant Vi ct or Imbar )
opt imizat ion t end t o minimize t he overal l cost of t he syst em. Anot her advant ages is because t he cl ient / server approach is increasingl y popul ar, many appl icat ions and sof t ware devel opment t ool s are designed t o run in a cl ient / server environment . Organizat ions can of t en reduse devel opment t ime and cost by using t his.
The disadvant ages of using a cl ient / server syst em are t he compl exit ies of appl icat ion devel opment and management are much great er in a cl ient / server syst em t han t hey are on a singl e comput er, securit y al so becomes a maj or concern. Securit y f eat ures, perhaps incl uding f irewal l s t o l imit access f rom out side t he organizat ion.
2. 2. The Int ernet and Client / Server Comput ing in DSS
The Int ernet is a giant worl dwide inf ormat ion source. The Worl d Wide Web, especial l y, provides easy access t o inf ormat ion on a weal t h of t opics. Much of t his inf ormat ion can be usef ul f or decision making. It is possibl e t o t ake t his same t echnol ogy 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 devel oping Web pages, t he Java l anguage t hat most browsers can int erpret , t he JavaScript l anguage f or commands added t o HTML, and t he st andards t hat Web browsers must f ol l ow def ine, in ef f ect , a pl at f orm f or appl icat ions. DSS can use t his pl at f orm so t hat any user wit h a Web browser can access t hose DSS easil y. The advant ages are it is accessibl e f rom anywhere in t he indust rial ized worl d and t he web can be accessed via any t ype of hardware. The disadvant ages is web access can be sl ow because a l ot 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 l ocal area net work
2. 3. DSS Using Shared Dat a on a Separat e Syst em
It is not necessary f or t he server in a cl ient / server syst em t o be t he cent ral corporat e comput er t hat st ores t he l ive, 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 l oad it int o a dif f erent comput er.
The l inked-syst em approach is ef f ect ive when t he appl icat ion al l oes 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 cal l ed a dat a warehouse.
A dat a warehouse is a read-onl y dat abase. It s users cannot updat e t he “ l ive” organizat ional inf ormat ion. That l imit 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 appl icat 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 appl icat ions, so DSS response t ime is preserved no mat t er what is going on el sewhere in t he organizat ion and t he DSS hardware can be opt imized f or t hat purpose and t hat purpose al one. 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 erminal s or may have t o f ol l ow compl ex net work l ogon 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 St and-Alone Syst em
DSS t hat do not access a l arge cent ral dat abase are candidat es f or st and-al one syst ems. A st and-and-al one DSS can be run on a comput er dedicat ed t o t he DSS t ask or on a mul t ipl e-user comput er used in t ime-sharing mode. Being “ st and-al one” is a mat t er of degree. The absol ut el y, t ot al l y, 100 percent st and-al one syst em is al most as rare as t he unicorn. Even users of nominal l y st and-al one syst em exchange f il es via universal serial bus (USB) or send e-mail via a dial -up modem l ink. What t he t erm real l y 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 resul t s t o a col l eague f or review and comment . The advant ages of using a st and al one syst em are t he syst em can be t ot al l y opt imized f or DSS and t he compl exit y of sharing a resource wit h ot her users is avoided. The disadvant ages of using a st and al one syst em are any dat a t he syst em requires must be provided by it s users and it may be dif f icul t t o int egrat e a st and al one syst em wit h corporat e appl icat ions at a l at er dat e.
3. Choosing a DSS Hardware Environment
Here is a l ist of quest ions t hat can ask t o hel p make t he choice among t he previousl y l ist ed opt ions. As a group, t hough, t heir answers wil l hel p point out t he right DSS hardware approach :
1. Are t here any corporat e pol icies t hat must f ol l ow ? If t here are, t hey may narrow t he choice by mandat ing one opt ion, or by el iminat ing some opt ions.
(Radi ant Vi ct or Imbar )
independent l y, or must t heir usage be coordinat ed t hrough a shared dat abase or in any ot her way ? The more simil arit y, t he more sharing, t he more shoul d l ook t oward shared syst ems and LAN wit h high-end servers. The l ess sharing and simil arit y, t he more power probabl y bel ongs on individual deskt ops.
3. Are most of t he prospect ive users al ready 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.
4. 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 reasonabl e cost ?
5. Is anot her powerf ul server syst em avail abl e, l inked t o t hat mainf rame or not , wit h suf f icient capacit y ?
6. Do prospect ive users al ready have microcomput ers or workst at ions t hat can handl e t he appl icat ion ?
7. If new syst ems are required, wil l t he exist ing cent ral syst em be abl e t o share dat a wit h t hem ? t o shoul der an appl icat ion j oint l y wit h t hem ?
8. Do t he necessary devel opment t ool s exist f or any of t hose syst ems ? Are t hey al ready wit hin t he organizat ion , avail abl e f rom t he syst em vendor, or avail abl e f rom t hird part ies ?
9. Wit h which of t hese syst ems and t ool s, if any, are t he prospect ive DSS devel opers al ready f amil iar ?
10.Does t he appl icat ion require access t o a dat abase ? If so, does t hat dat abase al ready 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 shoul d t he dat abase be f or t he appl icat ion t o f unct ion ?
11.Does t he appl icat ion’ s use of t he corporat e dat abase require onl y t he abil it y t o read t he dat a, or must it al so be abl e t o updat e t he dat a ?
12.How much processing power does t he appl icat ion require ? How much dat a st orage capacit y ?
13.Are prospect ive users capabl e of perf orming (and wil l ing t o perf orm) basic syst em administ rat ion t asks, such as inst al l ing sof t ware and backing up dat a f il es ?
Summary
DSS are inf ormat ion syst ems whose primary purpose is t o provide knowl edge workers wit h inf ormat ion. Common charact erist ics of most or al l DSS incl ude t heir use by managers and ot her knowl edge workers, t heir use of a dat abase and t heir use of model s. DSS are general l y used when a comput er cannot be programmed t o make a decision f or al l cases. They support , but do not repl ace, human decision makers.
organizat ion. Knowing t hese wil l hel p t o communicat e t he DSS vision t o management and wil l hel p t o pl an syst ems t hat wil l cont inue t o meet user needs in t he f ut ure. DSS can run on a variet y of dif f erent pl at 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 mul t iuser comput er may be suit abl e when it has adequat e capaci t y, prospect ive DSS users are al ready 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 l ive dat abase. Inst ead, t he necessary dat a can be consol idat ed int o a special ized decision support dat abase, of t en cal l ed a dat a warehouse. Whil e t his is not absol ut el y 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 al l . In t hese cases a st and-al one DSS, usual l y running on t he user’ s deskt op syst em wit h no act ive connect ions t o t he out side worl d, may be t he best sol ut ion.
BIBLIOGRAPHY
Mal l ach, Ef rem G (2000). Deci si on Suppor t and Dat a War ehouse Syst em: McGraw-Hil l .
St air, Ral ph M. , and George W. Reynol ds (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 l iam C. Perkins (1991). Managi ng Inf or mat i on Technol ogy: What Manager s Need t o Know : Macmil l an, New York.
Demarest , Mark. , Technol ogy and Pol icy 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. Access dat e 10 November 2006.
