Decision Support Systems Constitute a Class of Computer

8/8/2019 Decision Support Systems Constitute a Class of Computer

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Decision support systems constitute a class of computer-basedinformation systemsincluding

knowledge-based systems that support decision-making activities.

DSSs serve the management level of the organization and help to take decisions, which may berapidly changing and not easily specified in advance.

Contents

[hide]

1 Overview

2 History 3 Taxonomies

4 Architecture

o 4.1 Development Frameworks

5 Classifying DSS

6 Applications 7 Benefits of DSS

8 See also

9 References

10 Further reading

[edit] Overview

A Decision Support System (DSS) is a class of information systems (including but not limited

to computerized systems) that support business and organizational decision-making activities. Aproperly designed DSS is an interactive software-based system intended to help decision makers

compile useful information from a combination of raw data, documents, personal knowledge, orbusiness models to identify and solve problems and make decisions.

Typical information that a decision support application might gather and present are:

inventories of all of your current information assets (including legacy and relational data

sources, cubes, data warehouses, and data marts), comparative sales figures between one week and the next,

projected revenue figures based on new product sales assumptions.

[edit] History

According to Keen (1978)[1], the concept of decision support has evolved from two main areas ofresearch: The theoretical studies of organizational decision making done at the Carnegie Institute

of Technologyduring the late 1950s and early 1960s, and the technical work on interactive

computer systems, mainly carried out at theMassachusetts Institute of Technology in the 1960s.
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[1] It is considered that the concept of DSS became an area of research of its own in the middle of

the 1970s, before gaining in intensity during the 1980s. In the middle and late 1980s,executive

information systems (EIS), group decision support systems (GDSS), and organizational decisionsupport systems (ODSS) evolved from the single user and model-oriented DSS.

According to Sol (1987)[2]

the definition and scope of DSS has been migrating over the years. Inthe 1970s DSS was described as "a computer based system to aid decision making". Late 1970s

the DSS movement started focusing on "interactive computer-based systems which helpdecision-makers utilize data bases and models to solve ill-structured problems". In the 1980s

DSS should provide systems "using suitable and available technology to improve effectiveness

of managerial and professional activities", and end 1980s DSS faced a new challenge towards thedesign of intelligent workstations.[2]

In 1987 Texas Instrumentscompleted development of the Gate Assignment Display System

(GADS) forUnited Airlines. This decision support system is credited with significantly reducing

travel delays by aiding the management of ground operations at various airports, beginning with

O'Hare International Airport in Chicagoand Stapleton Airport inDenverColorado.

[3][4]

Beginning in about 1990, data warehousingand on-line analytical processing (OLAP) began

broadening the realm of DSS. As the turn of the millennium approached, new Web-based

analytical applications were introduced.

The advent of better and better reporting technologies has seen DSS start to emerge as a critical

component ofmanagement design. Examples of this can be seen in the intense amount of

discussion of DSS in the education environment.

DSS also have a weak connection to the user interfaceparadigm ofhypertext. Both the

University of VermontPROMIS system (for medical decision making) and the Carnegie MellonZOG/KMS system (for military and business decision making) were decision support systems

which also were major breakthroughs in user interface research. Furthermore, although hypertextresearchers have generally been concerned withinformation overload, certain researchers,

notably Douglas Engelbart, have been focused on decision makers in particular.

[edit] Taxonomies

As with the definition, there is no universally-accepted taxonomyof DSS either. Differentauthors propose different classifications. Using the relationship with the user as the criterion,

Haettenschwiler[5] differentiatespassive, active, and cooperative DSS. Apassive DSSis a system

that aids the process of decision making, but that cannot bring out explicit decision suggestionsor solutions. An active DSScan bring out such decision suggestions or solutions. A cooperative

DSSallows the decision maker (or its advisor) to modify, complete, or refine the decision

suggestions provided by the system, before sending them back to the system for validation. The

system again improves, completes, and refines the suggestions of the decision maker and sendsthem back to her for validation. The whole process then starts again, until a consolidated solution

is generated.
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Another taxonomy for DSS has been created by Daniel Power. Using the mode of assistance as

the criterion, Power differentiates communication-driven DSS, data-driven DSS, document-

driven DSS, knowledge-driven DSS, and model-driven DSS.[6]

A communication-driven DSS supports more than one person working on a shared task;

examples include integrated tools like Microsoft's NetMeeting orGroove[7]

A data-driven DSS or data-oriented DSS emphasizes access to and manipulation of a

time series of internal company data and, sometimes, external data. A document-driven DSS manages, retrieves, and manipulates unstructured information

in a variety of electronic formats.

A knowledge-driven DSS provides specializedproblem-solving expertise stored as facts,rules, procedures, or in similar structures.[6]

A model-driven DSS emphasizes access to and manipulation of a statistical, financial,

optimization, or simulation model. Model-driven DSS use data and parameters providedby users to assist decision makers in analyzing a situation; they are not necessarily data-

intensive. Dicodessis an example of anopen sourcemodel-driven DSS generator[8].

Using scope as the criterion, Power[9] differentiates enterprise-wide DSSand desktop DSS. Anenterprise-wide DSSis linked to large data warehouses and serves many managers in thecompany. A desktop, single-user DSSis a small system that runs on an individual manager's PC.

[edit] Architecture

Design of a DroughtMitigation Decision Support System.

Three fundamental components of a DSS architecture are:[5][6][10][11][12]

1. the database (orknowledge base),2. the model (i.e., the decision context and user criteria), and

3. the user interface.

The usersthemselves are also important components of the architecture.[5][12]
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[edit] Development Frameworks

DSS systems are not entirely different from other systems and require a structured approach.

Such a framework includes people, technology, and the development approach.[10]

DSS technology levels (of hardware and software) may include:

1. The actual application that will be used by the user. This is the part of the application thatallows the decision maker to make decisions in a particular problem area. The user can

act upon that particular problem.

2. Generator contains Hardware/software environment that allows people to easily develop

specific DSS applications. This level makes use of case tools or systems such as Crystal,AIMMS, and iThink.

3. Tools include lower level hardware/software. DSS generators including special

languages, function libraries and linking modules

An iterative developmental approach allows for the DSS to be changed and redesigned at variousintervals. Once the system is designed, it will need to be tested and revised for the desired

outcome.

[edit] Classifying DSS

There are several ways to classify DSS applications. Not every DSS fits neatly into one category,

but a mix of two or more architecture in one.

Holsapple and Whinston[13]classify DSS into the following six frameworks: Text-oriented DSS,

Database-oriented DSS, Spreadsheet-oriented DSS, Solver-oriented DSS, Rule-oriented DSS,

and Compound DSS.

A compound DSS is the most popular classification for a DSS. It is a hybrid system that includes

two or more of the five basic structures described by Holsapple and Whinston[13].

The support given by DSS can be separated into three distinct, interrelated categories[14]:

Personal Support, Group Support, and Organizational Support.

DSS components may be classified as:

1. Inputs: Factors, numbers, and characteristics to analyze

2. User Knowledge and Expertise: Inputs requiring manual analysis by the user3. Outputs: Transformed data from which DSS "decisions" are generated4. Decisions: Results generated by the DSS based on user criteria

DSSs which perform selected cognitive decision-making functions and are based on artificial

intelligence orintelligent agentstechnologies are called Intelligent Decision Support Systems

(IDSS)[15].
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The nascent field ofDecision engineering treats the decision itself as an engineered object, and

applies engineering principles such as Design and Quality assurance to an explicit representation

of the elements that make up a decision.

[edit] Applications

As mentioned above, there are theoretical possibilities of building such systems in any

knowledge domain.

One example is the Clinical decision support system formedical diagnosis. Other examplesinclude a bank loan officer verifying the credit of a loan applicant or an engineering firm that has

bids on several projects and wants to know if they can be competitive with their costs.

DSS is extensively used in business and management. Executive dashboard and other business

performance software allow faster decision making, identification of negative trends, and betterallocation of business resources.

A growing area of DSS application, concepts, principles, and techniques is in agricultural

production, marketing for sustainable development. For example, the DSSAT4 package [16][17],

developed through financial support of USAID during the 80's and 90's, has allowed rapidassessment of several agricultural production systems around the world to facilitate decision-

making at the farm and policy levels. There are, however, many constraints to the successful

adoption on DSS in agriculture[18].

DSS are also prevalent in forest management where the long planning time frame demandsspecific requirements. All aspects of Forest management, from log transportation, harvest

scheduling to sustainability and ecosystem protection have been addressed by modern DSSs. A

comprehensive list and discussion of all available systems in forest management is beingcompiled under the COST action Forsys

A specific example concerns the Canadian National Railway system, which tests its equipment

on a regular basis using a decision support system. A problem faced by any railroad is worn-out

or defective rails, which can result in hundreds of derailments per year. Under a DSS, CNmanaged to decrease the incidence of derailments at the same time other companies were

experiencing an increase.

DSS has many applications that have already been spoken about. However, it can be used in any

field where organization is necessary. Additionally, a DSS can be designed to help make

decisions on the stock market, or deciding which area or segment to market a product toward.

CACI has begun integrating simulation and decision support systems. CACI defines three

levels of simulation model maturity. Level 1 models are traditional desktop simulation models

that are executed within the native software package. These often require a simulation expert toimplement modifications, run scenarios, and analyze results. Level 2 models embed the

modeling engine in a web application that allows the decision maker to make process and

parameter changes without the assistance of an analyst. Level 3 models are also embedded in a
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web-based application but are tied to real-time operational data. The execution of level 3

models can be triggered automatically based on this real-time data and the corresponding results

can be displayed on the managers desktop showing the prevailing trends and predictiveanalytics given the current processes and state of the system. The advantage of this approach is

that level 1 models developed for the FDA projects can migrate to level 2 and 3 models in

support of decision support, production/operations management, process/work flowmanagement, and predictive analytics. This approach involves developing and maintaining

reusable models that allow decision makers to easily define and extract business level

information (e.g., process metrics). Level 1 models are decomposed into their business objectsand stored in a database. All process information is stored in the database, including activity,

resource, and costing data. The database becomes a template library that users can access to

build, change, and modify their own unique process flows and then use simulation to study their

performance in an iterative manner.

[edit] Benefits of DSS

1. Improves personal efficiency2. Expedites problem solving (speed up the progress of problems solving in an organization)

3. Facilitates interpersonal communication4. Promotes learning or training

5. Increases organizational control

6. Generates new evidence in support of a decision7. Creates a competitive advantage over competition

8. Encourages exploration and discovery on the part of the decision maker

9. Reveals new approaches to thinking about the problem space10. Helps automate the managerial processes.

Decision Support Systems DSS (definition)
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Decision Support Systems (DSS) are a specific class of computerized information system that

supports business and organizational decision-making activities. A properly designed DSS is an

interactive software-based system intended to help decision makers compile useful informationfrom raw data, documents, personal knowledge, and/or business models to identify and solve

problems and make decisions.

Typical information that a decision support application might gather and present would

be:

Accessing all of your current information assets, including legacy and relational data

sources, cubes, data warehouses, and data marts

Comparative sales figures between one week and the next

Projected revenue figures based on new product sales assumptions The consequences of different decision alternatives, given past experience in a context

that is described

Information Builders' WebFOCUS reporting software is ideally suited for building decisionsupport systems due to its wide reach of data, interactive facilities, ad hoc reporting capabilities,

quick development times, and simple Web-based deployment.

Information Builders and iWay Software Professional Services specialize in building custom-

tailored Web decision support systems. We offerservice packagesdesigned for quickimplementations, and we use the latest technologies to incorporate leading-edge capabilities into

our solutions including a wide range of wireless and mobile options.
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Definition:A Decision Support System (DSS) is an umbrella term used to describeany computer application that enhances the users ability to make decisions. More

specifically, the term is usually used to describe a computer-based system designedto help decision-makers use data, knowledge and communications technology to

identify problems and make decisions to solve those problems.

Types of DSS

Since the definition of Decision Support Systems can be stretched to include almostany application that processes data there is some confusion as to exactly what

constitutes a DSS. In an effort to clarify the term, DS systems can be separated intoseven broad categories, each aiding decision making by different methods.

* Communications Driven DSS

A C-D DSS is a type of DSS that enhances decision-making by enablingcommunication and sharing of information between groups of people. At its most

basic level a C-D DSS could be a simple threaded e-mail. At its most complex itcould be a web-conferencing application or interactive video.

Communication-Driven DSS will exhibit at least one of the following characteristics:

Supports coordination and collaboration between two or more people;Facilitates information sharing;

Enables communication between groups of people;Supports group decisions.

* Data-Driven DSS

Data-driven DSS are a form of support system that focuses on the provision ofinternal (and sometimes external) data to aid decision making. Most often this will

come in the form of a data warehouse a database designed to store data in such away as to allow for its querying and analysis by users.

Another example of a data-driven DSS would be a Geographic Information System(GIS), which can be used to visually represent geographically dependant data using

maps.

* Document-Driven DSS

Document-driven DSS are support systemsdesigned to convert documents into

valuable business data. While data-drivenDSS rely on data that is already in a

standardised format that lends itself todatabase storage and analysis, document-driven DSS makes use of data that cannot

easily be standardised and stored.
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The three primary forms of data used in document driven DSS are:

Oral (i.e. transcribed conversations);Written (i.e. reports, memos, e-mail and other correspondence);

Video (i.e. TV commercials and news reports).

None of these formats lend themselves easily to standardised database storage andanalysis, so managers require DSS tools to convert them into data that can be

valuable in the decision making process.

Document-driven DSS is the newest field of study in Decision Support Systems.Examples of document-driven tools can be found in Internet search engines,

designed to sift through vast volumes of unsorted data through the use of keywordsearches.

* Knowledge-Driven DSS

Knowledge-driven DSS are systems designed to recommend actions to users.

Typically, knowledge-driven systems are designed to sift through large volumes ofdata, identify hidden patterns in that data and present recommendations based on

those patterns.

* Model-Driven DSS

Model-driven support systems incorporate the ability to manipulate data togenerate statistical and financial reports, as well as simulation models, to aid

decision-makers. Model-based decision support systems can be extremely useful inforecasting the effects of changes in business processes, as they can use past data

to answer complex what-if questions for decision makers.

In addition to these basic types of DSS there are also two additional factors:whether the DSS is spreadsheet-based, web-based or something else entirely.

* Spreadsheet-based DSS

Model- and Data-driven DS systems can be built using spreadsheets. Spreadsheetsoffer decision-makers easy to understand representations of large amounts of data.

Additionally, spreadsheet data is arranged in such a way as to make it easy toconvert the data into visualisations to further aid decision-makers.

* Web-based DSS

Any type of DSS can be web-based. The term simply describes any decision supportsystem that is operated through the interface of a web browser, even if the data

used for decision support remains confined to a legacy system such as a datawarehouse.

Scope of DSS

In addition to these basic types of Decision Support System there are also two
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separate categories used to define systems.

* Enterprise-wide DSS

Enterprise-wide DS systems are systems that are linked into large data warehouses,and offer decision support to managers at all levels of an enterprise. Enterprise-

wide systems will typically be basic, general use systems that can perform a widevariety of functions.

* Desktop DSS

Desktop DS systems are much smaller applications designed to be run from adesktop PC. While these systems may well be linked into a data warehouse or other

large volume of data, they will typically be more limited in scope.

An example of a desktop DSS is Microsoft Excel, the desktop spreadsheetapplication.
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An Executive Information System (EIS) is a type ofmanagement information systemintendedto facilitate and support the information and decision-makingneeds of senior executives by

providing easy access to both internal and external information relevant to meeting the strategic

goals of the organization. It is commonly considered as a specialized form of a Decision SupportSystem (DSS) [1]

The emphasis of EIS is on graphical displays and easy-to-use user interfaces. They offer strong

reporting and drill-down capabilities. In general, EIS are enterprise-wide DSS that help top-level

executives analyze, compare, and highlight trends in importantvariables so that they can monitorperformance and identify opportunities and problems. EIS and data warehousingtechnologies

are converging in the marketplace.

In recent years, the term EIS has lost popularity in favour ofBusiness Intelligence (with the sub

areas of reporting, analytics, anddigital dashboards).

Contents

[hide]

1 History

2 Components

o 2.1 Hardware

o 2.2 Software

o 2.3 User Interface

o 2.4 Telecommunication

3 Applicationso 3.1 Manufacturing

o 3.2 Marketing

o 3.3 Financial

4 Advantages and Disadvantages EISo 4.1 Advantages of EIS

o 4.2 Disadvantages of EIS

5 Future Trends

6 See also

7 References

8 External links

[edit] History

Traditionally, executive information systems were developed as mainframe computer-basedprograms. The purpose was to package a companys data and to provide sales performance or

market research statistics for decision makers, such as financial officers, marketing directors, and
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chief executive officers, who were not necessarily well acquainted with computers. The objective

was to develop computer applications that would highlight information to satisfy senior

executives needs. Typically, an EIS provides data that would only need to support executivelevel decisions instead of the data for all the company.

Today, the application of EIS is not only in typical corporate hierarchies, but also at personalcomputers on a local area network. EIS now cross computer hardware platforms and integrate

information stored on mainframes, personal computer systems, and minicomputers. As someclient service companies adopt the latest enterprise information systems, employees can use their

personal computers to get access to the companys data and decide which data are relevant for

their decision makings. This arrangement makes all users able to customize their access to theproper companys data and provide relevant information to both upper and lower levels in

companies.

[edit] Components

The components of an EIS can typically be classified as:

[edit] Hardware

When talking about hardwarefor an EIS environment, we should focus on the hardware thatmeet the executives needs. The executive must be put first and the executives needs must be

defined before the hardware can be selected. The basic computer hardware needed for a typical

EIS includes four components:

1. Input data-entry devices. These devices allow the executive to enter, verify, and updatedata immediately;

2. The central processing unit (CPU), which is the kernel because it controls the othercomputer system components;

3. Data storage files. The executive can use this part to save useful business information,

and this part also help the executive to search historical business information easily;

4. Output devices, which provide a visual or permanent record for the executive to save orread. This device refers to the visual output device or printer.

In addition, with the advent of local area networks (LAN), several EIS products for networked

workstations became available. These systems require less support and less expensive computer

hardware. They also increase access of the EIS information to many more users within acompany.

[edit] Software

Choosing the appropriate software is vital to design an effective EIS.[citation needed] Therefore, thesoftware components and how they integrate the data into one system are very important. The

basic software needed for a typical EIS includes four components:

1. Text base software. The most common form of text are probably documents;
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2. Database. Heterogeneous databases residing on a range of vendor-specific and open

computer platforms help executives access both internal and external data;3. Graphic base. Graphics can turn volumes of text and statistics into visual information for

executives. Typical graphic types are: time series charts, scatter diagrams,maps, motion

graphics, sequence charts, and comparison-oriented graphs (i.e.,bar charts);

4. Model base. The EIS models contain routine and special statistical, financial, and otherquantitative analysis.

Perhaps a more difficult problem for executives is choosing from a range of highly technical

software packages. Ease of use, responsiveness to executives' requests, and price are all

reasonable considerations. Further, it should be considered whether the package can run onexisting hardware.

[edit] User Interface

An EIS needs to be efficient to retrieve relevant data for decision makers, so the user interface is

very important. Several types of interfaces can be available to the EIS structure, such asscheduled reports, questions/answers, menu driven, command language, natural language, andinput/output. It is crucial that the interface must fit the decision makers decision-making style. If

the executive is not comfortable with the information questions/answers style, the EIS will not be

fully utilized. The ideal interface for an EIS would be simple to use and highly flexible,

providing consistent performance, reflecting the executives world, and containing helpinformation.

[edit] Telecommunication

As decentralizing is becoming the current trend in companies,telecommunicationswill play a

pivotal role in networked information systems. Transmitting data from one place to another hasbecome crucial for establishing a reliable network. In addition, telecommunications within an

EIS can accelerate the need for access to distributed data.

[edit] Applications

EIS enables executives to find those data according to user-defined criteria and promote

information-based insight and understanding. Unlike a traditional management information

system presentation, EIS can distinguish between vital and seldom-used data, and track differentkey critical activities for executives, both which are helpful in evaluating if the company is

meeting its corporate objectives. After realizing its advantages, people have applied EIS in many

areas, especially, in manufacturing, marketing, and finance areas.

[edit] Manufacturing

Basically, manufacturing is the transformation of raw materials into finished goods for sale, orintermediate processes involving the production or finishing of semi-manufactures. It is a large

branch of industry and of secondary production. Manufacturing operational control focuses on
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day-to-day operations, and the central idea of this process is effectiveness and efficiency. To

produce meaningful managerial and operational information for controlling manufacturing

operations, the executive has to make changes in the decision processes. EIS provides theevaluation of vendors and buyers, the evaluation of purchased materials and parts, and analysis

of critical purchasing areas. Therefore, the executive can oversee and review purchasing

operations effectively with EIS. In addition, because production planning and control dependsheavily on the plants data base and its communications with all manufacturing work centers,

EIS also provides an approach to improve production planning and control.

[edit] Marketing

In an organization, marketing executives role is to create the future. Their main duty is

managing available marketing resources to create a more effective future. For this, they needmake judgments about risk and uncertainty of a project and its impact on the company in short

term and long term. To assist marketing executives in making effective marketing decisions, an

EIS can be applied. EIS provides an approach to sales forecasting, which can allow the market

executive to compare sales forecast with past sales. EIS also offers an approach to product price,which is found in venture analysis. The market executive can evaluate pricing as related to

competition along with the relationship of product quality with price charged. In summary, EIS

software package enables marketing executives to manipulate the data by looking for trends,performing audits of the sales data, and calculating totals, averages, changes, variances, or ratios.

All of these sales analysis functions help marketing executives to make final decisions.

[edit] Financial

A financial analysis is one of the most important steps to companies today. The executive needs

to use financial ratios and cash flow analysis to estimate the trends and make capital investment

decisions. An EIS is a responsibility-oriented approach that integrates planning or budgetingwith control of performance reporting, and it can be extremely helpful to finance executives.

Basically, EIS focuses on accountability of financial performance and it recognizes the

importance of cost standards and flexible budgeting in developing the quality of informationprovided for all executive levels. EIS enables executives to focus more on the long-term basis of

current year and beyond, which means that the executive not only can manage a sufficient flow

to maintain current operations but also can figure out how to expand operations that are

contemplated over the coming years. Also, the combination of EIS andEDI environment canhelp cash managers to review the companys financial structure so that the best method of

financing for an accepted capital project can be concluded. In addition, the EIS is a good tool to

help the executive to review financial ratios, highlight financial trends and analyze a companys

performance and its competitors.

[edit] Advantages and Disadvantages EIS

[edit] Advantages of EIS
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Easy for upper-level executives to use, extensive computer experience is not required in

operations

Provides timely delivery of company summary information

Information that is provided is better understood

Filters data for management

Improves to tracking information Offers efficiency to decision makers

[edit] Disadvantages of EIS

System dependent

Limited functionality, by design

Information overload for some managers

Benefits hard to quantify

High implementation costs

System may become slow, large, and hard to manage

Need good internal processes for data management May lead to less reliable and less secure data

[edit] Future Trends

The future of executive info systems will not be bound by mainframe computer systems. This

trend allows executives escaping from learning different computer operating systems andsubstantially decreases the implementation costs for companies. Because utilizing existing

software applications lies in this trend, executives will also eliminate the need to learn a new or

special language for the EIS package. Future executive information systems will not onlyprovide a system that supports senior executives, but also contain the information needs for

middle managers. The future executive information systems will become diverse because ofintegrating potential new applications and technology into the systems, such as incorporatingartificial intelligence (AI) and integrating multimedia characteristics and ISDNtechnology into

an EIS. EIS - timely, efficient and effective in supporting the decision making process.
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Executive Information Systems

Definition:An Executive Information System (EIS) is a set of management tools supporting theinformation and decision-making needs of management by combining information availablewithin the organisation with external information in an analytical framework.

EIS are targeted at management needs to quickly assess the status of a business or section ofbusiness. These packages are aimed firmly at the type of business user who needs instant and upto date understanding of critical business information to aid decision making.

The idea behind an EIS is that information can be collated and displayed to the user withoutmanipulation or further processing. The user can then quickly see the status of his chosen

department or function, enabling them to concentrate on decision making. Generally an EIS is

configured to display data such as order backlogs, open sales,purchase order backlogs,shipments, receipts and pending orders. This information can then be used to make executive

decisions at a strategic level.

The emphasis of the system as a whole is the easy to use interface and the integration with avariety of data sources. It offers strong reporting and data mining capabilities which can provide

all the data the executive is likely to need. Traditionally the interface was menu driven with

either reports, or text presentation. Newer systems, and especially the newerBusinessIntelligence systems, which are replacing EIS, have a dashboard or scorecard type display.

Before these systems became available, decision makers had to rely on disparate spreadsheetsand reports which slowed down the decision making process. Now massive amounts of relevant
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information can be accessed in seconds. The two main aspects of an EIS system are integration

and visualisation. The newest method of visualisation is the Dashboardand Scorecard. The

Dashboard is one screen that presents key data and organisational information on an almost realtime and integrated basis. The Scorecard is another one screen display with measurement metrics

which can give a percentile view of whatever criteria the executive chooses.

Behind these two front end screens can be an immense data processing infrastructure, or a couple

of integrated databases, depending entirely on the organisation that is using the system. The

backbone of the system is traditional server hardware and a fast network. The EIS software itselfis run from here and presented to the executive over this network. The databases needs to be

fully integrated into the system and have real-time connections both in and out. This information

then needs to be collated, verified, processed and presented to the end user, so a real-time

connection into the EIS core is necessary.

Executive Information Systems come in two distinct types: ones that are data driven, and ones

that are model driven. Data driven systems interface with databases and data warehouses. They

collate information from different sources and presents them to the user in an integrateddashboard style screen. Model driven systems use forecasting, simulations and decision tree like

processes to present the data.

As with any emerging and progressive market, service providers are continually improving their

products and offering new ways of doing business. Modern EIS systems can also present

industry trend information and competitor behaviour trends if needed. They can filter andanalyse data; create graphs, charts and scenario generations; and offer many other options for

presenting data.

There are a number of ways to link decision making to organisational performance. From a

decision maker's perspective these tools provide an excellent way of viewing data. Outcomes

displayed include single metrics, trend analyses, demographics, market shares and a myriad ofother options. The simple interface makes it quick and easy to navigate and call the information

required.

For a system that seems to offer business so much, it is used by relatively few organisations.

Current estimates indicate that as few as 10% of businesses use EIS systems. One of the reasons

for this is the complexity of the system and support infrastructure. It is difficult to create such a

system and populate it effectively. Combining all the necessary systems and data sources can bea daunting task, and seems to put many businesses off implementing it. The system vendors have

addressed this issue by offering turnkey solutions for potential clients. Companies like Actuate

and Oracle are both offering complete out of the box Executive Information Systems, and thesearen't the only ones. Expense is also an issue. Once the initial cost is calculated, there is the

additional cost of support infrastructure, training, and the means of making the company data

meaningful to the system.

Does EIS warrant all of this expense? Green King certainly thinks so. They installed a Cognos

system in 2003 and their first few reports illustrated business opportunities in excess of

250,000. The AA is also using a Business Objects variant of an EIS system and they expect a
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return of 300% in three years. (Guardian 31/7/03)

An effective Executive Information System isn't something you can just set up and leave it to doits work. Its success depends on the support and timely accurate data it gets to be able to provide

something meaningful. It can provide the information executives need to make educated

decisions quickly and effectively. An EIS can provide a competitive edge to business strategythat can pay for itself in a very short space of time.


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Group Decision Support Systems (GDSS) are a class ofelectronic meeting systems, a

collaboration technology designed to support meetings and group work [1]. GDSS are distinct

from computer supported cooperative work (CSCW) technologies as GDSS are more focused ontask support, whereas CSCW tools provide general communication support[1].

Group Decision Support Systems (GDSS) are referred to as a Group Support Systems (GSS)

orelectronic meeting systems since they share similar foundations and functionalities.

Significant research supports measuring impacts of:

Adapting human factors for these technologies,

Facilitating interdisciplinarycollaboration, and

Promoting effective organizational learning.

Group Decision Support Systems are categorized within a time-place paradigm.

Whethersynchronous orasynchronous the systems matrix comprises:

same time AND same place

same time BUT different place

different time AND different place

different time BUT same place

Commercial software products that support GDSS practices over the Internet in bothsynchronous and asynchronous settings include facilitate.com, smartSpeed Connect, ThinkTank

and ynSyte's WIQ.

There is also an initiative to create open-source software that can support similar group processes

in education, where this category of software has been called a Discussion Support System. SeeCoFFEE.
http://en.wikipedia.org/wiki/Electronic_meeting_systemhttp://en.wikipedia.org/wiki/Electronic_meeting_systemhttp://en.wikipedia.org/wiki/Electronic_meeting_systemhttp://en.wikipedia.org/wiki/Group_decision_support_systems#cite_note-Dennis.2C_A.R._1988-0http://en.wikipedia.org/wiki/CSCWhttp://en.wikipedia.org/wiki/Group_decision_support_systems#cite_note-Dennis.2C_A.R._1988-0http://en.wikipedia.org/wiki/Group_decision_support_systems#cite_note-Dennis.2C_A.R._1988-0http://en.wikipedia.org/wiki/Electronic_meeting_systemhttp://en.wikipedia.org/wiki/Human_factorshttp://en.wikipedia.org/wiki/Interdisciplinaryhttp://en.wikipedia.org/wiki/Interdisciplinaryhttp://en.wikipedia.org/wiki/Collaborationhttp://en.wikipedia.org/wiki/Organizational_learninghttp://en.wikipedia.org/wiki/Synchronoushttp://en.wikipedia.org/wiki/Asynchronoushttp://en.wikipedia.org/wiki/CoFFEEhttp://en.wikipedia.org/wiki/Electronic_meeting_systemhttp://en.wikipedia.org/wiki/Group_decision_support_systems#cite_note-Dennis.2C_A.R._1988-0http://en.wikipedia.org/wiki/CSCWhttp://en.wikipedia.org/wiki/Group_decision_support_systems#cite_note-Dennis.2C_A.R._1988-0http://en.wikipedia.org/wiki/Electronic_meeting_systemhttp://en.wikipedia.org/wiki/Human_factorshttp://en.wikipedia.org/wiki/Interdisciplinaryhttp://en.wikipedia.org/wiki/Collaborationhttp://en.wikipedia.org/wiki/Organizational_learninghttp://en.wikipedia.org/wiki/Synchronoushttp://en.wikipedia.org/wiki/Asynchronoushttp://en.wikipedia.org/wiki/CoFFEE


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An expert system is softwarethat attempts to provide an answer to a problem, or clarify

uncertainties where normally one or more humanexpertswould need to be consulted. Expert

systems are most common in a specificproblem domain, and is a traditional application and/orsubfield ofartificial intelligence. A wide variety of methods can be used to simulate the

performance of the expert however common to most or all are 1) the creation of aknowledge

basewhich uses some knowledge representationformalism to capture the Subject MatterExpert's (SME) knowledge and 2) a process of gathering that knowledge from the SME and

codifying it according to the formalism, which is called knowledge engineering. Expert systems

may or may not have learning components but a third common element is that once the system isdeveloped it is proven by being placed in the same real world problem solving situation as the

human SME, typically as an aid to human workers or a supplement to some information system.

Expert systems were introduced by researchers in the Stanford Heuristic Programming Project,

Edward Feigenbaum, PI, with the Dendral and Mycin systems. Principal contributors to thetechnology were Bruce Buchanan, Edward Shortliffe, Randall Davis, William vanMelle, Carli

Scott, and others at Stanford. Expert systems were among the first truly successful forms of AI

software.

[1]

[2]

[3]

[4]

[5]

[6]

The topic of expert systems has many points of contact with generalsystems theory, operations research,business process reengineeringand various topics in appliedmathematics and management science.

Contents

[hide]

1 Aspectso 1.1 Certainty factors

o 1.2 Chaining

o 1.3 Software architectureo 1.4 End user

o 1.5 Explanation system

o 1.6 Comparison to problem-solving systems

o 1.7 Individuals interacted with

o 1.8 Inference rule

o 1.9 Procedure node interface

2 Application

o 2.1 Advantages and disadvantages

2.1.1 Disadvantages

o 2.2 Types of problems solved

3 Shells or Inference Engine

4 See also

5 References

6 Bibliography

7 External links
http://en.wikipedia.org/wiki/Softwarehttp://en.wikipedia.org/wiki/Softwarehttp://en.wikipedia.org/wiki/Experthttp://en.wikipedia.org/wiki/Experthttp://en.wikipedia.org/wiki/Experthttp://en.wikipedia.org/wiki/Problem_domainhttp://en.wikipedia.org/wiki/Artificial_intelligencehttp://en.wikipedia.org/wiki/Artificial_intelligencehttp://en.wikipedia.org/wiki/Knowledge_basehttp://en.wikipedia.org/wiki/Knowledge_basehttp://en.wikipedia.org/wiki/Knowledge_basehttp://en.wikipedia.org/wiki/Knowledge_basehttp://en.wikipedia.org/wiki/Knowledge_representationhttp://en.wikipedia.org/wiki/Knowledge_representationhttp://en.wikipedia.org/wiki/Subject_Matter_Experthttp://en.wikipedia.org/wiki/Subject_Matter_Experthttp://en.wikipedia.org/wiki/Knowledge_engineeringhttp://en.wikipedia.org/wiki/Edward_Feigenbaumhttp://en.wikipedia.org/wiki/Dendralhttp://en.wikipedia.org/wiki/Mycinhttp://en.wikipedia.org/wiki/Expert_system#cite_note-0http://en.wikipedia.org/wiki/Expert_system#cite_note-1http://en.wikipedia.org/wiki/Expert_system#cite_note-1http://en.wikipedia.org/wiki/Expert_system#cite_note-2http://en.wikipedia.org/wiki/Expert_system#cite_note-2http://en.wikipedia.org/wiki/Expert_system#cite_note-3http://en.wikipedia.org/wiki/Expert_system#cite_note-3http://en.wikipedia.org/wiki/Expert_system#cite_note-4http://en.wikipedia.org/wiki/Expert_system#cite_note-5http://en.wikipedia.org/wiki/Systems_theoryhttp://en.wikipedia.org/wiki/Operations_researchhttp://en.wikipedia.org/wiki/Business_process_reengineeringhttp://en.wikipedia.org/wiki/Business_process_reengineeringhttp://en.wikipedia.org/wiki/Business_process_reengineeringhttp://en.wikipedia.org/wiki/Applied_mathematicshttp://en.wikipedia.org/wiki/Applied_mathematicshttp://en.wikipedia.org/wiki/Management_sciencehttp://en.wikipedia.org/wiki/Expert_systemhttp://en.wikipedia.org/wiki/Expert_system#Aspectshttp://en.wikipedia.org/wiki/Expert_system#Certainty_factorshttp://en.wikipedia.org/wiki/Expert_system#Chaininghttp://en.wikipedia.org/wiki/Expert_system#Software_architecturehttp://en.wikipedia.org/wiki/Expert_system#End_userhttp://en.wikipedia.org/wiki/Expert_system#Explanation_systemhttp://en.wikipedia.org/wiki/Expert_system#Comparison_to_problem-solving_systemshttp://en.wikipedia.org/wiki/Expert_system#Individuals_interacted_withhttp://en.wikipedia.org/wiki/Expert_system#Inference_rulehttp://en.wikipedia.org/wiki/Expert_system#Procedure_node_interfacehttp://en.wikipedia.org/wiki/Expert_system#Applicationhttp://en.wikipedia.org/wiki/Expert_system#Advantages_and_disadvantageshttp://en.wikipedia.org/wiki/Expert_system#Disadvantageshttp://en.wikipedia.org/wiki/Expert_system#Types_of_problems_solvedhttp://en.wikipedia.org/wiki/Expert_system#Shells_or_Inference_Enginehttp://en.wikipedia.org/wiki/Expert_system#See_alsohttp://en.wikipedia.org/wiki/Expert_system#Referenceshttp://en.wikipedia.org/wiki/Expert_system#Bibliographyhttp://en.wikipedia.org/wiki/Expert_system#External_linkshttp://en.wikipedia.org/wiki/Softwarehttp://en.wikipedia.org/wiki/Experthttp://en.wikipedia.org/wiki/Problem_domainhttp://en.wikipedia.org/wiki/Artificial_intelligencehttp://en.wikipedia.org/wiki/Knowledge_basehttp://en.wikipedia.org/wiki/Knowledge_basehttp://en.wikipedia.org/wiki/Knowledge_representationhttp://en.wikipedia.org/wiki/Subject_Matter_Experthttp://en.wikipedia.org/wiki/Subject_Matter_Experthttp://en.wikipedia.org/wiki/Knowledge_engineeringhttp://en.wikipedia.org/wiki/Edward_Feigenbaumhttp://en.wikipedia.org/wiki/Dendralhttp://en.wikipedia.org/wiki/Mycinhttp://en.wikipedia.org/wiki/Expert_system#cite_note-0http://en.wikipedia.org/wiki/Expert_system#cite_note-1http://en.wikipedia.org/wiki/Expert_system#cite_note-2http://en.wikipedia.org/wiki/Expert_system#cite_note-3http://en.wikipedia.org/wiki/Expert_system#cite_note-4http://en.wikipedia.org/wiki/Expert_system#cite_note-5http://en.wikipedia.org/wiki/Systems_theoryhttp://en.wikipedia.org/wiki/Operations_researchhttp://en.wikipedia.org/wiki/Business_process_reengineeringhttp://en.wikipedia.org/wiki/Applied_mathematicshttp://en.wikipedia.org/wiki/Applied_mathematicshttp://en.wikipedia.org/wiki/Management_sciencehttp://en.wikipedia.org/wiki/Expert_systemhttp://en.wikipedia.org/wiki/Expert_system#Aspectshttp://en.wikipedia.org/wiki/Expert_system#Certainty_factorshttp://en.wikipedia.org/wiki/Expert_system#Chaininghttp://en.wikipedia.org/wiki/Expert_system#Software_architecturehttp://en.wikipedia.org/wiki/Expert_system#End_userhttp://en.wikipedia.org/wiki/Expert_system#Explanation_systemhttp://en.wikipedia.org/wiki/Expert_system#Comparison_to_problem-solving_systemshttp://en.wikipedia.org/wiki/Expert_system#Individuals_interacted_withhttp://en.wikipedia.org/wiki/Expert_system#Inference_rulehttp://en.wikipedia.org/wiki/Expert_system#Procedure_node_interfacehttp://en.wikipedia.org/wiki/Expert_system#Applicationhttp://en.wikipedia.org/wiki/Expert_system#Advantages_and_disadvantageshttp://en.wikipedia.org/wiki/Expert_system#Disadvantageshttp://en.wikipedia.org/wiki/Expert_system#Types_of_problems_solvedhttp://en.wikipedia.org/wiki/Expert_system#Shells_or_Inference_Enginehttp://en.wikipedia.org/wiki/Expert_system#See_alsohttp://en.wikipedia.org/wiki/Expert_system#Referenceshttp://en.wikipedia.org/wiki/Expert_system#Bibliographyhttp://en.wikipedia.org/wiki/Expert_system#External_links


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[edit] Aspects

[edit] Certainty factors

The MYCIN rule-based expert system introduced a quasi-probabilistic approach called certainty

factors, whose rationale is explained below.

A human, when reasoning, does not always conclude things with 100% confidence: he mightventure, "If Fritz is green, then he is probably a frog" (after all, he might be a chameleon). This

type of reasoning can be imitated by using numeric values called confidences. For example, if it

is known that Fritz is green, it might be concluded with 0.85 confidence that he is a frog; or, if itis known that he is a frog, it might be concluded with 0.95 confidence that he hops. These

certainty factor (CF) numbers quantify uncertainty in the degree to which the available evidence

supports a hypothesis. They represent a degree of confirmation and are not probabilities in a

Bayesian sense. The CF calculus, developed by Shortliffe & Buchanan, increases or decreasesthe CF associated with a hypothesis as each new piece of evidence becomes available. It can be

mapped to a probability update, although degrees of confirmation are not expected to obey thelaws of probability. It is important to note, for example, that evidence for hypothesis H may havenothing to contribute to the degree to which Not_h is confirmed or disconfirmed (e.g., although a

fever lends some support to a diagnosis of infection, fever does not disconfirm alternative

hypotheses) and that the sum of CFs of many competing hypotheses may be greater than one(i.e., many hypotheses may be well confirmed on the available evidence).

The CF approach to rule-based expert system design does not have a widespread following, in

part because of the difficulty of meaningfully assigning CFs a priori. (The above example of

green creatures being likely to be frogs is excessively naive.) Alternative approaches to quasi-probabilistic reasoning in expert systems involve Fuzzy Logic, which has a firmer mathematical

foundation. Also, rule-engine shells such asDroolsandJess do not support probabilitymanipulation: they use an alternative mechanism called salience, which is used to prioritize theorder of evaluation of activated rules.

In certain areas, as in the tax-advice scenarios discussed later, probabilistic approaches are not

acceptable.(Being likely to be 95% correct means a 5% probability of being wrong.) The rules

that are defined in such systems have no exceptions: they are only a means of achieving softwareflexibility when external circumstances change frequently. Because rules are stored as data, the

core software does not need to be rebuilt each time changes to federal and state tax codes are

announced.

[edit] Chaining

Two methods of reasoning when using inference rulesare backward chaining and forward

chaining.
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Forward chaining starts with the data available and uses the inference rules to conclude more

data until a desired goal is reached. Aninference engineusing forward chaining searches the

inference rules until it finds one in which the ifclause is known to be true. It then concludes thethen clause and adds this information to itsdata. It would continue to do this until a goal is

reached. Because the data available determines which inference rules are used, this method is

also called data driven.

Backward chaining starts with a list of goals and works backwards to see if there is data whichwill allow it to conclude any of these goals. An inference engine using backward chaining would

search the inference rules until it finds one which has a then clause that matches a desired goal. If

the ifclause of that inference rule is not known to be true, then it is added to the list of goals. Forexample, suppose a rule base contains

1. (1) IF X is green THEN X is a frog. (Confidence Factor: +1%)

2. (2) IF X is green THEN X is NOT a frog. (Confidence Factor: +99%)

3. (3) IF X is a frog THEN X hops. (Confidence Factor: +50%)

4. (4) IF X is a frog THEN X does NOT hop. (Confidence Factor +50%)

Suppose a goal is to conclude that Fritz hops. Let X = "Fritz". The rule base would be searched

and rule (3) would be selected because its conclusion (the then clause) matches the goal. It is not

known that Fritz is a frog, so this "if" statement is added to the goal list. The rule base is againsearched and this time rule (1) is selected because its then clause matches the new goal just

added to the list. This time, the ifclause (Fritz is green) is known to be true and the goal that

Fritz hops is concluded. Because the list of goals determines which rules are selected and used,this method is calledgoal driven.

However, that if we use confidence factors in even a simplistic fashion, (for example, by

multiplying them together as if they were like soft probabilities), we get a result that is knownwith a confidence factor of only one-half of 1%. (by multiplying 0.5 x 0.01 = 0.005). This is veryuseful, since without confidence factors, we might erroneously conclude with certainty that a sea

turtle named Fritz hops just by virtue of being green. In Classical logic or Aristotelian term

logicsystems, there are no probabilities or confidence factors; all facts are regarded as certain.(An ancient example from Aristotle: "Socrates is a man. All men are mortal. Thus Socrates is

mortal").

In real world applications, few facts are known with absolute certainty and the opposite of a

given statement may be more likely to be true ("Green things in the pet store are not frogs, withprobability or confidence factor 99% in my pet store survey)". Thus it is often useful when

building such systems to try and prove both the goal and the opposite of a given goal to see

which is more likely.

[edit] Software architecture

The following general points about expert systems and their architecture have been illustrated.
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1. The sequence of steps taken to reach a conclusion is dynamically synthesized with

each new case. It is not explicitly programmed when the system is built.

2. Expert systems can process multiple values for any problem parameter. This permitsmore than one line of reasoning to be pursued and the results of incomplete (not fully

determined) reasoning to be presented.

3. Problem solving is accomplished by applying specific knowledge rather than specifictechnique. This is a key idea in expert systems technology. It reflects the belief that

human experts do not process their knowledge differently from others, but they do

possess different knowledge. With thisphilosophy, when one finds that their expertsystem does not produce the desired results, work begins to expand the knowledge base,

not to re-program theprocedures.

There are various expert systems in which a rulebase and an inference engine cooperate to

simulate the reasoning process that a human expert pursues in analyzing a problem and arrivingat a conclusion. In these systems, in order to simulate the human reasoning process, a vast

amount of knowledge needed to be stored in the knowledge base. Generally, the knowledge base

of such an expert system consisted of a relatively large number of "if then" type of statementsthat were interrelated in a manner that, in theory at least, resembled the sequence of mental stepsthat were involved in the human reasoning process.

Because of the need for large storage capacities and related programs to store the rulebase, most

expert systems have, in the past, been run only on large information handling systems. Recently,the storage capacity of personal computers has increased to a point where it is becoming possible

to consider running some types of simple expert systems onpersonal computers.

In someapplications of expert systems, the nature of the application and the amount of stored

information necessary to simulate the human reasoning processfor that application is just too

vast to store in the activememory of a computer. In other applications of expert systems, thenature of the application is such that not all of the information is always needed in the reasoning

process. An example of this latter type application would be the use of an expert system to

diagnose a data processing system comprising many separate components, some of which areoptional. When that type of expert system employs a single integrated rulebase to diagnose the

minimum system configuration of the data processing system, much of the rulebase is not

required since many of the components which are optional units of the system will not be presentin the system. Nevertheless, earlier expert systems require the entire rulebase to be stored since

all the rules were, in effect, chained or linked together by the structure of the rulebase.

Decision Support Systems Constitute a Class of Computer

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