RESEARCH PROCESS ORIENTED INTELLIGENT DECISION

SUPPORT SYSTEM FRAMEWORK

Yanjun Ji and Juanqiong Gou

School of Economic and Management, Beijing Jiaotong University, Easten Jiaoda Road, Beijing, China

Keywords: Intelligent decision support systems, Hierarchy framework, Research-oriented process.

Abstract: According to the overall development of intelligent decision support system (IDSS), we sum up the features

of some existing basic framework and lacks in dealing with problems. Based on the basic framework and

hierarchical thinking of intelligent decision support system, we make the research progress-oriented

hierarchical framework of intelligent decision support system and briefly describe the function of each

module. Finally, starting from the acquisition of knowledge system in hierarchical framework, we analyse

the knowledge’s collection and processing in intelligent decision support system, explicit of tacit knowledge

and module-chain solutions of and process knowledge.

1 INTRODUCTION

Intelligent Decision Support System (IDSS) is one

kind of Decision Support System (DSS) developed

by American scholar Bonczek and others in the 80s.

Its core idea is to combine artificial intelligence with

other relevant scientific findings to make an

intelligent decision support system, which can deal

with not only quantitative issues but also qualitative

issues.

IDSS is the combination of artificial intelligence

(AI) and decision support systems (DSS), it is

actually composite system, which integrates decision

support system (DSS) and expert system (ES). ES

simulates the ability of experts to solve problems

and to support the policy-makers to make their

decisions. DSS, which is built on the integration of

data processing and model-driven, focused on

quantitative analysis during the 1970’. Because of

having no human intelligence, lacking of knowledge

and expert support, the result of processing

uncertainty and unstructured problems was

unsatisfactory. And knowledge-based expert system

was smart enough to focus on qualitative analysis.

IDSS, which integrates ES on the basis of DSS, was

developed since then. DSS’s main parts include the

database system, model base system, human-

computer interaction system components, etc. ES is

made of knowledge, its management system and the

inference machine. The traditional way to combine

these two was to separate ES in different parts of

DSS, or the whole ES to act as a separate component

inside DSS.

IDSS, which takes advantage of both ES and

DSS, fully achieve the combination of qualitative

and quantitative. Its problem-solving ability has

greatly improved.

After years of in-depth research, intelligent

decision support system theory has been infiltrated

into the structure, problem solving and other aspects,

which has significant impact on strategy methods

and process. IDSS research has developed from

decision-making components to the components of

the comprehensive integration, from quantitative-

relied models to knowledge-based intelligent

decision-making methods, to make intelligent

decision support system theory and method more

mature. Although the theory of intelligent decision

support approach is becoming more and more

mature, but there is not too much real success,

because there is great relation between intelligence

reality and system knowledge domination, system

application framework and integration methods and

so on.

534

Ji Y. and Gou J..

RESEARCH PROCESS ORIENTED INTELLIGENT DECISION SUPPORT SYSTEM FRAMEWORK.

DOI: 10.5220/0003587105340538

In Proceedings of the 13th International Conference on Enterprise Information Systems (SSE-2011), pages 534-538

ISBN: 978-989-8425-53-9

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

2 THE HIERARCHICAL

STRUCTURE FRAMEWORK

OF INTELLIGENT DECISION

SUPPORT SYSTEM

2.1 The Hierarchical Structure

Framework of Intelligent Decision

Support System

2.1.1 The Integrated Architecture based on

Components

Since R.H. Sprgue brought up Two Libraries

(database and model base) theory of Intelligent

decision support system's basic architecture in 1980,

there has emerged the framework based X library

and the problem processing mode, and later it

developed into IDSS consisting of the problem-

solving ,human-computer interaction System, model

base systems, database systems and knowledge base

system, and knowledge systems, known as the

expert system (ES), consists of three parts including

the knowledge base, knowledge base management

system, inference engine. Its basic structure is shown

in Figure 1.

Figure 1: The three libraries basic framework of Intelligent

Decision Support.

When IDSS deal with a particular problem, it

will combines with the corresponding data into its

database, model base and knowledge base, or add a

related field database in its three-library system.

Based on this, there emerges five libraries system

structure which also includes methods and text

database. But with the increase of the number of

libraries in IDSS, the structure becomes complicated

as the function is enhanced, which make it difficult

to achieve.

2.1.2 The Structure based on the

Problem-Processing Mode

The DSS model structure based on issues-dealing

Bonczek proposed consists of knowledge system,

language system and the problem processing system.

Decision problems in which language subsystem

provides representation of decision problem and

interpretation of the decision-making structures;

knowledge subsystem is responsible for issues

related to decision-making; problem processing

subsystem gets decision problem from the language

subsystem, and through relevant knowledge

operation of knowledge subsystem, results in

rational decision-making results. The structure uses

a unified structure of knowledge- processing, which

is good to the expansion of system functions, and

can be considered as an evolutionary structure.

However, the model follows the idea of expert

system in problem solving, and can not fully reflect

the role of decision-makers in model structure and

model selection. As the knowledge required for

decision-making process are very complex, which

include factual knowledge, reasoning knowledge

and expertise knowledge in decision-making areas.

Yet such a system as a decision support system will

make the system too large and difficult to achieve,

for knowledge of this structure lack of

indiscrimination in knowledge concept and specific

issues in specific areas.

Therefore, oriented to the application for

different objects, the three library systems should be

optimized reasonably to enhance its coordination

and consistency, and it also need to be redefined the

framework in the problem solving process.

2.2 The Introduction of Hierarchical

Idea

The significant progress of General system theory

provides a new approach to intelligent decision

support system. The basic idea is: any complex

system can be viewed as a multi-model system. The

general system model commonly used has three

categories: input / output system model, target

acquisition systems model, hierarchical system

model. Therefore, the general system model can be

used as an important class of model problem in

description implementation. The introduction of

intelligent decision support method can effectively

achieve the control of the evaluation process. Based

on the classic three-library system of intelligent

decision support system, we optimize IDSS

framework referred to hierarchy, and the whole

system can be divided into the application layer,

RESEARCH PROCESS ORIENTED INTELLIGENT DECISION SUPPORT SYSTEM FRAMEWORK

535

business logic layer and data layer system, shown as

Figure 2.

Interface

Knowledge

Engineering

Process

Control

Computing

Technology

Manageme

Concluding

Query

Process

Decision

Support

System‐

Administrati

KnowledgeManagementSystem

KB MB DB

Application

Layer

Business

LogicLayer

DataLayer

Figure 2: IDSS system architecture based on the Hierarchy

model.

After using Hierarchy model, the IDSS top-down

framework is divided into three layers: application

layer, business logic layer and data layer.

2.2.1 Application Layer

The application layer mainly deals with all analysis

communication between business logic layer and

users, including 4 sub-modules: interactive interface

is responsible for passing user requests and results

display, etc., it is an important channel to for users to

interact with the system. Query processor interprets

the user's natural language into executable

statements for machine searching. Decision-making

is the presenting interface for knowledge the system.

2.2.2 Business Logic Layer

Business logic layer handles all activities related to

the evaluation process, which includes five modules,

namely, knowledge engineering module, process

control modules, processing operations module,

technology management module and the conclusions

generated module. And its overall responsibility is

knowledge focus processing, process control and

results analysis.

2.2.3 Data Layer

Data layer is mainly used for knowledge, models,

data extraction, and storage and call management.

3 THE KNOWLEDGE-BASED

PROCESS IN SYSTEM WITH

HIERARCHICAL MODEL

Knowledge Systems is the core components of

intelligent decision support system. For quite some

time, the decision support system is affected by the

structure of multi-database system, it also separate

data, model, method and knowledge to build

database and management. According to different

decision tools applied, there appears several decision

support system, such as model-relied system, data-

relied system and communication-relied system and

so on. Actually, during the decision-making process,

no matter what the decision-maker gets, from fact,

rules, method to the inferential process. All those

things are essential knowledge for making a decision.

To manage that knowledge together, it will make a

concise system structure, and also a unified dealing

method, which then become a main stream of IDSS

knowledge research.

3.1 Classification of Knowledge

Knowledge involves in IDSS mainly have three

kinds: descriptive knowledge, procedural knowledge

and reasoning knowledge. Descriptive knowledge

defines various environment-related status

information for the specific decision-making,

including past, present, future and assumed data and

information. Procedural knowledge defines process,

steps and strategies in the problem solving, such as

business-running rules, action programs. Reasoning

knowledge defines how to get valid conclusions in

various possible situations. The three kinds of

knowledge are corresponding to data, models,

indicators, methods in traditional multi-database

system, and the specified relation is shown in

figure3 below:

Figure 3: Classification of Knowledge in IDSS.

We can see there are not great differences

between descriptive knowledge & reasoning

knowledge and traditional multi-database. It needs

just slight modifications for the relational database

Reasoning

Knowledge

Descriptive

Knowledge

Indicator Models

Meth

ods

Proce

Rules

Data&

Information

Procedural

knowledge

ICEIS 2011 - 13th International Conference on Enterprise Information Systems

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in knowledge-based system. But for procedural

knowledge representation, such as the evaluation

process, it is difficult to achieve by traditional

methods, so it needs reasonable analysis and design.

3.2 Knowledge Engineering

Knowledge engineering design is based on expert

system, which is responsible for collection,

development of knowledge, access to external

information, process it by knowledge rules, store

information the form of database records to support

calling, comparing and choosing in the decision-

making process. In addition, an important function

of knowledge engineering is to interact with

knowledge workers, access to professional and

authoritative knowledge as the first reference of the

system operation control. The process is as shown in

Figure 4:

Figure 4: Schematic diagram of the knowledge

engineering process.

Ordinary users or knowledge workers and

external information are the three main sources for

knowledge engineering sources to obtain

information. Information got through these three

sources will experience knowledge extraction,

transformation and loading operations, access to

knowledge and analysis platform for analysis and

processing, filter out redundant information, and

store in knowledge base according to certain rules to

for the call.

In addition to knowledge library, the model

library can provide information about the limitations

and operating parameters on computing model. The

database can access a lot of knowledge through data

mining, data integration and other technology.

Conclusion is another important function of

knowledge engineering. It is responsible for

assessing outputs from different types of analysis

(including providing specific decision services based

on the track and analyse of user

behaviour) .Especially for the causal task; it will

screen out false or inconsistent results based on a

standard, and generates simple and profound

explanation of the model environment the user can

understand. In the process, it will use the generation

model and knowledge base, model base interaction,

deductive knowledge, analogical reasoning and

other techniques.

3.3 Dominance of Process and Other

Hidden Information

The knowledge management system is mainly

responsible for dominance of knowledge. It will

make the empirical and rational knowledge got from

a variety of information sources be structured and

formal processed, then dump them respectively, in a

particular machine readable format to knowledge

base, model base and database. Knowledge

management systems manage many knowledge

objects including data; text flow, validating model,

metadata, process, processing algorithms and the

corresponding operating software.to complete the

unified function.

Process is an important component of tacit

knowledge. A complete process mainly includes

model, index data, evaluation criteria and

operational control these four types of information.

Each model calls indicators and data in turn in

accordance with control information for processing

operations. Results operations carried out will be

determined by the evaluation criteria until the

satisfactory results are got. According to the results

of different judgments, the model will be called by

multiple times, and the model organization called in

accordance with the form of a chain is known as the

model chain. The model chain is shown as below.

Figure 5: Model chain of the process.

A model chain reflects a process, in control of all

kinds of information in the model chain running:

such as the evaluation value index which is stored in

the database as a record, then can save knowledge of

the process.

Process control controls the running process

according to a variety pre-qualification before the

model chain run , and provides the operation

interaction (including the analysis and tracking of

• Indicators

•Data

•Rules

• Operation contr

Model1

• Indicator

•Data

•Rules

• Operation contr

Model2

•Ect.

Ect.

• Indicator

•Data

•Rules

• Operation contr

Modeln

RESEARCH PROCESS ORIENTED INTELLIGENT DECISION SUPPORT SYSTEM FRAMEWORK

537

user preference), that is, in the form of Process

control platform to provide the tips and guidance of

parameters ,input and output, models, knowledge in

the process of decision-making process during user

Interaction.

4 CONCLUSIONS

Intelligent decision support system is a complex

system. Using logical framework can make the

system structure more clear and the integration of

the various parts easier. Good system structure also

laid the foundation for future system

implementation. Knowledge is the core of intelligent

system. How to externalize this knowledge, which

decides the system’s degree of intelligence e.g.

process, experience, becomes increasingly

important. Based on these, the paper provides a

reference for future researchers. Next research goal

is the integration of the various components within

the hierarchy framework.

This paper was supported by “the Fundamental Research

Funds for Central Universities (2009BAG12A10-2)”

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