RESEARCH AND PRACTICE OF GROUP DECISION SYSTEMS

Ma Shao-qiu

ShiMao Group, 37F. ONE Lujiazui, 68 Yin Cheng Road Central, Shanghai, China

Zheng Yan

BaoSteel Group,18F. BaoSteel Tower, 4 Pu Dian Road, Shanghai, China

Keywords: Business Intelligence (BI), Decision Support System (DSS), Data Warehouse (DW).

Abstract: In order to build an information system that support the Enterprise Group Decision Systems, we analyzed

the Information Model of Enterprise Group Decision Support System, presented the architecture of

enterprise group decision support systems which was based on data warehouse and business intelligence for

the entire group, presented solutions of model-driven enterprise group decision support systems by defining

the Decision Scheme Model and Decision-making Task Model, and applied in a real estate enterprise group.

Application shows that the architecture is feasible and effective.

1 INTRODUCTION

In the process of implementation of information

technology, Enterprise Group accumulating a large

number of decentralized business data of projects,

operations, finance, marketing, office, etc. How to

turn the mass distribution of the business data into

information for business decision-making has

become the core problem of Enterprise Group

Decision Support. Currently, research in Group

decision support systems which was based on data

warehouse have been numerous, but research for the

methods and techniques of industry group decision

support systems is still rare. By drawing on the

methods and techniques of some literature, using the

technology of data warehousing, data mining,

model-driven, etc. , we presented the hierarchical

structure of Enterprise Group Decision Support

System. By defining the Decision Scheme Model

and Decision-making Task Model, we presented

solutions of model-driven enterprise group decision

support systems. Finally, the proposed method and

technology were applied in a real estate enterprise

group, and achieved significant results.

2 INFORMATION MODEL OF

GROUP DECISION SUPPORT

SYSTEM

There are so many factors involved in decision-

making process, in order to obtain effective

decision-making information, reasonable

information model must be established. Through

analysis of related factors about Enterprise Group

Decision, Information Model of Group Decision

Support System are shown in Figure 1.

Figure 1 shows the related data which affect the

Enterprise Group Decisions, including groups data

(contain Project, Operations, Finance, Marketing,

Office, etc.) also includes the necessary external

information (such as market information). The

format and meaning of different data are not entirely

consistent. So only taking effective data

management methods can achieve business group

decision making. This paper completed the data

environment construction of Enterprise Group

Decision Support System by using data warehouse

architecture, achieved the enterprise group decision

support system functions by using the decision-

oriented model-driven approach.

287

Shao-qiu M. and Yan Z..

RESEARCH AND PRACTICE OF GROUP DECISION SYSTEMS.

DOI: 10.5220/0003603002870290

In Proceedings of the 13th International Conference on Enterprise Information Systems (BIS-2011), pages 287-290

ISBN: 978-989-8425-54-6

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

Figure 1: Information Model of Group Decision Support

System.

3 ARCHITECTURE OF GROUP

DECISION SUPPORT SYSTEM

3.1 Architecture of Data Warehouse

To support the collectivize enterprise groups

decision making, the use of the data warehouse can

provide enterprise groups the decision support data

environment, through hierarchical strategy to reduce

the complexity of data extraction and conversion.

The architecture of Enterprise Group data warehouse

is shown in Figure 2. The data warehouse can

provide these following services for decision support

analysis and processing:

(1) To extracted the data for analysis from the

OLTP database under the theme. During the

extraction process, it is demand to do classify, sum

or do statistical treatment with the raw data. The

processing of extraction is actually the re-

organization of the data.

(2) In the extraction process, it is demand to do

the data cleaning, that is to remove the unqualified

raw data, if necessary, the defect data must be

supplemented.

(3) When the theme of the analysis and decision-

making are changed, it can query and visit data by

topic.

(4) To use off-line mass storage, online disk

storage, memory, multilevel storage model to

address the huge amount of data and organizational

problems about dividing by topic and size.

Data extraction and purification, storage

organization etc., are the key technology of building

data warehousing. In addition, the design of data

warehouse should also pay special attention to the

size and dividing problems.

Figure 2: Distributed Data Warehouse of Enterprise

Group.

3.2 Hierarchy of Decision Support

System

Decision support systems using business intelligence

(BI) technology, the hierarchical structure is shown

in Figure 3:

Figure 3: Hierarchy of Decision Support System.

From this framework, we can see clearly about

the pattern of business intelligence architecture,

including Data Layer, Business Logic Layer and

Application Layer. Data Layer is basically the

process of ETL. Business Logic Layer is mainly the

process of OLAP and Data Mining. The Application

Layer mainly includes the process of data display,

analysis and performance analysis.

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3.3 Basic Structure of Decision Support

System

The basic structure of Decision Support System

mainly consists of four parts, namely, the part of

data, the part of model, the part of reasoning and the

part of HCI:

The part of data is a database system;

The part of model includes Model Library (MB)

and its management system (MBMS);

The part of reasoning is consisted by the

Knowledge Base (KB), Knowledge Base

Management System (KBMS) and the Inference

Engine;

The part of HCI is the HCI interface of decision

support system, for receiving and inspection user

requests, call the internal system function software

for decision making to make the model running ,data

calling and knowledge reasoning to achieve the

organic unity of solving the decision problems

effectively.

4 APPLICATION OF DECISION

SUPPORT SYSTEM

In the process of implementing the real estate

business group decision support system development

and application based on the distributed data

warehouse, involving developers, expert users,

decision-making users, data warehouse designers.

Developers are responsible for designing and

implementing the entire decision-making system,

and other work such as adding new decision

algorithm etc.

Expert users are responsible for

structuring Decision-making Task Model.

Decision-

making users are responsible for completing the

enterprise decision analysis based on the distributed

data warehouse.

Data warehouse designers are

responsible for defining the fact table of subject-

oriented data warehouse, completing data extraction,

transformation, loading and maintenance in the data

warehouse.

In the Enterprise Group Decision Support

System, the system functions are divided into 3

levels, decision information, decision analysis and

decision support. In decision-making layer, through

a common reporting system and graphical display

components, users can define the query criteria,

define the output columns and graphics display,

dynamically generated query statements or graphics.

In the decision analysis layer, the system uses

online analysis process mode, it is support of

digging other functions. In the decision support layer,

the system uses data mining technology, through the

decision-making model and decision algorithm, it

can find some market discipline, customer variation

trend and business models which are hard to find in

the report query.

Enterprise Group Decision Support System

coordinate the management needs, around the

business objective and risk indicators which the

group is concerned, through an integrated

information platform, so that the relevant

management and business process can be visible,

steerable, optimized to play a greater support on the

Group operations and decision in application of

information technology.

Meanwhile, the Enterprise Group Decision

System introduce a variety of models to assist aid

decision making. The land value increment tax

calculation and trial calculation model is provided.

The model can automatically get the actual value

and predicted value of costs, expenses, income, etc.

of the corresponding business accounting. So that

for ordinary residence and non-ordinary residence,

the use of two nationally recognized methods can

automatically calculating the corresponding tax rates

and the tax. On this basis, it can also provide a

variety of basis for decision-making by adjusting the

dynamic trial calculation of cost-plus factor and/or

income factor.

On the basis of land value increment tax

calculation and trial calculation model, the system

provides a profit, profit margin sensitivity analysis

model, which is shown in Figure 6. Through a

variety of data automatic acquisition and integrated

application, it can intelligence estimate the

ineffective and inefficient pricing range interval. By

adjusting the price and tax optimization at the same

time, it can significantly increase profits and profit

margins. The model has been the important tool and

means of a real estate to adjust the newly sale one’s

pricing adjustments.

5 CONCLUSIONS

The paper provided the whole framework of the

Enterprise Group Decision Support System on the

basis of data warehouse. Enterprise Group Data

warehouse provide a good data environment for

enterprise group decision support, by introducing

hierarchical strategy such as the decision scheme

layer, decision task model layer etc. It can reduce the

complexity of decision support systems, integrated

the data and decision-making algorithm, has a strong

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289

Figure 6: Profit, Profit margin sensitivity analysis.

versatility and scalability. Application shows that the

proposed method is feasible and effective. Next, we

will explore the INTERNET and the research and

application of the decision support system based on

the knowledge management, grid technology;

meanwhile, we will hammer at enhancing the input

and output means, such as the technology of

introduction of text to speech, speech recognition,

voiceprint recognition; we will also discuss the

customer-oriented decision support system.

REFERENCES

Li Qi-yan, 2007. Enterprise Business Intelligence Tutorial,

Tongji University Press. Shanghai.

Zhao Wei-dong, 2009. Business Intelligence. Tsinghua

University Press. Beijing.

Li Dong and Cai Jian, 2005. Decision Support System and

Knowledge Management System, Renmin University

Press. Beijing.

Turban E., Aronson J. E. and Liang Ding-peng, 2009.

Decision Support Systems and Intelligent Systems,

Engineering Industry Press. Beijing. 7

edition.

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