A research framework for Web-based open decision support systems

Knowledge-Based Systems 18 (2005) 309–319 www.elsevier.com/locate/knosys

A research framework for Web-based open decision support systems Yong Xiea, Hongwei Wanga,*, Janet Efstathioub b

a Institute of Systems Engineering, Huazhong University of Science and Technology, Wuhan 430074, China Manufacturing Systems Group, Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK

Received 28 October 2002; accepted 1 December 2004 Available online 15 December 2004

Abstract With the prevalence of the Web, most decision-makers are likely to use the Web to support their decision-making. Web-based technologies are leading a major stream of researching decision support systems (DSS). In this paper, we propose a formal definition and a conceptual framework for Web-based open DSS (WODSS). The formal definition gives an overall view of WODSS and creates a uniform research framework for various decision support systems. The conceptual framework based on browser/broker/server computing mode employs the electronic market to mediate decision-makers and providers, and facilitate sharing and reusing of decision resources. We also analyze the basic functions and develop an admitting model, a trading model and a competing model of electronic market in WODSS based on market theory in economics. These models reveal the key mechanisms that drive WODSS function efficiently. Finally, an illustrative example is studied to support the proposed ideas. q 2004 Published by Elsevier B.V. Keywords: Decision support systems; Electronic market; Decision resources; Web-based DSS

1. Introduction Although it appeared many years ago, the development and application of decision support system (DSS) is rather limited. The main factors that cause this situation are: † Poor maintainability: A decision-maker must take pains and time maintaining the DSS instead of concentrating on decision problems. Measures should be taken to shield decision-makers from burdensome maintenance. † Poor flexibility: DSS are often application-specific, and it is difficult to update the DSS according to decisionmakers’ needs. Because of the lack of flexibility, the application of DSS is greatly restricted. † Less reusability: Due to the restrictions of the framework, many existing DSS resources such as data, model and knowledge are unable to be reused by decisionmakers. Developers have to spend much money and * Corresponding author. E-mail address: [email protected] (H. Wang). 0950-7051/$ - see front matter q 2004 Published by Elsevier B.V. doi:10.1016/j.knosys.2004.12.001

human resources in repeated work. It is important to make decision resources reusable in order that all potential decision-makers share decision resources conveniently. The increasing use of the Web in DSS is providing an attractive framework to overcome the current limitations. As the Web becomes more and more popular, many researchers and organizations are beginning to focus on applying Web technologies to enhance DSS applications [2,4,12]. On the other hand, the enriched decision resources such as decision models, On-line Analysis Process (OLAP) tools and data mining tools are bringing enormous business opportunities to DSS developers. They can benefit from publishing and sharing their decision resources on the Internet. Apart from simple information, decision resources will be able to assist users to solve some complex problems. Because of the heterogeneousness of various decision resources, it is greatly necessary to describe them in open standard format such as Extensible Markup Language (XML) that is commonly used on the Web, which facilitates easier searching and management of decision resources. In order to investigate the viability of

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Web-based DSS, we should focus on the following questions: † What is the appropriate research framework for Webbased DSS? † How can providers publish and share their decision resources on the Web? † How can consumers or decision-makers find and access their preferred decision resources to assist them to solve decision problems? To address the above problems, we propose a research framework for Web-based open decision support systems (WODSS). In this framework, we design an electronic market for decision resources that consists of four components: consumer, electronic market, provider and decision resources provided by provider. The electronic market offers infrastructure services to help providers register decision resources and facilitate consumers to search and utilize decision resources to solve problems. We choose the Web and browser/broker/server computing mode as the platform of WODSS because they are valuable and viable for performing rapid and flexible access to information for both consumers and providers. The major innovation of our work is that we present a formal definition and a new research framework based on browser/broker/ server computing mode for WODSS. We also develop an admitting model, a trading model and a competing model of electronic market in WODSS and reveal the mechanisms that drive WODSS function efficiently. The rest of the paper is organized as follows. First, we take an overview of some related work in Section 2. Then we propose a formal definition of WODSS and compare it with the conventional DSS in Section 3. In Section 4, we give a new research framework based on browser/broker/ server computing mode and design an electronic market to mediate decision-makers and decision resource providers. We also analyze the transaction process of WODSS in Section 5. In Section 6, we introduce the three models of electronic market in WODSS. We also present an illustrative example to clarify the ideas discussed in the above sections in Section 7. Finally, we conclude with a summary and future research directions in Section 8.

2. Literature review Before we discuss our work, we need to review the related literature to give an overview of Web-based DSS. It can be traced back to 1995, a series of papers on using the Web and Internet for decision support were presented at the third international Conference of ISDSS (the International Society for Decision Support Systems) in Hong Kong. In the next year, a DSS/WWW Workshop was held as part of the IFIP Working Group 8.3 Conference on ‘Implementing Systems for Supporting Management Decisions: Concepts,

Methods and Experiences’ in London [12]. From then on, many researchers and organizations were engaged in Webbased DSS and made a lot of achievements. The famous known works are DecisionNet by Bhargava [1] and Open DSS by M.Goul [6]. DecisionNet aims to establish an electronic market for decision technology that consists of consumers, providers and brokers mediating consumers and providers. The market helps to bring together and provide services for matching consumers, providers and decision technology. Goul et al. proposed a set of protocols called Open DSS Protocols for DSS deployment on the Internet. In contrast to DecisionNet, it connects consumers and providers via the open DSS protocols instead of broker in DecisionNet. As for the decision resources concerned, Webbased DSS are to provide data services [10] and computing services or model services [3]. Jeusfeld et al. [5] advanced this idea to decision components on the Internet and designed a script language to connect the distributed decision components. Additionally, a few researchers pay attention to the Web support mechanisms for DSS. Power [4] investigates the support mechanisms and identifies five types of them: data-driven, model-driven, knowledgedriven, document-driven and communication-driven. Sridhar [7] focused on the data-dialog-model framework of DSS and explored in detail how Web technologies can improve the functions of the three components, respectively. He then presents taxonomy of major types of Web-based decision support. Ba et al. [8] designed a client–broker–server framework to achieve information integration in Web-based DSS. Kersten [11] developed the INSPIRE prototype to study and conduct negotiation via the Web in DSS. The above researches reveal that many researchers set forth their work about Web-based DSS from different points of view. We prefer to present a fundamental research approach of WODSS and propose a formal definition and new framework. Based on this framework, we also bring forward some key issues about WODSS.

3. The main ideas and formal definition of WODSS 3.1. Main ideas and some definitions There is no commonly accepted definition about Webbased DSS now. Power gave a definition of Web-based DSS [4]. The main ideas are that decision support systems are services on the Web. These services could be accessible to anyone with a problem and an Internet connection. Decision-makers only use ‘thin’ client browsers such as Internet explorer to support decision-making. Some researchers prefer that Web-based DSS are DSS implemented on the Web. We propose Web-based open DSS with two core ideas: ‘electronic market’ and ‘open’. An electronic market for decision resources acts as an intermediary between decision-makers and providers. It helps providers register decision resources to the electronic


market and facilitate consumers to search and utilize decision resources to solve problems. An electronic market offers a good platform for sharing and reusing decision resources, and it also provides planning and executing services, which shield decision-makers from troublesome maintenance. ‘Open’ is another key idea of WODSS. Viewed from system theory, a system should be open for getting new information from the environment in order to evolve into new states. ‘Open’ in WODSS has the three meanings as follows: (1) Electronic markets are open to decision-makers and providers. They can apply for legal users of electronic markets easily. This makes it more convenient for users to share their decision resources. (2) Decision resources are described in open standard format such as DC (Dublin Cores) and XML [20], which facilitates easier search for users or software agents. (3) Environment of electronic markets is open in order to allow more electronic markets to join it. Some terms such as decision resources, electronic market have been used in previous sections, but now we need to precisely define them in order to elucidate Web-based open DSS expediently. Definition 1 (Decision resources) Decision resources are all information resources that can support decision-making. They help users to solve some problems or make decision. For simplicity, we identify four types of decision resources in this paper: database, models, knowledge and documents. Definition 2 (Consumer or decision-maker) A consumer or decision-maker is a user who needs and wants to use certain decision resources to help him to solve problems or make decision. Decision-maker can benefit from applying decision resources to support decision-making. Definition 3 (Provider) A provider is a user who offers decision resources and publishes them to attract decisionmakers for using the decision resources. Provider can benefit from offering decision resources to decision-makers. Definition 4 (Electronic market) An electronic market is a market infrastructure on the Internet where providers register and publish their decision resources and decisionmakers search and acquire decision resources supporting decision-making. Electronic market acts as the intermediary to match decision-makers and providers and offers a good platform for them to share and transact decision resources.

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Definition 5 (WODSS)WODSS is a seven-tuple of the form: WODSS Z hP; S; x; E; d; D; li

(1)

where P is a set of providers, PZ fPi jiZ 1; 2; .; ng. S is a set of decision resources. Four types of decision resources: database (DBS), models (MS), knowledge (KS) and documents (DS) are usually concerned by researchers. This causes the appearance of multiplebases system framework for DSS. E is a set of electronic market, EZ fEi jiZ 1; 2; .; kg. In general, electronic markets are organized to form marketplace in order to extend and enhance the functions of one electronic market. Two forms can be applied to organize electronic markets, peer-to-peer and hierarchical, which are described in detail in Section 4.3. D is a set of decision-makers, DZ fDi jiZ 1; 2; .; mg. Different kinds of decision-makers require different supporting mode and user interface for DSS. This is the user operating mode research framework for DSS explained in the next section. x: P!S/2E is a registering function that shows providers P registering their decision resources S to the electronic market environment E. Where P!S is the Cartesian product of P and S, 2E is the power set of E. d: E!S/2D is a utilizing function that manifests decision-makers D querying, searching, integrating and utilizing decision resources S from electronic markets environment E to solve decision problems. l: D/P is a feedback function. It represents that decision-makers feed back their suggestions to providers in order that they improve the quality of their decision resources. The feedback function makes WODSS a closed-loop system, which adapts it to the decisionmakers’ various needs.

3.3. Advantages of WODSS Compared with the traditional DSS infrastructure, the formal definition of WODSS creates a uniform research framework for decision support systems. The traditional research work about DSS developed in two ways: resources organizing mode and user operating mode. The former emphasizes the elements and structure of decision support systems, focusing on ‘system’. But the latter pays more attention to the user interface of decision support systems, focusing on ‘support’ for decision-makers. The comparison between traditional DSS and WODSS is shown in Table 1. Resources organizing mode includes three kinds of research framework:

3.2. Formal definition of WODSS With the main ideas and some useful terms defined above, we then propose a formal definition of WODSS.

(1) Multi-base systems (MBS): Spargue brought forward two-base system framework for DSS [14] in 1980, which consists of a database and its management

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Table 1 WODSS comparison with traditional DSS Traditional DSS

WODSS

Resources organization mode

MBS

Multiple resources base

KBS

Problem-solving knowledge base Decision resources center, experts group Group management and communication Organization management and communication Distributed management and communication User knowledge base

P

DSC User operating mode

D

GDSS

High-level decision-makers Group users

ODSS

Organization users

DDSS

Distributed users

ADSS

Various users

S

system, a model base and its management system, and user interface system. Based on this framework, many researchers extended the two-base framework to multiple bases, such as knowledge base, method base, document base etc. As WODSS are concerned, the set of decision resources S consists of multiple decision resources, while d should support integration of them. (2) Knowledge-based systems (KBS): Bonczek presented this framework in 1981 [13]. It focuses on the knowledge base of system and draws on reasoning and problem-solving technology in artificial intelligence to decision support systems. Then a new branch of DSS— intelligent DSS(IDSS) came into being. To realize KBS in WODSS, S should include problem-solving knowledge, while d must support reasoning and problem solving. (3) Decision support centers (DSC): It is a decision resource center with a group of experts to aid highlevel decision-makers to make emergent or momentous decisions [15]. It adopts centralized approach that is different from distributed DSS. User operating mode pays more attention to user ‘support’ interface for DSS. It includes four kinds of research framework: (1) Group decision support systems (GDSS): It supports group users to cooperate and make decisions. GDSS provides a communicative and interactive platform for individual members that need to work in a group to reach a decision. (2) Organization decision support systems (ODSS): It supports organization users to coordinate and make decisions. ODSS provides an organization-wide platform to enhance and facilitate the decision process for organization members by network and communication technologies. Especially in ODSS, related decisions are

E

x

d

l

Multiple resources base integration Reasoning and problem-solving Centralized problem-solving, experts cooperation Group cooperation and decision-making Organization coordination and decision-making Distributed problem-solving User interest and pattern learning

Message feedback

made among organization decision-makers according to their roles and responsibilities in the organization. (3) Distributed decision support systems (DDSS): It supports distributed decision-makers to cooperate and make distributed problem solving. In DDSS, decisionmakers are distributed physically in different hosts. They make decentralized decision to some extent and communicate through network. (4) Adaptive decision support systems (ADSS): Decision resources S includes a knowledge base about decisionmakers’ interests, while d supports decision-makers’ interests learning and l feeds back decision-makers’ suggestions to providers who offer customized decision resources thereafter. From the above analysis, we can find that WODSS is a general framework for DSS. Different kinds of traditional DSS research branches can be realized by instantiating WODSS in a certain way, as shown in Table 1. Additionally, WODSS explicitly demonstrates the entities and their relationship and establishes a useful foundation for further work.

4. A proposed research framework of WODSS 4.1. Computing mode evolution of DSS As the design objective of WODSS is to enable decision resources shareable and accessible on the Web [9], it is very important for WODSS to be with an open and flexible framework. The computing mode plays a great role in the framework [22]. In general, a typical DSS application can be divided into three logic layers: user logic, business logic and data logic, as shown in Fig. 1. As the three sub-system framework for DSS is concerned, the dialog sub-system manages user logic and man–machine conversation. The model sub-system deals with business logic and fulfills


Fig. 1. The logic layer for a DSS application.

Fig. 2. Computing mode evolution of DSS.

business tasks such as computing, reasoning, problem solving, etc. The data sub-system deals with data logic. It stores related dada in database for the other two sub-systems and provides data access interface for them. The three logic layers are often in different distribution ways, which forms the computing mode of a DSS application. In general, the evolution of computing mode in DSS comes through four stages: single, client/server, browser/ server and browser/broker/server, as shown in Fig. 2. They are described in detail respectively as follows: (1) Single: It is an isolated mode that is difficult to extend and integrate new resources, and the three layers are integrated closely and centralized in a single host. (2) Client/server: It is a closed mode including client and server. It runs in two ways: thin-client/fat-server and fat-client/thin-server. Unlike single mode, the three layers are distributed in client and server side in client/server mode in order to enhance the flexibility and extensibility of DSS application. When user logic resides in client, business logic and data logic reside in server side, it is termed thin-client/fat-server mode. When user logic and business logic reside in client, but

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only data logic resides in server side, it is termed fatclient/thin-server mode. Client connects with server via specified network protocol. So those clients that are incompatible with the network protocol cannot access the server. (3) Browser/server: It is a less open mode based on open network platform via the Web. It is a special thinclient/fat-server mode with standard client—browser, which facilitates decision-makers to apply decision resources expediently without any other specific client software. But in this mode, the browser should ‘know’ explicitly where the server is, namely, the IP address of the server. (4) Browser/broker/server: It is an open and advanced browser/server mode with a broker to match browsers and servers. In this mode, the browser need not ‘know’ explicitly where the server is because the broker does it automatically. When a new server is created, every browser will ‘know’ it from the broker as long as it registers to the broker. So it is easier to integrate new decision resources and extend to a more powerful system in this mode. It is the right computing mode that is fit for WODSS. 4.2. A research framework for WODSS According to the formal definition introduced above, we design a framework based on browser/broker/server computing mode. It is shown in Fig. 3. The system consists of four components: decision-makers, electronic markets, decision resources and providers which respectively correspond to the entities in the formal definition, and that the relationships between these components manifest the functions in the formal definition (all notations in Fig. 3 have the same meaning as that in the formal definition). The key component of the system is the electronic market. It serves as the broker between decision-makers and providers and assists them to trade decision resources. Decisionmakers only use ‘thin’ client browsers to access decision resources, while providers put their decision resources on the server side and register them in electronic markets so that decision-makers search and access them easily.

Fig. 3. A framework for WODSS.

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Fig. 4. Organizing mode of electronic markets.

We identify some basic functions of the electronic market and describe them in detail as follows: (1) Electronic markets allow providers to register, update and withdraw their decision resources. (2) Electronic markets allow decision-makers to search, list and select their appropriate decision resources. Additionally, in order to offer transparent decision support services to decision-makers, it is necessary for electronic markets to provide planning and executing services [10]. The planning services aim to employ decision resources to make problem-solving plans for decision-makers, and the executing services helps those who have no necessary computing platform to execute the plan on the remote platform. (3) Electronic markets allow decision-makers to feed back their suggestions to providers by e-mail, message board, etc. 4.3. Organizing mode of electronic markets in WODSS We have noted that the key component of WODSS is electronic market (EM), but one market has some limitations in scalability and serving for multiple users. So extensibility is an important goal for the design of electronic markets [10]. Two forms can be applied to organize electronic markets, peer-to-peer and hierarchical, as shown in Fig. 4. The electronic markets with a certain organizing mode form a marketplace. The two forms of segmented electronic markets overcome the above limitations to a certain degree. In peer-topeer electronic markets, every market plays the same role.

When a new electronic market joins, it will have the same authority as others. In hierarchical electronic markets, on the other hand, the market holds distinguished status according to the hierarchy it belongs to. There is a child– parent relationship between an electronic market and the electronic market authorizing its admittance. Marketplaces become a tree with a center market as the root node and the others as offspring, just like Domain Name servers (DNS) on the Internet. Once a new market joins the marketplace, it will inherit the services of its parent with possible extensions.

5. Transaction process of WODSS The framework describes the structure of WODSS. In this section, we also explore the dynamic characters of WODSS and illustrate how it runs as expected. Dynamic characters can be described as transaction processes. All components in the framework are involved in a process, and transaction series are described in a sequence diagram that is shown in Fig. 5. A whole transaction cycle consists of seven phases as follows: Providers create or update decision resources. Decision resources are registered in electronic markets after standardization. Decision-makers search their favorite decision resources from electronic markets. Electronic markets return searching results to decision-makers. Decision-makers access the appropriate decision resources according to the returned results in . Electronic markets will then make a problem-solving plan. It can be executed remotely and the results are returned to decision-makers. Alternatively, decisionmakers may also download decision resources and execute the plan on their local machines. Decision-makers feed back their suggestions to providers by e-mail or message board after they

Fig. 5. Sequence diagram for a transaction process of WODSS.


employ the decision resources so that providers improve their decision resources according to decision-makers’ needs.

6. Some functions and models of electronic markets in WODSS 6.1. Fundamental functions of electronic markets in WODSS The former definition and framework describe the structure of WODSS. In the following subsections, we will analyze the function models of WODSS based on its structure. As the electronic markets play an important role in WODSS, most functions of the WODSS are related to them. We identify the basic functions of electronic markets in WODSS that are shown in Table 2. 6.2. Some models of electronic markets in WODSS In order to explore the decision resources trade process between decision-makers and providers, and to analyze the markets mechanisms in WODSS, it is necessary to create a model that enables efficient functioning of the electronic markets. We draw on market theory in economics [17,21] and create three models: admitting model, trading model and competing model. The admitting model describes the required qualification of the entities to participate in the electronic markets environment. The trading model illustrates how providers and decision-makers trade decision resources via electronic markets. The competing model reveals the profit mechanisms that drive the electronic markets to function efficiently. The following subsections discuss the three models in detail respectively. 6.2.1. Admitting model The admitting model aims to check and ratify the entities applying to participate in the electronic markets environment. The entities include providers, decision-makers, decision resources and electronic markets, just as mentioned

in the formal definition of WODSS. We have earlier noted that WODSS are open systems for different entities, but they do not mean that any entity is allowed to join the electronic market environment without any restriction. A series of rules are needed to direct the actions of an entity. So we provide the admitting model to meet the requirements. It can be studied from the following three aspects: (1) Users admittance: Providers and decision-makers must be identified by electronic markets and must promise to comply with the market rules before they are legal users of electronic markets and acquire services from them. Those who are bankrupt in reputation will be eliminated from electronic markets. (2) Decision resources admittance: Decision resources must be described in an open standard format such as RDF/XML that is commonly accepted on the Web [18, 19]. A standard description about decision resources facilitates easier searching and management. It is necessary to note that Resources Description Framework (RDF) is very useful for the representation of metadata in the form of XML documents. RDF is a common infrastructure to encode, exchange and reuse structured descriptions of decision resources on the Web, which results in semantic integration of decision resources even though they are heterogeneous at the physical level. (3) Electronic markets admittance: As introduced in Section 4.3, WODSS can be viewed as the federated electronic market systems organized in term of peer-topeer or hierarchical mode. When a new market applies to join the system, it will be granted a certain authority according to its grade and the organizing mode of electronic markets. 6.2.2. Trading model If we regard the admitting model as creating rules of electronic markets, then trading model depicts the trading process between providers and decision-makers. It consists of two sub-models: trade driving model and trade matching

Table 2 Basic functions of electronic markets in WODSS WODSS

Functions

Meanings

d

Search List Select Plan Execute Export Withdraw Update Feedback Register Deregister Authorize

Search the expected decision resources according to the related keywords Look over the list of registered decision resources Select the appropriate decision resources from list or the searching results Make a problem-solving plan according to the certain decision resources Provide a platform for decision-makers to execute the plan Providers register and publish decision resource on electronic markets Providers retract decision resources from electronic markets Providers update decision resource according to decision-makers’ suggestions Decision-makers feed back their suggestions to providers by e-mail, etc. D and P register to electronic markets to be legal users D and P deregister from electronic markets D and P are authorized by electronic markets to access the appropriate resources

x

l P and D

315

316


model. Any trading model is derived from the two submodels.

The decision-maker compares the received bids and chooses the best. A typical bidding process includes:

(1) Trade driving model: It specifies the promoter who initiates the trade. If decision-makers initiate the trade, then it is called a pull model. If providers initiate the trade, it is called a push model. Now most of applications on the Web follow the pull model. Decision-makers must spend much time searching appropriate resources to meet their demands. Correspondingly, the push model is more convenient for decision-makers because providers actively push the right resources to decision-makers according to their interests. (2) Trade matching model: The matching function is one of the most important functions of electronic markets [21, 23]. It describes the bilateral relationship between providers and decision-makers. When a trade starts, providers and decision-makers compete and pursue their private utility. There are four possible ways that electronic markets can match providers and decisionmakers: (1) One-to-one: One decision-maker to one provider. It serves for a decision-maker to make simple decision. The decision-maker only needs decision resource from one provider to support decision-making. (2) One-to-many: One decision-maker to many providers. It serves for a decision-maker to make complex decision. The decision-maker needs different decision resources from more than one provider to support decision-making. (3) Many-to-one: Many decision-makers to one provider. It serves for many decision-makers with a decision resource center that is regarded as the provider. Decision support center (DSC) runs in this way. (4) Many-to-many: Many decision-makers to many providers. It serves for GDSS, ODSS, and DDSS, etc. (3) Some popular trading models: A certain trading model comes into being when the driving model and matching model are decided. Liang gave six popular trading models in the electronic trading [16]. These trading models are barter, bargaining, bidding, auction, clearing and contract. We analyze these models and obtain the comparison results shown in Table 3.

(1) The decision-maker broadcasts his demands and calls for bidding after specifying the detailed criteria. (2) Many bidders submit their bids according to the criteria. (3) The decision-maker chooses the best bid that meets the requirements. (4) The decision-maker pays for the bid and gets the corresponding decision resources to support his decision-making.

With bidding as an example, it is a pull trade model that involves a decision-maker and many potential providers. Table 3 Comparisons of six popular trading models in WODSS Driving model

Matching model 1 to 1

1 to N

Pull model

Barter, bargaining Barter, bargaining

Bidding

Push model

N to 1

Auction

M to N Clearing, contract Clearing, contract

6.2.3. Competing model As the key component of WODSS, electronic markets play an important role in facilitating the exchange of decision resources, information and payments. The behavior of decision-makers, providers and electronic markets is motivated by their desire to maximize their private utility. But in order to make electronic markets run normally, it is necessary to rationalize profit and loss of everyone. Competing model can archive this goal and motivate WODSS to function efficiently via three objectives. (1) Decision-makers utility maximization. It is important to meet decision-makers’ demands and find the most appropriate decision resources to maximize his utility. It will attract more and more decision-makers to register to electronic markets and stimulate the market demands. The mathematical model is UDj Z

k X

½Ui ðSi Þ K Pi ðSi Þ O 0

(2)

iZ1

where UDj is the utility of decision-makers Dj. iZ1,2,.,k is the number of decision resources that Dj needs to support his decision-making. Si is an item of decision resources. Ui(Si) is the utility of Si for Dj, Pi(Si) is the price that Dj must pay for employing Si. It is required that UDj be greater than zero, which guarantees the basic satisfaction of decision-maker with his utility. Obviously, the greater the UDj is, the better it is for the decision-maker. In real-life situation, it is a decision-makers’ utility maximizing problem. The decisionmaker should make choice whether to buy a decision resource or not according to its price and expected value. When the expected value is higher than the price, he will buy the decision resource, or else, he will not. This is the decision rule of decision-maker in real situation. This model stimulates the demands for decision resources. (2) Providers satisfaction. In order to stimulate providers to create and publish their decision resources on electronic markets, it is required that providers benefit from the decision resources offered by them. The model can be described as U Pj Z

k X ½Pi ðSi Þ K Ci ðSi Þ O 0 iZ1

(3)


where UPj is the utility of provider Pj, iZ1,2,.,k is the number of decision resources that Pj provides, Si is an item of decision resources, Pi(Si) is the selling price for Si, Ci(Si) is the cost for creating Si. In this model, UPj must be greater than zero, which guarantees provider satisfaction with his utility. This model stimulates the supply of decision resources. In real-life situation, it is a decision resources pricing problem. Provider should decide the price of his decision resource according to its creating cost and market demands. Creating cost is the reserve price, the decided price should not be lower than this price and it also should not be too high. The price is also affected by the decision-makers’ demands and their expected utility of the decision resource. If the price is too high to exceed the decision-maker’s expected utility, he will not buy the decision resource, and then both the provider and the decision-maker will obtain zero profit. This is an unfavorable outcome for both of them. But we should indicate that for the same decision resource, different decision-maker may have different expected utility, in this situation, discriminatory pricing strategy is a good choice and available method to decide the price of decision resource [24]. Actually, most providers register their decision resources to electronic markets and offer free use for a specified period. This measure facilitates decision-makers to know clearly about the decision resource without paying any cost, which makes for attracting more and more decision-makers. On the other hand, it facilitates providers to acquire decision-makers’ demand preferences and actualize discriminatory pricing strategy by collecting their accessing record and suggestions. The feedback function in WODSS plays a great role in realizing these functions. (3) Electronic markets satisfaction. Without electronic markets, the previous two objectives cannot be achieved. On the other hand, electronic markets will not function efficiently without the participation of providers and decision-makers. In order to promote the creation of electronic markets, it is also necessary to guarantee the profit of these intermediaries. The model can be described as UEk Z

m X iZ1

CDi C

n X jZ1

CPj C

q X

UðSl Þ K Ck ðEk ÞO 0

(4)

lZ1

where UEk is the utility of electronic market Ek, CDi and CPj are membership fees for decision-makers and providers, respectively, U(Sl) is the services charge for trading decision resource Sl, Ck(Ek) is the cost for creating electronic market Ek. In real-life situation, it is a membership-registering problem. It is obvious that the more registered providers and decision-makers there are in Ek, the more profit it will gain. Additionally, the increase of trading volume will also contribute to more profit for electronic market Ek. UEk is greater than zero, which guarantees electronic markets satisfaction with their profit. This model stimulates the creation of electronic markets and attracts them to join

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the electronic markets environment. We also show from the model that electronic markets should improve their service quality to attract more and more users and take measures to motivate them to trade more. This will provide much benefit to electronic markets as well as to providers and decisionmakers. In the above three objectives, the first one is the most important because it is the engine for electronic markets in WODSS, many DSS applications are driven by decisionmakers’ demands. In general, a pull model is fit for this kind of electronic markets and the appropriate trading model is bidding. As the second model is concerned, a push model such as auction is the best for providers’ satisfaction. The electronic market helps to achieve the first two goals and benefits from the trading services that it offers for decisionmakers and providers, which guarantees its private profit as described in the third model.

7. An illustrative example To clarify the ideas discussed in the previous sections, we give an illustrative example that the WODSS framework is exploited to support national education planning in China. The design objective of the project is to provide the Ministry of Education (MOE) with a good platform to make longterm education development plan in China. In this project, the hierarchical markets organizing mode is adopted to suit the natural structure of education institutions in China as shown in Fig. 6. For the convenience of depiction, we choose a scenario with one municipality decision-maker and two county decision-makers to illustrate how they cooperate to make an education development plan for the municipality. In the scenario, the municipality decision-maker Dm and the two county decision-makers Dc1, Dc2 form a cooperative decision group. Dm makes his decision on the basis of data or decision plans from Dc1 and Dc2. He has two choices to achieve his goal, makes the plan directly from the basic data or exploits a uniting model to sum up the finished plans from Dc1, Dc2 and forms the municipality plans indirectly. Firstly, we should identify the components of WODSS in this scenario according to the formal definition in formula (1): † DZ fDm ; Dc1 ; Dc2 g, where Dm holds higher status than Dc1 and Dc2.

Fig. 6. The hierarchical structure of education institution in China.

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† EZ fEm ; Ec1 ; Ec2 g,where Em is the municipality electronic market and Ec1, Ec2 are the county electronic markets, respectively. They are physically distributed and serve the users in different levels. † PZ fPm ; Pc1 ; Pc2 g, where Pm is the decision resources providers of the municipality electronic market and Pc1, Pc2 are providers of the county electronic markets, respectively. † S includes data (basic data for education plan and some midterm data for finished plan stored in database), models (uniting model for summing up the finished plans and forecasting models for students, teachers, schools, education outlay, etc.) and help documents. The finished plans derive from basic data and forecasting models. Then according to the transaction process introduced in Section 5, providers register decision resources to the corresponding electronic markets, and the decision resources distribution status in electronic markets can be described as follows: In Em : Uniting model M u, forecasting models Mf Z fMf _stu ; Mf _tea ; Mf _sch ; Mf _out g, where Mf_stu, Mf_tea, Mf_sch, Mf_out, are forecasting models for students, teachers, schools, education outlay respectively, help documents DSm. They are all provided by Pm. In Ec1: Forecasting models Mf 1 Z fMf _stu ; Mf _tea ; Mf _sch ; Mf _out g that have the same schemes but different parameters with that in Em, basic data DBb1 and midterm data DBm1 of county, help documents DS1. They are all provided by Pc1. In Ec2: Forecasting models Mf 2 Z fMf _stu ; Mf _tea ; Mf _sch ; Mf _out g that have the same schemes but different parameters with that in Em, basic data DBb2 and midterm data DBm2 of county, help documents DS2. They are all provided by Pc2. From the above introduction, we can find that the demands for education plan of Dm drive the trade process and many providers as well as Dc1, Dc2 take part in the trade. Because Dc1, Dc2 also provide finished education plans of the counties, they hold the dual roles of both decisionmakers and providers. Bidding model is the best trading model to fit the scenario. So in the following, we focus on how the bidding model facilitates Dm to make an education plan for the municipality. (1) Announcing: Dm broadcasts his demands on Em and calls for models and data to make the education plan for the municipality. (2) Bidding: Many bidders including Pm, Pc1, Pc2 submit their bids according to the criteria. The uniting model Mu and forecasting models Mf from Pm can achieve the goals of Dm. But on Em, there are neither necessary finished plans for Mu nor necessary basic data for Mf to complete the plan. So the new bids are produced and

transferred to Ec1, Ec2. On Ec1, Ec2, midterm data DBm1, DBm2 that represent finished plans fit the uniting model Mu, basic data DBb1, DBb2 are summed up to be the basic data for Mf. So Dm is faced with two choices to make the education plan for the municipality. One is applying Mu to sum up the two finished counties plans DBm1, DBm2, and the other is exploiting Mf to make the plan based on basic data directly. (3) Awarding: Dm evaluates the two approaches with time and cost to pursue maximal profit as in formula (2) before he makes the choice. Finally, he chooses Mu to sum up the finished plans DBm1, DBm2 and forms the education plan for the municipality because this approach is more time-saving and cost-saving. (4) Paying: Dm pays for Mu and the finished plans DBm1, DBm2 and gets the corresponding decision resources to make the education plan for the municipality. From the above scenario, we can find that WODSS liberates decision-makers from weary maintenance and offers a good platform for decision-makers and providers to trade decision resources for their private profit. We also see that it is very convenient to establish GDSS or DDSS based on WODSS infrastructure.

8. Concluding remarks and future work The technology underlying the Web is fast evolving. It creates a major opportunity to improve the research of DSS technology. Decision-makers are likely to use the Web to support their decision-making. On the other hand, the emergence of electronic markets for decision technology also has a strong impact on the DSS theory and applications. In this paper, we propose a new research framework for DSS using Web technology (WODSS) and bring up many key issues related to it. The main contributions and highlights of this research are: (1) It proposes a formal definition and a conceptual framework of WODSS. The formal definition creates a uniform research framework for various decision support systems. And the conceptual framework based on browser/ broker/server computing mode employs the electronic market to mediate decision-makers and providers, and provides a good platform to facilitate sharing and reusing of decision resources. (2) It develops an admitting model, a trading model and a competing model of electronic market in WODSS based on market theory in economics. These models reveal the key mechanisms that direct WODSS function efficiently and show how decision-makers and providers make deal and benefit via WODSS. The research provides guidelines for designing and developing DSS on the Web. It is, of course, not without limitations. Further efforts are needed to describe decision resources in standard format for easier search and management. How to integrate heterogeneous decision resources


and make a problem-solving plan for decision support? How to trade decision resources employing market competition mechanism between providers and decision-makers in electronic markets? All these issues are very important for WODSS and worthy of deep investigations. We hope that this paper will further increase interest in this important topic.

Acknowledgements The research was supported by the Teaching and Research Award Fund for Outstanding Young Teachers in Higher Education Institutions of Ministry of Education (MOE), China.

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