Decision support systems

stop over championship claysAbstr lay step to the foreNowadays, Decision Support Systems has a signifi dopet fictitious character in almost all aras of life. These frames go further and use sunrise(prenominal) technologies like in human bodyation mine and friendship and education baring (KDD) to re handle and facilitate human end do. First of all we leave more(prenominal) or less definitions intimately(predicate) ending making, lessons and routinees. Afterwards, we discuss about companionship and data discovery and excessively, adroit decisiveness domiciliate establishments. At last, as an empirical survey, we examine two variant cultures in using stopping point making go for systems. One of them uses close support system in clinical environs to improve the decisiveness making and reduce critical errors signifi gagetly mend the another(prenominal) uses the traditional system and relies on the human memory and know alternatively than using s topping point support systems.Keywords Decision Making, Decision Support, familiarity and info Discovery (KDD), Intelligent Decision Support SystemsIntroduction nurture system has a signifi undersurfacet role in supporting finish making, and in some cross environments like business, health and education, gets the mandatory part. Moreover, much(prenominal)(prenominal) systems go further and use data excavation and fellowship and data discovery (KDD) techniques to improve their abilities in supporting last making. One of the environments that posit information systems support for making crucial ratiocinations and require direct proceeds on human life is clinical and health environment. We atomic number 18 pass to look by dint of the lay out of finality support system in it.Decision MakingDecisions and presentsDecision making is undeniably an inbred and resilient part of the human life. A conclusiveness line may contain of numerous smaller conclusions inter- related unneurotic, and the get outs of quatern-fold closings can be consolidated together or one decisiveness can influence a nonher resultant one. This influence can be fed as the input to a subsequent closing, or as a closingal choice for the users in find which finding to make subsequently. This bigger stopping point, and its smaller finalitys embedded inwardly, moldiness be represented in a simple manner for finality noblemans to read, infrastand, and authorize with.Each closing can be represented in the form of a sticker, to represent, describe and depict the determination riddle and its fundamental moveion under consideration, whether it is simply an abstraction schema, insights to the finiss rather than mere numbers actual model instance, or executable electronic calculating machine program module. Each stopping point model can be a permanent exemplar scenario which can be retrieved and included as part of a bigger scenario. Alternatively, it ca n be a temporary modeling scenario that is aggregated or pipelined indoors a bigger scenario. much(prenominal) model integrating treatments are subject to the readiness of users at the time of making much(prenominal) decisions. Even though apiece of these decisions may construct a direct or indirect behavior on other subsequent decisions and can easily influence the general decision and conclusion, some decision making processes and systems treat these decisions as item-by-item and unrelated. This obscures the users from seeing and discovering the true effectuate and influence of the decision trouble and its interaction under consideration, whether they are interrelate and/or interdependent. The element of interdependence may non be ascertained until the full picture can be seen and assessed.Even though many decisions do occur in a sequential fashion, thither are overly many decisions that occur in parallel, evolve over time and converge to a concluding decision, or eventually combine or are interwoven into a lowest decision. Therefore, the decision making process should neither be fixed nor determine beforehand so that the execution establish can be constructd as required. Hence, modeling is an important process in understanding, capturing, representing, and solving these decision models peculiarly in terms of their interrelatedness across multiple models and their instances over a period of time. Furthermore such(prenominal) models should ideally be able to contribute functional, behavioral, organizational, and informational perspectives.Decision systems are intended to assist users in making a decision. There are several character references of users involved in using decision systems and these users progress as they develop more confidence from in experience/na?ve decision producers, to average decision clerics/ analysts, to experienced decision churchmans/modelers. Each type of user has varied affects and should not be restric ted by the constraints of any decision system that dictates the travel and techniques behind analyzing and solving a decision problem.Some users may need more decisional and/or system usage guidance dapple others may prefer to have tokenish guidance. Some may wish the decision system go away consent care of the entire decision making process including prescribing the methodicalness in which individually rate of data is requested as well as the order in which each decision model is carryd turn others may wish to intervene to a greater extent in designing the entire decision making process and the execution order to suit, or to a lesser extent in specifying a incident resolving power method. There are a variety of reasons as to why a human intervention is warranted and needed from the perspective of an experienced user. However, it is evoke to note that the type of guidance may have an adverse effect on decision model selection and ultimately the decision outcome.It is counterintuitive and impractical to expect decision makers to operate a different decision making system for each decision and to comprehend the full effects of the consolidation and integration from these decisions. A decision making process is not necessarily about concentrating on the decision itself, but should emphasize the ship canal in which decisions are make. Therefore, users should be able to choose an optimizing approach and root word as well as a grateful approach and solution, and not be limited to solely one approach and solution that is traditionally incorporated in decision systems.Due to the frequency and complexity of interrelated decisions, some users may recall an existing scenario as input to some other scenario, or recall several existing scenarios for comparative purposes. Decision systems need to be built in a flexible way so that decision models and agents can be easily assembled and/or fused together to create new scenarios and specific scenarios can b e built and well- unploughed to meet the needs of grumpy user groups. With all these issues in mind, the manakin and architecture of an ideal decision system should have independent components that enable components to be easily assembled and unified together to form a decision scenario. They should be flexible enough to helps various types of users and accommodate various types of decision making processes. They should also be sufficiently versatile to handle decision problems regardless of paradigms and/or creations under consideration. Good decision making cloths must therefrom be in place for system framework and architecture to exhibit modeling flexibility, component independence, and versatility in domain and/or paradigm.To stamp down the issues and fulfill the requirements discussed to a higher place, we commencement ceremony propose a converging decision abbreviation process, an optimizing? delicious decision model, and a cyclical modeling lifecycle.Normative d ecision making processesDecisions can evolve and converge into a concluding decision over time. This can occur within re-evaluating a decision problem, or evaluating across multiple decision problems that are standardised. This iterative decision making process is known as the convergence process. As decisions evolve and tweak over time, decision makers are able to concentrate on essential factors and eliminate nonessential ones in order to narrow down the domain of the decision problem. Such direction-foc utilize method provides a cut down interpreting of the problem. A decision is subsequently made from these remaining factors of the reduced problem. Such decision-focused method provides an actionable result from the given problem. Since there can be many decisions within a decision problem, several iterations of attention-focused and decision-focused methods are utilize while intermediate decisions within the decision problem are made and converged. Such revision and right ness occur irrespective of paradigms and domains. This notion of applying the attention focused and decision-focused methods within a convergence decision making process are depicted in estimate 1. look 1. Converging decision analysis, as in an 1D-CSP scenarioOne-Dimensional Cutting Stock Problem (1D-CSP) was used for illustrative purposes in order to design and implement the proposed framework and architecture. 1D-CSP is about acidulous strips of raw material into desire sizes according to customer order widths. We often do not have unlimited supplies of raw materials and would therefore need to formulate and decide on which baseball swing patterns are used. 1D-CSP is a resource management problem with a traditional goal of minimizing wastage. at any rate wastage, there may be other objectives that must be considered. For example, smirch machine setups done the changing of incisive knives, minimize machine setups with reducing the number of cutting patterns used, or minimi ze the number of disruption in the sequence of cutting patterns used. Even though 1D-CSP is considered to be a simple problem in pure mathematical terms, it becomes a slightly complex decision problem once one considers all the actually institution constraints and objectives, and the interrelated decisions involved within its decision making process.The 1D-CSP can be used as a decision problem to flesh out the converging decision analysis process, as depicted in excogitation 1. The first decision is a pattern generation heuristic that generates combinations of cutting patterns. This decision concentrates only on generating those cutting patterns that are relevant to the decision problem under consideration (an attention-focused method). The second decision is deter excavation which cutting patterns among the generated ones should be retained or discarded (a decision-focused method). This can be ground on specific rules such as an allowable number of cutting knives per cutting pattern. It can also be based on the decision makers personal experience on whether certain cutting patterns should be discarded. The trine decision is the creation of linear programming constraints that identifies the feasible area of the problem under consideration (an attention-focused method), while the quaternary decision is finding an optimum point within the feasible area (a decision-focused method).Neither of the focused methods has to train an optimal or a satisfying solution necessarily. It is entirely up to the decision maker to decide on what sort of solution is desired at the time. Each decision and solution can be encompassed within a decision model that consists of two the optimizing model and satisfying model, as depicted in Figure 2. In a decision problem that consists of multiple interrelated decisions, the result from one model may be fed into another model perpetually until an ultimate result is reached, and the result from a model can take on a different s olution option. Each decision model may return to itself for refinement, or return to the previous model for supernumerary processing, or feed to the bordering model for further processing. This return may be due to an infeasible solution, or a better understanding of the model which eventually leads to a change in the parameters of the model.The 1D-CSP can be used to illustrate the optimizing?satisfying decision model, as depicted in Figure 2. The first decision model pattern generation heuristic is a satisfying model that produces only those cutting patterns that are relevant and desirable to the decision problem under consideration. The second decision model is also a satisfying model in selecting or deselecting among the cutting patterns already produced. The third and fourth decision models are optimizing models that optimize using the linear programmings simplex method.Figure 2. Optimizing?satisfying decision modelDecision modeling lifecycleThe approach of Simon to the deci sion making process in terms of discussion, design, and choice is very decision-oriented. However, as Glob has suggested it is about the way in which we model the decision. Therefore, we propose to integrate Simons proposal with MS/ORs modeling proposals that attempt to support any mannikin and aspects of decisions and modeling lifecycle. Such a design approach is crucial to support the modeling and decision environments and ensure that non-predetermined decision making processes and interrelated decisions characteristics can be modeled.This proposed modeling process is cyclical and iterative, and enables continuous ad butment and refinement specially in storing and retrieving decision problems as decision scenarios, as summarized in Figure 3. Despite the fact that the modeling lifecycle progresses step-by-step in a cycle, it can return to any foregoing steps and not just the previous one, and can skip some steps in the subsequently iteration if it has already gone through tha t particular step earlier on. It is however more difficult to represent these possible movements visually in the modelling lifecycle and is therefore not illustrated in Figure 3. The lifecycle is valuable not only from the point of view of modeling the decision itself but especially for highlighting the role of the system components of the decision, whether it is a data, model, solver, or scenario. Once a problem is understood it can be represented in the form of a model which is because instantiated with data and integrated with solvers so that it can be executed. Such a model is especially beneficial if it is storable and retrievable for later use and comparison. Once a model is represented, a solution can be derived through analyzing and investigating as well as comparing with various model instances. The derived solution is then reviewed and validated. If it is considered unsatisfactory such information can be used to modify and reformulate the decision model.Figure 3. Cyclical modeling lifecycleEven though the decision system leave alone progress through the entire modeling lifecycle in producing the end result, it is important to note however that not all users will execute all the steps of the modeling lifecycle. Depending on the competencies of the decision makers and their permissions, they may interact with certain steps in the modeling lifecycle. For example, the inexperienced decision maker may interact with only step 2 the average decision maker may interact with steps 2, 3 and 4 whereas the experienced decision maker may interact with all 6 steps in the modeling lifecycle, as shown and contrasted in Figure 4. This decision modeling lifecycle provides a sound basis for the decision support and modeling framework and architecture.Figure 4. Interaction amid 3 types of user groups and the modeling lifecycleIntelligent Decision Support SystemsWhile IDSS (Intelligent Decision Support Systems) have been receiving increase attention from the DSS resea rch community by incorporating intimacy- based techniques to provide bright and active behavior, the body politic-of-the-art IDSS architecture provides little support for incorporating novel technologies that serve useful DSS information, such as the results from the companionship and data discovery (KDD) community.Data Mining and experience DiscoveryIn recent years, the terms noesis discovery and data mining ( putting greenly referred to as KDD) have been used synonymously. They both refer to the area of research that draws upon data mining methods from pattern credit (Tuzhilin, 1993), machine learning (Han et al., 1992) and database (Agrawal et al., 1993, 1994) techniques in the context of vast organizational databases. Conceptually, KDD refers to a multiple step process that can be highly interactive and iterative in the next (Fayyad Uthurusamy, 1995) the selection, cleaning, transformation and projection of data mining the data to extract patterns and withdraw models e valuating and interpreting the extracted patterns to decide what constitutes ? association? consolidating the cognition, resolving conflicts with previously extracted knowledge making the knowledge usable for use by the interested elements within the system. A number of KDD systems are similar to IADSS data mineworker actors in spirit and in technique. Such work in designing and implementing practical KDD systems is crucial to our research in the sense that their results provide solid KDD pragmatic technologies ready to be integrated into our IADSS architecture. However, the current state of using KDD techniques for decision support remains in its infancy, as advance applications that use exclusively KDD techniques. It is our point of view that such isolated applications have limited scope and capabilities, while future KDD techniques will variation an underlying role in complex business systems that incorporate a great range of technologies including happy agents, multimedia and hypermedia, distributed systems and computer networks such as the internet, and many others. From a DSS perspective, a simple DSS architecture that consists of a single decision maker with single information source knowledge discovery functionality lacks the expertness to deal with complex situations in which multiple decision makers or multiple information sources are involved. Most existing DSSs with data mining and knowledge discovery capability fall into this category.Intelligent AgentsThe concept of intelligent agents is quick becoming an important area of research (Bhargava Branley, 1995 Etzioni Weld, 1994 Khoong, 1995). Informally, intelligent agents can be seen as packet agents with intelligent behavior, that is, they are a combination of software agents and intelligent systems. Formally, the term agent is used to denote a software-based computer system that enjoys the following properties (Wooldridge Jennings, 1995)Autonomy Agents operate without the direct inter vention of humans. Co-operatively Agents co-operate with other agents towards the achievement of certain objectives. Re action mechanism Agents perceive their environment and respond in a timely fashion to changes that occur. Pro-activity Agents do not simply act in response to their environment they are able to exhibit goal-directed behavior by taking the initiative. Mobility Agents are able to travel through computer networks. An agent on one computer may create another agent on another computer for execution. Agents may also transport from computer to computer during execution and may carry accumulate knowledge and data with them. Furthermore, there has been a rapid growth in attention paid to developing and deploying intelligent agent-based systems to tackle authentic world problems by taking advantage of the intelligent, autonomous and active nature of this technology (Wang Wang, 1996).Intelligent Decision Support SystemsIntelligent decision support systems (Chi Turban, 19 95 Holtzman, 1989), incorporating knowledge-based methodology, are designed to aid the decision-making process through a set of recommendations reflecting domain expertise. Clearly, the knowledge-based methodology provides useful features for the application of domain knowledge in decision making. However, the knowledge stored in the knowledge bases is highly domain-oriented and relatively small changes in the problem domain require extensive intervention by the expert. muscular information intercourse steers, such as the internet (information superhighway), are continuously changing the decision making process. When decision makers make decisions they not only rely on brittle domain knowledge but also on other relevant information from all over the world. As a result, the challenge of discovering and incorporating new knowledge with existing ones requires us to get in new techniques (such as intelligent agents and knowledge discovery) into DSSs. Research into IDSS includes the work by Rao et al. (1994), who presented an intelligent decision support system architecture, IDSS, that stresses active involvement of computer systems in decision making, on the other hand, the work by Sycara at CMU LEI (Laboratory for Enterprise Integration) proposed the PERSUADER (Sycara, 1993), which incorporates machine learning for intelligent support of conflict resolution and the work on NEST which incorporates distributed artificial intelligence (DAI) with group decision support systems by Fox and Shaw (Shaw Fox, 1993). The proposed IDSS architecture is similar in substance to our proposed IADSS, which incorporates distributed artificial intelligence and incorporates the principles of co-operative distributed problem solving in the decision-making process. However, as we have pointed out above, it is necessary for the incorporation of data mining technology which extracts important information from vast amounts of organizational data sources in order to provide additional information that may be crucial for the decision-making process.IADSS architectural configurationAs we have pointed out in our presentment, there exist numerous obstacles that remain to be overcome in instantly?s DSSs to fully achieve the vision of IADSS. The integration of intelligent agents with DSSs will be able to breed most, if not all, of the articulated issues. However, even within the application of an intelligent agent-based architecture, there exist two different forms (or configurations) of the decision-making process that the particular architecture will be able support integrity decision maker-multiple miners and multiple decision makers-multiple miners.Single Decision Maker- fivefold Miner DSS ProcessesWe have argued in the previous section that a possible configuration of IADSS architecture, namely the single decision maker-single miner form, has severe limitations when it comes to extendibility and the ability to be integrated into an overall organizational deci sion support framework. However, in many real life cases, the single decision maker situation is still of importance. In today?s organization, there may exist a unnumerable of organizational information sources on which useful data relationships and patterns may be discovered to support the singular decision maker?s decision process. As a result, the IADSS configuration of a single decision maker with multiple data miners warrants attention and analysis. Under IADSS, the architecture of such a single decision maker, multiple knowledge miners assisted DSS is shown in Figure 5.Figure 5. Multi-Agent-based DSSFigure 6. A Multi-Agent-Based GDSSThere are uncouth chord classes of intelligent agents (we call them decision support agents or DS agents) contained within this architecture Knowledge miners that discover hidden data relations in information sources, user protagonists that act as the intelligent interface agents between the decision maker and the IADSS and a knowledge four-in -hand with repository support that provides system co-ordination and facilitates knowledge communication. Further details about the functionality and internal structure about each type of agent is elaborated in the next section.Multiple Decision Maker-Multiple Miner-Assisted GDSS ProcessThe single decision maker configuration discussed above can be easily extended into a group decision support system (GDSS) architecture (as seen in Figure 6. by the introduction of additional user assistants for each additional decision maker).Compared to the single decision maker configuration in Figure 5, each user assistant agent is further augmented to provide support for group-based communication between different decision makers. It is important to observe that with the introduction of each additional DS agent only an extra knowledge communication channel between the new DS agent and the knowledge motorbus is needed. This enables a manageable linear increase in the number of knowledge communic ation links corresponding to the increase in the number of agents in the IADSS system, rather than the quadratic increase in the number of direct communication links in a direct agent-to-agent fashion. Furthermore, our proposed IADSS is an open architecture with potential for the integration of future technologies by the incorporation of additional classes of intelligent agents.IADSS architecture at a glanceIntelligent Decision Support AgentsAs draw above, there are three types of intelligent agents in an IADSS system Knowledge miners, user assistants and knowledge managers. This section will provide a more detailed description of such agents and their internal architectures.Knowledge Miners. The role of knowledge miners in IADSS is to actively discover patterns or models about a particular topic which provides support in the decision-making process. There are four components in a knowledge miner. The IADSS interface component manages the communication between the miner and the kn owledge manager. When a knowledge miner receives messages that are represented in a communal re debut, the IADSS interface translates these messages into the local format based on the common vocabulary. On the other hand, when the knowledge miner sends messages out, the IADSS interface translates them into common format first, then sends them to the knowledge manager. In order to carry out the mining task, the necessary run knowledge as well as domain knowledge is kept in the knowledge base component, while the data interface component serves as a gateway to the out-of-door information sources. The knowledge discovery is usually done by discovering special patterns of the data, i.e. by clustering together data that share certain common properties. For instance, a knowledge miner may find that within this week, a number of stocks are going up. There are two different types of knowledge mining agents, event-driven knowledge miners and tusk-driven knowledge miners. The event-driven knowledge miners are agents that are invisible to the decision makers, and their results may contribute towards the decision-making process. Based on the specification of the IADSS, such event-driven knowledge miners start when the IADSS starts up. When a particular event comes, an agent will start its knowledge mining. Events may be temporal events, e.g. every day at 1 a.m., every hour, etc. Or, events may be constraint-triggered events, e.g. every 10,000 customers, when a certain type of customer reaches lo%, etc. Usually, such event-driven knowledge miners work periodically. They follow a sleep-work-sleep-work cycle and will be destroyed when the entire IADSS system terminates. On the other hand, task-driven knowledge miners are created for particular data mining tasks based on requests originated by the decision makers. After a knowledge miner completes its task, it sends the mining results to the knowledge manager and is then terminated automatically. From the view point of de cision support, knowledge miners play the role of information extractors which discover hidden relationships, dependencies and patterns within the database, whether the information is discovered by an event-driven knowledge miner or a task-driven knowledge miner, which may be utilized as evidence by decision makers within the GDM process.User Assistants. Interaction between a particular decision maker and the IADSS is accomplished through a user assistant agent. The architecture of a user assistant contains four components. The multimedia user interface component manages the interactions with the decision maker such as accepting requests for a task-driven knowledge miner, while the IADSS interface manages the knowledge communication with the knowledge manager. The necessary knowledge such as the common vocabulary, decision history and others are kept in a local knowledge base component. All three components are controlled by an operational component that provides the facility of dif ferencing, multimedia presentation and collaboration. With regard to the role the user assistant plays in the decision process, it enables the decision maker to view the current state of the decision process and to convey his or her own opinions and arguments to the rest of the decision making group. It also enables the decision maker to issue requests for task-driven knowledge miners to attempt to discover some particular type of organizational knowledge from business data. The user assistant will then relay the request to the knowledge manager and interpret the mining result if it is deemed appropriate.Knowledge Manager The knowledge manager provides management and co-ordination control functions over all the agents in the IADSS architecture. The internal component-wide architecture of the knowledge contains four Components The decision maker interface, the operational facilities, the miner interface and the agent knowledge base that provides support for localized reasoning. From the functional standpoint, the knowledge manager provides the following functionality in the IADSS architectureMakes decisions concerning the creation and termination of knowledge miners as provided by the miner interface component of the knowledge manager.Mediates requests from user assistants through the decision maker interface, analyzes these requests through the localized knowledge and inference engine and then initiates an appropriate group of task-driven knowledge miners to collaboratively perform the requested task through the miner interface.Mediates the discovered knowledge from knowledge miners (whether it is an event-driven or a task-driven miner), stores the knowledge into the repository for possible future usage and forwards the relevant knowledge to interested decision maker users through the decision-maker interface.Manages and co-ordinates the knowledge transactions with each individual decision support agent such as common vocabulary, available decision topics, exi sting mining results and strategic knowledge, as provided by the operational facilities component.Manages the synchronization between the collection of decision support agents such as the progress of the task-driven knowledge miners and the notification of the decision makers when crucial knowledge is discovered.Mediates all other types of communication among decision support agents including the communication among user assistants and supports the retrieval of appropriate evidence from the repository by user assistants.In terms of the decision support process, the knowledge manager plays the role of manager and mediator between two decision makers, between the decision maker and the corresponding task-driven miners and between all decision support agents and the repository to address the issue of knowledge sharing.Current prescription process at the infirmaryThe prescription process is shown in Figure 7. This description is based on interviews (questions 1?3 in the interview guidel ine, Appendix A) and observations by the first author.Figure 7. Current prescription process in the Ekbatan and Boras Hospital (UML activity diagram)The process starts as the physician in charge takes the patients history, performs physical examinations, and reviews available medical documents, including progress notes, laboratory findings, and imaging. These data sources guide the physician(s) to a set of differential diagnoses or a definitive diagnosis, which help the prescriber(s) to select appropriate treatment for the patient.The prescriber will then register medical records