International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) is an online Journal in English published bimonthly for scientists, Engineers and Research Scholars involved in computer science, Information Technology and its applications to publish high quality and refereed papers. Papers reporting original research and innovative applications from all parts of the world are welcome. Papers for publication in the IJETTCS are selected through rigid peer review to ensure originality, timeliness, relevance and readability. The aim of IJETTCS is to publish peer reviewed research and review articles in rapidly developing field of computer science engineering and technology. This journal is an online journal having full access to the research and review paper. The journal also seeks clearly written survey and review articles from experts in the field, to promote intuitive understanding of the state-of-the-art and application trends. The journal aims to cover the latest outstanding developments in the field of Computer Science and engineering Technology.
International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: firstname.lastname@example.org, email@example.com Volume 1, Issue 2, July – August 2012 ISSN 2278-6856 Graph based framework for designing Modern Decision Support Systems Prof (Dr.) M. P. Thapliyal1, Sandeep Kautish2 1 Associate Professor, H.N.B.Garhwal University Srinagar, Dist.Pauri Garhwal (Uttarakhand) India 246174 2 Research Scholar between model subsystem and model deployment Abstract: As the business world growing at rapid pace, subsystem . Decision Support Systems are the most talked topic since past decade. Data contained on Information Systems are used to In against any user query, search will be made in database derive analytical models which help decision makers in terms and will be retrieved if information available. Otherwise, of simplifying semi-structured problems and suggesting the model database will be searched for seeking models suitable solutions. The most widely used approach to solve which can produce desired information. If model semi-structured problems is to combine the expertise of a decision maker and analytical capabilities of Decision available then there will be another check for its required Support Systems (DSS), empowered by a model database. inputs (Liang 1985). If no desired model is available, This paper aims to frame modern and flexible DSS which there will be attempt to develop composite model. This supports knowledge sharing among users and offers improved implementation will be based on graph theory. cognitive user profile with desired customization. Methodology used to design such system is constructing 2. DECISION SUPPORT SYSTEMS (DSS) customized graph based framework of model section for DSS. Results suggest that the proposed approach would be viable The term decision support system (DSS) can be broadly for use in real world problems. defined as a class of computer based information systems Keywords: Decision Support Systems, Model that support decision-making activities. Turban (1995) Management Systems, Graph Framework, Modeling defines it as "an interactive, flexible, and adaptable computer-based information system, especially developed for supporting the solution of a non-structured 1. INTRODUCTION management problem for improved decision making. It utilizes data, provides an easy-to-use interface, and allows Researchers are always been trying to find new for the decision maker's own insights". DSS are usually techniques to help decision makers for decision making context and task-specific and this is the reason why it is problems of real world scenario. The Decision Support impossible to give a precise definition including all the Systems (DSS) is the domain area in which such semi- facets of the DSS. The only thing that can be said for sure structured problems are dealt and since evolution of DSS, about a DSS is that it includes a decision-making process, it has been emerging topic in computer science study. which has a more widely accepted definition / This section describes the concept of Decision Support Decision-making is the cognitive process with Systems and intelligent computing techniques i.e. Graph psychological construct in which users select one course based approach for model selection and determination. of action from among multiple alternatives. Only the The architecture consists of four main components: a result of the process can be observed in the form of a database, a model management subsystem, dialogue commitment to take action. Decision-making is an system and knowledge management subsystem. Since the important part of many professions, where specialists approach used in the study is based on reasoning and apply their expertise in a given area to making informed judgment, a different Model base management system is decisions. proposed in the study. The new architecture of model Decision support systems operate in a multidisciplinary management system is presented which consists of 3 environment, including among others database research, modules: a modeling subsystem, a modeling deployment artificial intelligence, human-computer interaction, subsystem and an inference engine. The proposed simulation methods, and software engineering. framework includes two major subsystems: modeling and model deployment system out of which modeling system Figure 1 shows an overview of a typical DSS, comprising concentrates on improving effectiveness of models and five key elements: (i) access to external databases, (ii) a model deployment subsystem focuses on proper Database Manager, (iii) a Model Database Manager, (iv) utilization of models. Another important component of a Dialogue Manager and (v) the Decision Maker. framework is inference engine which makes connectivity Volume 1, Issue 2 July-August 2012 Page 45 International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: firstname.lastname@example.org, email@example.com Volume 1, Issue 2, July – August 2012 ISSN 2278-6856 Decision making is the process of getting final state (output information) from initial state (input information). The models in model base have two core elements: nodes and edges. The modeling process based on graph is a process which creates a directed graph and selects a path which is best or optimum suitable in order to for getting desired output. The directed graph or model graph consists of all possible alternatives for solving the problem by mean of one path for each alternative which is called a model. Now, various definitions are described for better understanding of the terminology used in framework. A) Node – A node represents a state or data attributes. Figure 1 Overview of a Decision Support System; adapted from (Laudon & Laudon, 2004) Figure 2 depicts a node named A, which represents a set of data related to supply. The access to External Databases is used to obtain data from outside of systems i.e. other information systems. B) Edge – An edge represents a set of functionality that Raw data can be processed to be converted in transforms a set of input data to their output. In figure information, which in turn can be stored in the Internal 3, E1 represents an edge represents the function of Database for later use. price on demand. The Database Manager takes responsibility to acquire There must be connectivity via an edge between two external data and mediating the access to Internal nodes. In this case only, we can call it a basic model. In Database. The Model Database Manager stores meta-data short, an edge makes a model by connecting two nodes in about analytical models contained in Model Database and particular. Figure 3, as a whole is a basic model as it has is used to manipulate these models during decision 2 nodes (A & B) with one edge (E1). We can represent making processes . this basic model as a triple (A, E1, B). The Dialogue Manager provides the user interface layer C) Node – An AND node is the ending node of more that combines the expertise of Decision Makers and than one basic model. An AND node is true only if all system analytical capabilities comprised both in Model edges ending on that node are true. Figure 3 represents and Internal Databases, providing the greatly needed a AND Node as node A & B are resulting into node C. interactivity to DSS. 3. GRAPH THEORY BASED FRAMEWORK FOR DSS DESIGNING Research on DSS has increased since past decade but very few research attempts are made on inclusion of knowledge sharing concepts. Structured modeling (Geoffrion 1985) was the theory which focused on functional relationship among the models during modeling process . Structure modeling considers each Figure 3: AND Node model of model base as single entity. Geoffrion’s work is D) OR Node - An OR node is the ending node of more the base of graph based approach presented here. The than one basic model. An OR node is true only if at only difference between framework presented here and least one edge ending at the node is true. In this structuring modeling is the latter focuses on functional research work, OR node is depicted as ellipse. relationships among different models and consider high level of cohesion among these models. The proposed E) Path – A path is a finite sequence of edges of the framework deals not only with representation of model, form that – method of integrating and selecting models. a. These edges are connected. b. Only one edge is true out of all that enters at each 3.1 GRAPH BASED REPRESENTATION OF OR node. FRAMEWORK c. All edges are true that enters at each AND node. Volume 1, Issue 2 July-August 2012 Page 46 International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: firstname.lastname@example.org, email@example.com Volume 1, Issue 2, July – August 2012 ISSN 2278-6856 depth-first search, breadth-first search and best-first search . For the purpose of formulation of our model graph, the depth-first and best-first search are more suitable as they both support modeling concepts. The basic techniques of modeling are optimization and satisficing. The depth-first search strategy picks an alternative at one node arbitrary and work forward. As long as any hope of reaching the destination using first alternative, the other alternatives remains ignored. In case of the first alternative gets proved wrong in desired solution, the process starts again with another alternative. A typical process of handling user query using depth- Figure 4: OR Node search technique is as follows – Step 1: Search for an OUTPUT relation in the model base F) Composite Model – A composite model is an to see whether there is model that produce desired output. integrated set of set of basic models. Step 2: If no such model is found, then stop searching and report about same. The system may ask user to develop new G)Model graph - A model graph is a graph that model. Step 3: If a model is available, search INPUT relation to represents all possible models i.e. basic model and get input data for execution of the model. composite models. In a model graph, each path represents Step 4: Repeat the following sequence until all inputs are a model. One of the most important properties of model found or one/more input data is not available – graph is its acyclic nature. 4.1 Take one input and check whether it’s an output of its preceding model. 3.2 BASIC MODEL a. If it is true then drop this model and move to After understanding the graph representation of the step 3. model, now, we can portray our basic model. The model b. If it is not true then skip this procedure. is built upon 5 basic types of information: the output of 4.2 Search the database for availability. the model, inputs required, and computational methods a. If the input is available in the database, then retrieve its used in the model, integrity constraints and validity of value and go to step 4.1 model. b. If the input not available, then go to step 4.3. 4.3 Search the OUTPUT relation of the model to see All models have 5 basic relations and our model also has whether it can be produced by a model. same as follows – a. If no model is available, go to step 4.4 b. If a model is found, go to step 4.5. INPUT (Model#, Inputs) 4.4 Prompt the user for the input. OUTPUT (Model#, Outputs) a. If it is provided by the user, obtain the value and OPERATIONS (Model#, Functions) go to step 4.1 INTEGRITY (Model#, Constraints) b. If not so, then drop the model. VALIDITY (Model#, Validation) 4.5 Search the INPUT relation of the model to find input data required for execution. Repeat step 4 until all input Here, Inputs and Outputs may have N number of data have been obtained or one input in proved parameters i.e. input1, input2…..input n and so forth. unavailable. The context free model presented above can be mounted Step 5: If all input data for model execution is available, the check integrity constraints. on the graph based implementation described in previous 5.1 If any of integrity constraint is not satisfied, then drop section. The Inputs and Outputs relations are formulating the model. a model graph. The Operations relation is represented as 5.2 If all constraints are satisfied, then add the model to an edge. The integrity relation of the model specifies the model graph. constraints which must be satisfied before considering the Step 6: Check whether any other model producing the model as applicable. The validity relation is essential for desired information. measuring the fitness of the model for a particular type of 6.1 If there is another model then go to step 3. problem. Validity of a model gives it a subjective 6.2 Otherwise, stop the process. confidence about the proposed successful implementation of the model . 3.4 MODEL SELECTION TECHNIQUES In the previous section, we have devised formulation of 3.3 MODEL FORMULATION model graph. In this section, we will discuss the strategies Formulation of model graph requires an extensive search for providing necessary advice. There are two different in database and the model base as well. Many approaches strategies for this: optimizing and satisficing . are developed for creating and traversing a search tree i.e. Volume 1, Issue 2 July-August 2012 Page 47 International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: firstname.lastname@example.org, email@example.com Volume 1, Issue 2, July – August 2012 ISSN 2278-6856 The optimizing strategy requires a complete model graph problem. We need one quantitative measure of validity in and then evaluation will take place to find the best order to select best model in model graph . Therefore, alternative. Given the validities of all models are we need a model evaluation function which will available; the optimizing strategy will maximize the determine the validity of model on the basis of some validity of the selected alternative. Although the predefined criteria. There are three points need to be optimization strategy ensures that, given the criteria, the taken into consideration while developing model formulated model is the best available alternative, evaluation function. First is, the criteria for determining sometimes it becomes unreal to for a system to develop validity values. We can have four possible criteria as 1) complete model graph. For example, if a model has b degree of accuracy of model, 2) user’s preference, 3) costs branches with d level of depth, depth-first search incurred, and 4) numbers of models integrated in (Liang technique will raise O (bd) time complexity to find the & Jones, 1988). optimized solution (Korf 1987). Hence, in such kind of The following is the roadmap in order to reduce situations, we need to adapt satisficing strategy which complexity of the model evaluation function. requires evaluation of each path as soon as it is found and Step 1: Determine the validity of each model. VALIDITY accept it if it satisfactory. Hence, formulation of complete function will validate each model. model graph may not be mandatory . The satisficing Step 2: Remove dominated alternatives and simplify the strategy is not much different from others except it problem i.e. if more than one model produces desired evaluates each path at same time of its formulation. The output then select the one with highest validity. process of formulating model graph is terminated Step 3: Calculate validities of all alternative paths from immediately after finding a satisfactory path. The selected initial state to the final state. path will be developed further to produce the desired Step 4: Select the model with highest validity. In case of a output. Figure 5 depicts the process of modeling to complete model graph has been formulated and validities implement the satisficing strategy. of more than one model are equal then we can select model on the basis of some other non technical constraints i.e. modeling time or machine time. If the satisficing strategy has been implemented and complete model graph is not available then selection can be made on the basis of predetermined cutoff value. 4. ANALYSIS We have devised one framework for creating and utilizing Model Base management system. The process starts with presenting notions used in the formulation i.e. node, graph. After this, the basic model is being defined with five kinds of basic operations. Model formulation is the next task in the process in which an appropriate model is being searched which suits requirements of the particular problem. Depth first search algorithm is used to formulate the model graph which handles user queries. The last section presents the model selection techniques for which satisficing technique is used. One model evaluation function is being incorporated to validate the usefulness of the model in case of multiple models suits to one given problem. 5. CONCLUSION This paper has presented an approach to provide DSS with flexibility to problem characteristics and adaption to user cognitive profile. This approach was comprised by a Figure 5: Modeling with satisficing Strategy method that employs user cognitive profile information for decision models creation One of the major issues involved in implementation of The current version of the modern DSS employed only satisficing strategy is the development of model Decision Trees to solve classification databases. It is evaluation functions. This need arises when more than important to highlight that the proposed approach is one model is suitable for some specific problem. The abstract and thus independent of technique and class of Model Management System needs validity values of all problem. Results shown could be further improved by fine models to determine which model is best for given tuning algorithmic parameters Also, other classification Volume 1, Issue 2 July-August 2012 Page 48 International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Web Site: www.ijettcs.org Email: firstname.lastname@example.org, email@example.com Volume 1, Issue 2, July – August 2012 ISSN 2278-6856 techniques could be employed to further improve system of International Joint Conference on Neural accuracy and capability to deal with different problems. Networks-IJCNN, 2008, Hong Kong, China. For example, even though not eloquent regarding  Quinlan, R. (1993). C4.5: Programs for Machine explanations about classifications performed by Artificial Learning, Morgan Kaufmann Publishers, San Neural Networks (Haykin, 1994) they could be used to Mateo, CA. double check if a classification is correct.  Zitzler, E.; Laumanns, M & Thiele, L. (2008). SPEA2: Improving the strength pareto REFERENCES evolutionary algorithm, Technical Report 103, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology  Aitkenhead, M. (2008). A Co-evolving Decision (ETH), 2001. Tree Classification Method, Expert Systems with Applications, n. 34, 2008, p. 18-25. AUTHORS  Breiman, L.; Friedman, J.; Olshen, R. & Stone, C. (1984). Classification and regression trees, Prof. Dr. M. P Thapliyal is M.Sc in Electronics and Wadsworth, 1984. Ph.D in Physics. He is working as Associate Professor in  Chacrkaborty,I; Hub, J. & Cuid, D. (2008). Computer Science Department of HNB Garhwal Examining the effects of cognitive style in University, Srinagar Garhwal, Uttrakhand. He has more individuals' technology use decision making, than 15 years of teaching experience. Decision Support Systems, Vol. 45, 2008, pp. 228- 241. Decision Support Systems, Advances in 290 Sandeep Kautish has M.Sc in Computer Science, MCA  Chiavenato, I. (2004). Administração nos novos with over 08 years of experience in teaching. He is tempos, 2nd Ed., Elsevier, Rio de Janeiro, 2004. research scholar for Ph.D. in Computer Scinece.  Deb, K.; Pratap, A.; Agarwal, S. & Meyarivan, T. (2000). A fast and elist multi-objective Genetic algorithm: NSGA-II, KanGAL technical report, Indian Institute of Technology, Kanpur, India, 2000.  Djamasbi, S. & Stronga, D. (2008). The effect of positive mood on intention to use computerized decision aids, Information & Management,Vol. 45, 2008, pp. 43-51.  Eberhart, R. & Kennedy, J. (1995). 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Including Multi-objective Abilities in the Hybrid Intelligent Suite for Decision Support, Proceedings Volume 1, Issue 2 July-August 2012 Page 49
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