A knowledge based decision support system for computer performance management

Computer systems managers make decisions about hardware and software selection, performance evaluation, capacity planning, and other resource variables on the basis of factual data, accounting data, subjective judgements, and assumptions about the resource consumption of the jobs being run. The importance of computer resource planning calls for effective support methods. A Knowledge-Based DSS (KBDSS) will be able to assist managers in making these policy decisions by utilizing knowledge of the existing configuration and its capabilities, the organizational computing environment, available external resources, and their suppliers. Combining procedural and declarative methods, such a KBDSS may provide early warning of possible bottlenecks, forecast growth of hardware usage, and employ knowledge based inferencing to suggest suitable remedial actions to the systems manager. This paper presents a KBDSS for supporting computer resource planning decisions using a procedural/declarative framework, and illustrates the system's usage aspects.     

A structured modeling based methodology to design decision support systems

Many Decision Support Systems (DSS) support the decision making process through the use of mathematical models and data. DSS design involves modeling data as well as mathematical relationships in a domain. The process of model formulation and subsequent integration of model with data in a DSS is a complex and ill-structured process. This paper proposes a methodology based on Structured Modeling (SM), originally introduced by Geoffrion together with the modeling language SML, to model and design the DSS. The methodology includes rigorous and step by step procedures to design and integrate data and modelbases. The main contribution of our approach lies in the integration of research in database design, and mathematical model formulation within the structured modeling framework. The resultant procedures can be easily automated and taught to students in DSS courses. The motivation for our research stemmed from our constant frustrations in teaching DSS courses over the last five years. In the last two years, when we used our methodology, the performance of the students improved significantly. The average score in the DSS project went up to 85 from 60. Our positive experience in using our methodology in classes over the past two years suggests that the methodology imposes structure into the analysis of decision problems, and as a result students produce better DSS designs for classroom cases.     

A decision support system methodology for forecasting of time series based on soft computing

Exponential procedures are widely used as forecasting techniques for inventory control and business planning. A number of modifications to the generalized exponential smoothing (Holt-Winters) approach to forecasting univariate time series is presented, which have been adapted into a tool for decision support systems. This methodology unifies the phases of estimation and model selection into just one optimization framework which permits the identification of robust solutions. This procedure may provide forecasts from different versions of exponential smoothing by fitting the updated formulas of Holt-Winters and selects the best method using a fuzzy multicriteria approach. The elements of the set of local minima of the non-linear programming problems allow us to build the membership functions of the conflicting objectives. It is compared to other forecasting methods on the 111 series from the M-competition.     

Development of decision support knowledge based system for tool wear diagnosis

This paper deals with the problems faced by small and medium sized metal cutting industries, with the perspective of tool monitoring. In a small or medium size metal cutting industry employing major metal cutting process, one of the primary problem is that of tool monitoring and wear diagnosis. The problem is of immediate concern especially in those industries where the processes or operations employed are flexible and production depends entirely on orders from customers. Due to a flexible manufacturing setup, almost all major metal cutting processers need to be carried out. However, it becomes increasingly difficult for such small or medium size metal cutting industries to employ skilled manpower for each operation as well as expert technicians to supervise the operation, and carry out fault diagnosis and tool monitoring. Also, the problem associated with tool monitoring is that human operator carrying out the monitoring has to rely either on observation such as ceasing of tool, rise in temperature, generation of fumes, noisy operation, vibrations, considerable change in shape etc, or by monitoring the quality of the finished product. Also, there can be instances where the operator does notice a symptom but does not have the expertise to identify the cause of the trouble. Errors in tool monitoring can lead to considerable damage both to the machine as well as the workpiece. On the other hand, if the tool is replaced before it reaches its useful life expectancy, it leads to unnecessary additional cost. A Decision Support Knowledge Based System (DSKBS) has therefore been developed in this paper with the above considerations. The DSKBS provides the user with a friendly environment to diagnose a particular tool wear and obtain the necessary repair or replacement instructions. The goal is to increase productivity, decrease cost of operation and enhance total quality and reliability of the operation.     

Geovisualization for knowledge construction and decision support

Geovisualization is both a process for leveraging the data resources to meet scientific and societal needs and a research field that develops visual methods and tools to support a wide array of geospatial data applications. While researchers have made substantial advances in geovisualization over the past decade, many challenges remain. To support real-world knowledge construction and decision making, some of the most important challenges involve distributed geovisualization - that is, enabling geovisualization across software components, devices, people, and places.     

A distance-based group decision-making methodology for multi-person multi-criteria emergency decision support

In this paper, a distance-based group decision-making (GDM) methodology is proposed to solve unconventional multi-person multi-criteria emergency decision-making problems. In this model, some decision-makers are first identified to formulate a group decision-making framework. Then a standard multi-criteria decision-making (MCDM) process is performed on specific decision-making problems and different decision results are obtained from different decision-makers. Finally, these different decision results are aggregated into a group consensus to support the final decision-making. For illustration and verification purposes, a numerical example and a practical unconventional emergency decision case are presented. Experimental results obtained demonstrate that the proposed distance-based multi-criteria GDM methodology can improve decision-making objectivity and emergency management effectiveness.     

A methodology for custom VLSI layout

In this paper, we present a new methodology for custom VLSI layout which aims at a low turnaround time and a high quality of design. VLSI circuits are highly complex, and to speed up the design process we exploit the hierarchical structure of a design, splitting the problem domain into several levels. The process of layout at each level is divided into steps such as placement of rectangular blocks, determining block dimensions, determining interconnection paths, etc. In order to obtain high quailty, designs, we have systematically analyzed the relationship among the parameters being computed at various steps and have accordingly organized the flow of data and control through these steps. There are two novel features in our scheme. First, we do not follow the usual pure top-down or pure bottom-up approach, so as to take into account the influence of design decisions at the higher levels on design decisions made at the lower levels, as well as vice versa. For example, we determine the geometry, of a block taking into consideration the context in which it is placed, as well as the geometries of the lower level blocks it encloses. Second, we perform a look-ahead operation when the values of some parameters are needed before they are actually deterministically computable by the process. For example, at the time of placement, the area required for routing is estimated statistically (before doing the actual routing) so that a more routable placement can be obtained, thereby avoiding some unnecessary iterations.     

Context-aware decision support systems based on typical knowledge integration models

A methodology for designing context-aware decision support systems based on typical knowledge integration models is proposed. These models describe the functional capabilities of the system at different stages of its usage. The models are used to specify requirements for information and knowledge from the side of the context-aware system. The comparison of these requirements with the user requirements for the system functional capabilities and user restrictions allows us to obtain the functional capabilities that are available for a given user.     

New perspectives in decision support for port planning

The Decision-Support-System (DSS) to be presented within this paper is based on research done at the department of Computer Science of the Technical University of Berlin. Among the research conducted at the subdepartment for Applied Computer Science, the project “Determination of the Capacity of Ports” was one point of focus in the years 1980–1985. Findings of this research work as well as practical experiences helped in underpinning the knowledge of the decision processes in seaports; all this contributed to the development of a practice-oriented DSS. All parts of the system, including the data base, were programmed in SIMULA and implemented on the IBM 4381-2 of the Technical University of Berlin. Before the system is applied in an actual problem situation, all parts of the system, particularly the integrated simulation model, will have to be validated. Not until this validation has been done, will the decision makers have sufficient confidence regarding the decision support of this system. This validation is still being worked on. Further research is focused on the application of the DSS for similar decision problems in other traffic and transportation systems.     

A GIS-based multicriteria spatial decision support system for planning urban infrastructures

The planning of urban infrastructures has important spatial implications. The evaluation of alternative courses of action in this setting requires the explicit consideration of multiple criteria as they have important social, economic, and environmental effects. This paper presents a decision support system aimed at offering the users (e.g., government or municipal agencies) a flexible and user-friendly environment to provide decision aid in urban infrastructure planning. The visualization of available alternatives on maps provides a value-added for decision support processes in urban infrastructure evaluation problems. The development of this system has been motivated by a real world urban case study.     

A web-based decision support system for multi-criteria inventory classification using fuzzy AHP methodology

We propose an inventory classification system based on the fuzzy analytic hierarchy process (AHP), a commonly used tool for multi-criteria decision making problems. We integrate fuzzy concepts with real inventory data and design a decision support system assisting a sensible multi-criteria inventory classification. We report on a study conducted in a small electrical appliances company and validate the design of the proposed multi-criteria inventory classification system and its underlying fuzzy AHP model.     

Neuro-fuzzy approach versus rough-set inspired methodology for intelligent decision support

One of the two goals of this paper is to briefly present two different methodologies that can be used to the design of intelligent decision support systems, in particular, from the field of medicine. The first approach, combining artificial neural networks and fuzzy sets, yields a neuro-fuzzy classifier that can be trained with both purely numerical data as well as qualitative, linguistic, fuzzy data that describe the decision-making process. The second approach – resulting in a rough classifier – combines all positive aspects of rule induction systems with the flexibility of statistical techniques for classification. The second goal of this paper is to perform a broad comparative analysis of both proposed methodologies (and two others) applied to: (a) the problem of selecting surgical and non-surgical cases in the veterinary domain of equine colic, (b) the problem of diagnosing benign and malign types of breast cancer, and (c) the problem of corporate bankruptcy prediction (corporate ‘financial health'). Several aspects of comparison have been considered including the accuracy of the systems, diversity of the data processed, transparency and the form of decisions made.     

Human-automated path planning optimization and decision support

Path planning is a problem encountered in multiple domains, including unmanned vehicle control, air traffic control, and future exploration missions to the Moon and Mars. Due to the voluminous and complex nature of the data, path planning in such demanding environments requires the use of automated planners. In order to better understand how to support human operators in the task of path planning with computer aids, an experiment was conducted with a prototype path planner under various conditions to assess the effect on operator performance. Participants were asked to create and optimize paths based on increasingly complex path cost functions, using different map visualizations including a novel visualization based on a numerical potential field algorithm. They also planned paths under degraded automation conditions. Participants exhibited two types of analysis strategies, which were global path regeneration and local sensitivity analysis. No main effect due to visualization was detected, but results indicated that the type of optimizing cost function affected performance, as measured by metabolic costs, sun position, path distance, and task time. Unexpectedly, participants were able to better optimize more complex cost functions as compared to a simple time-based cost function.     

A decision based one-against-one method for multi-class support vector machine

The support vector machine (SVM) has a high generalisation ability to solve binary classification problems, but its extension to multi-class problems is still an ongoing research issue. Among the existing multi-class SVM methods, the one-against-one method is one of the most suitable methods for practical use. This paper presents a new multi-class SVM method that can reduce the number of hyperplanes of the one-against-one method and thus it returns fewer support vectors. The proposed algorithm works as follows. While producing the boundary of a class, no more hyperplanes are constructed if the discriminating hyperplanes of neighbouring classes happen to separate the rest of the classes. We present a large number of experiments that show that the training time of the proposed method is the least among the existing multi-class SVM methods. The experimental results also show that the testing time of the proposed method is less than that of the one-against-one method because of the reduction of hyperplanes and support vectors. The proposed method can resolve unclassifiable regions and alleviate the over-fitting problem in a much better way than the one-against-one method by reducing the number of hyperplanes. We also present a direct acyclic graph SVM (DAGSVM) based testing methodology that improves the testing time of the DAGSVM method.     

A domain-specific decision support system for knowledge discovery using association and text mining

We propose a novel association and text mining system for knowledge discovery (ASTEK) from the warranty and service data in the automotive domain. The complex architecture of modern vehicles makes fault diagnosis and isolation a non-trivial task. The association mining isolates anomaly cases from the millions of service and claims records. ASTEK has shown 86% accuracy in correctly identifying the anomaly cases. The text mining subscribes to the diagnosis and prognosis (D&P) ontology, which provides the necessary domain-specific knowledge. The root causes associated with the anomaly cases are identified by discovering frequent symptoms associated with the part failures along with the repair actions used to fix the part failures. The best-practice knowledge is disseminated to the dealers involved in the anomaly cases. ASTEK has been implemented as a prototype in the service and quality department of GM and its performance has been validated in the real life set up. On an average, the analysis time is reduced from few weeks to few minutes, which in real life industry are significant improvements.     

A methodology to quantify failure for risk-based decision support system in digital business ecosystems

In digital business ecosystem architecture it is rational for the trusting agent to analyse the possible risk according to its demand before interacting with a probable trusted agent. Doing so would assist the trusting agent in its decision process and would also give the trusting agent a hint of the direction in which the interaction might head. The possible risk in an interaction is a combination of the probability of failure and the possible consequences of failure of an interaction. In this paper, we propose a methodology by which the trusting agent determines the probability of failure in interacting with a probable trusted agent. The determined probability of failure by the trusting agent is according to the specific demand of its future interaction with the probable trusted agent.     

Group decision process support system for regional planning—A perspective from Japan

Regional planning has been regarded as a design activity. Usually planners focus on physical design rather than on societal issues. Nowadays, mass communication, environmental issues and social awareness lead to often complex and conflicting needs and interests of the public in regional planning. This paper focuses on the regional planning as a group problem solving process from the view of information processing. It offers an analysis of the causes of conflicts in the group decision process, and defines the characteristics of group decision process support systems     

Co-Insights framework for collaborative decision support and tacit knowledge transfer

Abundant software tools use visual analytics (VA) techniques to support various decisions with the aim of boosting better insights. Large organizations, however, lose efficiency in selecting the right tools to support the persons who apply the tools to various decision tasks. Consequently, the creation and sharing of insights are far from optimal, leading consistently to sub-optimal decisions. In this work, the Co-Insights framework is introduced with automated collaboration support features to enable effective creation and sharing of distributed insights. A collaboration network (Co-Net) is established to model the collaborative decision making process in an organization. Two important features of the Co-Insights framework are developed: collaborative agent allocation analysis (CA3) for task–participant matching; and a robust mechanism for the recommendation of selected VA tools, by participant–tool matching. Thus, by better matching of tasks and tools with participants, the creation and sharing of insights are improved in any collaborative team for better decision making, accompanied with the tacit knowledge transfer to sustain the entire organization. To validate the effectiveness of these two main features, two experiments built on the Co-Net model are performed to test the newly developed algorithms. It has been found that CA3 significantly improves the matching scores by up to 35%, compared with conventional task–participant matching methods. The neural network based participant–tool matching mechanism yields robust results with 4% mismatches for 10% noise levels, and with 16% mismatches for 30% noise levels. Real case applications and implications are described, and further plans to extend this new framework are also outlined based on the reported experiments and evaluations.     

GIS-based decision support for solar energy planning in urban environments

This paper describes the development of a solar energy planning system, consisting of a methodology and decision support software for planners and energy advisers. Intended primarily to predict and realise the potential of solar energy on an urban scale, the system will support decisions in relation to the key solar technologies: solar water heating, photovoltaics and passive solar gain. The prototype discussed here relates to the first of these. Based on a methodology for predicting the solar energy potential of domestic housing stock, it is implemented as a relational database application linked to a customised geographical information system (GIS). The methodology takes into account baseline energy consumption and projected energy saving benefits. To support this, the system incorporates a domestic energy model and addresses the major problem of data collection in two ways. Firstly, it provides a comprehensive set of default values derived from a new dwelling classification scheme that builds on previous research. Secondly, novel GIS tools enable key data to be extracted from digital urban maps in different operational modes. The paper concludes with a discussion of possible planning scenarios to illustrate how the system may be deployed at various levels of granularity to assist targeting of individual properties or city neighbourhoods, or for whole-city projections.