Knowledge is an increasingly important asset for organizational competition, and knowledge management becomes the most important issue for an organization. Building knowledge ontology is a good solution to increase knowledge reusability. Ontology explicitly defines concepts and their relationships, which can facilitate user understanding and further analysis. Based on previous research (Wu, 2006; Chuang, 2006), this research proposes a refined method for the construction of event ontology. The method includes text pre-processing, event ontology construction, and event ontology presentation. The text pre-processing module includes POS tagger, word filter, and term analysis. Based on the concept of sub-event, we can build a 3-level architecture of event ontology that includes sub-events, events, and topics in the event ontology construction module. Event ontology construction module developed in the project provides a friendly editing environment for the user to edit the concepts and attributes of an event that may cover ¡§who,¡¨ ¡§what,¡¨ ¡§where,¡¨ and ¡§what object.¡¨ In the event ontology presentation module, event episode may be illustrated by event frames, flow charts, and Gantt charts. To verify the feasibility of the proposed method, a prototype system has been built. The Alexander Poison Event was used as an example to demonstrate the value of the prototype system.

Conservation of historic buildings requires comprehensive and correct information of buildings to be analyzed in conservation decision making process in a systematic and rational approach. Geographical Information Systems (GIS) are advantageous in such cases which can be defined as computer based systems for handling geographical and spatial data. GIS have the potential to support the conservation decision making process with their storing, analyzing and monitoring capabilities. Therefore, information systems like GIS can be seen as a potential significant instrument for dealing with the conservation projects. This thesis aims to analyze the transformation process of the data collected in conservation process into practical information in order to adapt this process to a spatial information system. In this context, use of Geographical Information Systems is tested in the process of historic building conservation on spatial information system designed for Doganlar Church izmir chosen as the case study. Hence the advantages and disadvantages of local information systems in conservation decision making process of historic buildings can be criticized.

Gait analysis is the process of collecting and analyzing quantitative information about walking patterns of the people. Gait analysis enables the clinicians to differentiate gait deviations objectively. Diagnostic decision making from gait data only requires high level of medical expertise of neuromusculoskeletal system trained for the purpose. An automated system is expected to decrease this requirement by a &lsquo / transformed knowledge&rsquo / of these experts. This study presents a clinical decision support system for the detecting and scoring of a knee disorder, namely, Osteoarthritis (OA). Data used for training and recognition is mainly obtained through Computerized Gait Analysis software. Sociodemographic and disease characteristics such as age, body mass index and pain level are also included in decision making. Subjects are allocated into four OA-severity categories, formed in accordance with the Kellgren-Lawrence scale: &ldquo / Normal&rdquo / , &ldquo / Mild&rdquo / , &ldquo / Moderate&rdquo / , and &ldquo / Severe&rdquo / . Different types of classifiers are combined to incorporate the different types of data and to make the best advantages of different classifiers for better accuracy. A decision tree is developed with Multilayer Perceptrons (MLP) at the leaves. This gives an opportunity to use neural networks to extract hidden (i.e., implicit) knowledge in gait measurements and use it back into the explicit form of the decision trees for reasoning. Individual feature selection is applied using the Mahalanobis Distance measure and most discriminatory features are used for each expert MLP. Significant knowledge about clinical recognition of the OA is derived by feature selection process. The final system is tested with test set and a success rate of about 80% is achieved on the average.

Clinical guidelines are developed to assist healthcare practitioners to make decisions on a patient&#039 / s medical problems and as such they communicate with external applications to retrieve patient data, to initiate medical actions through clinical workflows and to transmit information to alert/reminder systems. The interoperability problems in the healthcare IT domain for interacting with heterogeneous clinical workflow systems and Electronic Healthcare Record (EHR) Systems prevent wider deployment of clinical guidelines because each deployment requires a tedious custom adaptation phase. In this thesis, we provide machine processable mechanisms that express the semantics of clinical guideline interfaces so that automated processes can be used to access the clinical resources for guideline deployment and execution. For this purpose, we propose a semantically enriched clinical guideline representation formalism by extending one of the computer interpretable guideline representation languages, GuideLine Interchange Format (GLIF). To be able to deploy the semantically extended guidelines to healthcare settings semi-automatically, the underlying application&#039 / s semantics must also be available. We describe how this can be achieved based on two prominent implementation technologies in use in the eHealth domain: Integrating Healthcare Enterprise (IHE) Cross Enterprise Document Sharing Integration Profile (XDS) for discovering and exchanging EHRs and Web service technology for interacting with the clinical workflows and wireless medical sensor devices. Since the deployment and execution architecture should be dynamic, and address the heterogeneity of underlying clinical environment, the deployment and execution is coordinated by a multi-agent system. The system described in this thesis is realized within the scope of the SAPHIRE Project.

The research focuses on the application of site selection for cage aquaculture in Taiwan by developing the site evaluation DSS (Decision Support System). The modeling aspect of the system belongs to the domain of multi-criteria decision theories, which AHP (Analytic Hierarchy Process) and Fuzzy Set theory were used. Two case studies based on real world and hypothetical data were conducted to verify the integrity of the system. According to the literature review and the interview with several domain experts, various impact factors were identified first. The corresponding weights of each factor were then decided by analyzing the questionnaires designed based on the concept of AHP. The following work was to evaluate those impact factors based on the experience of domain experts using some appropriate approaches. To represent the domain knowledge, it is appropriate to use rule based inference system. Besides, fuzzy set theory was chosen to describe the antecedent and consequence of the rule base due to the considerations of uncertainty from human experts and ocean field data. Several related mythologies derived from the fuzzy set theory were used, such as the operation of fuzzy composition, determination of suitable membership function, fuzzy relationship matrix, fuzzy inference, defuzzification, and fuzzy pattern classification. All impact factors were categorized into three different types of membership functions that were designed specifically for the site selection of cage aquaculture. The consequence in the rule base, which is the site suitability, was also represented as the unique membership function. To calculate the fuzzy relationship matrix, the current research found that the operation of ¡§algebraic product and bounder sum¡¨ would produce better results than the commonly used ¡§max-min¡¨ operation. Each impact factor would have the associated fuzzy relationship matrix derived from the rule base. The site suitability in term of a fuzzy set can then be inferred by the fuzzy composition of current situation of the factor and the relationship matrix. By multiplying the AHP weight and the fuzzy suitability, the final site suitability index, taking all the impact factors into consideration, can therefore be derived. The real data in Feng-Gang, located in the southern Taiwan, were collected and evaluated using the site selection DSS. The results show Feng-Gang is suitable for the development of cage aquaculture, which is validated by the current prosperous business locally in cage aquaculture. As for the evaluation of multiple sites, 18 hypothetical sites near shore around Taiwan were chosen to calculate the corresponding suitability indexes, which were then be partitioned into several groups using the fuzzy pattern classification. Based on the results, the sites that were classified in the same group have similar cultivation conditions, which also proves the applicability of the site evaluation DSS.

The planning and scheduling requirement of industry can not be satisfied by traditional scheduling systems. Companies need to put extra human resource to fix the result made by these systems. The main reason is an improper assumption of infinite capacity adopted by these scheduling systems. In order to improve the scheduling result, this research refers plastic injection industry¡¦s characters to implement a decision support system. The decision support system integrates Forward Finite Loading and Constraint Directed into our algorithm in order to minimize the increase in total cost, and raise the capacity balance between machines .According to the result of the practical research, we can prove this decision support system is more effective and efficiency than the traditional scheduling method.

Consumer decision behavior has been an interesting research topic for researchers and marketing people. While E-Commerce websites and online self-service are becoming more and more popular, it is important to understand how to support consumer in their online shopping decision process. The purpose of this research is to understand how consumer¡¦s decision behavior would be influenced in online shopping environment, when facing different product price and complexity, and discover the personal factors that might influence it¡¦s decision behavior. The result of our research showed that product complexity has significant influence on consumer¡¦s decision behavior. When product complexity become higher, the effort and time for a consumer to complete a purchase decision will also become higher. When consumer is more familiar with the product, more involve with the product, or have higher computer self-efficacy, consumer will use much harder decision tools then usual. But our result also showed that product price has no influence on consumer¡¦s decision behavior, this might be that in our experiment, consumers don¡¦t really have to pay for the product, so the influence of product price has no effect on consumer. From our result we can know that for different product complexity and consumer will result in different decision behavior. In the future, online shopping store can provide different decision tools for different consumer when facing different products, to help consumer make better decision.

The current project is an examination of the effectiveness of decision-support graphics in a simulated real-world task, and of the role those graphics should play in training. It is also an attempt to apply a theoretical account of memory performance-transfer-appropriate processing-to naturalistic decision making. The task in question is a low-fidelity air traffic control simulation. In some conditions, that task includes decision-support graphics designed to explicitly represent elements of the task that normally must be mentally represented-namely, trajectory and relative altitude. The assumption is that those graphics will encourage a type of processing different from that used in their absence. If so, then according to the theory of transfer-appropriate processing (TAP), the best performance should occur in conditions in which the graphics are present either during both training and testing, or else not at all. For other conditions, the inconsistent presence or absence of the graphics should lead to mismatches in the type of processing used during training and testing, thus hurting performance. A sample of 205 undergraduate students were randomly assigned to four experimental and two control groups. The results showed that the support graphics provided immediate performance benefits, regardless of their presence during training. However, presenting them during training had an apparent overshadowing effect, in that removing them during testing significantly hurt performance. Finally, although no support was found for TAP, some support was found for the similar but more general theory of identical elements.

Ashe Juniper encroachment onto privately owned rangelands in Central Texas has resulted in significant degradation of the ecological condition of these lands, and a subsequent public concern for the hydrologic function, wildlife habitat, and livestock production these historically predominant grasslands provide. The result has been an interest and public investment in land restoration programs such as the removal and management of brush via landowner cost-share. Implementation of a publicly funded land restoration program requires the allocation of millions of dollars of public funds on private lands over large geographic areas that represent hundreds of landowners with varying property management objectives, tract sizes, ecological conditions, and geologic characteristics. This study describes the development, accuracy, and application of a decision support geographic information system (DSGIS) for land restoration programs in the Leon, Lampasas, and Bosque River watersheds in the Brazos River basin of Central Texas. The spatially referenced data layers and associated database within the DSGIS provide the capability to assemble site specific information including vegetation cover, endangered species habitat, landowners, ecological sites, elevation and slope, hydrologic characteristics, and political boundaries to support policy and implementation decisions for Ashe Juniper (Juniperus ashei) brush control and management and goldencheeked warbler (Dendroica chrysoparia) habitat restoration programs. The goldencheeked warbler is a federally listed endangered species with a breeding range limited to the oak-juniper woodlands of Central Texas. The data layers were developed with the support of ongoing research from the Leon River Restoration Project (LRRP) in Coryell and Hamilton counties. One hundred and eighty-eight (188) sub-watersheds were delineated within the project area and prioritized for implementation of an Ashe Juniper brush control program and a golden-cheeked warbler habitat restoration program. Costs associated with the clearing and stacking of Ashe Juniper were estimated for selected subwatersheds based on projected landowner participation and an analysis of actual costs from the LRRP. Sub-watersheds were targeted for the implementation of an Ashe Juniper brush control and golden-cheeked warbler habitat management program in Bosque, Coryell, Lampasas, Bell, and Burnet counties. Detailed tables were also developed to document the density and quantity of pertinent layer attributes within each of the 188 sub-watersheds.

This thesis work describes the formulation of a Bayesian approach along with new tools to systematically reduce uncertainty in Research&Development (R&D) alternatives. During the initial stages of R&D many alternatives are considered and high uncertainty exists for all the alternatives. The ideal approach in addressing the many R&D alternatives is to find the one alternative which is stochastically dominant i.e. the alternative which is better in all possible scenarios of uncertainty. Often a stochastically dominant alternative does not exist. This leaves the R&D manager with two alternatives, either to make a selection based on user defined utility function or to gather more information in order to reduce uncertainty in the various alternatives. From the decision makers perspective the second alternative has more intrinsic value, since reduction of uncertainty will improve the confidence in the selection and further reduce the high downside risk involved with the decisions made under high uncertainty. The motivation for this work is derived from our preliminary work on the evaluation of biorefiney alternatives, which brought into limelight the key challenges and opportunities in the evaluation of R&D alternatives. The primary challenge in the evaluation of many R&D alternatives was the presence of uncertainty in the many unit operations within each and every alternative. Additionally, limited or non-existent experimental data made it infeasible to quantify the uncertainty and lead to inability to develop an even simple systematic strategy to reduce it. Moreover, even if the uncertainty could be quantified, the traditional approaches (scenario analysis or stochastic analysis), lacked the ability to evaluate the key group of uncertainty contributors. Lastly, the traditional design of experiment approaches focus towards reduction in uncertainty in the parameter estimates of the model, whereas what is required is a design of experiment approach which focuses on the decision (selection of the key alternative). In order to tackle all the above mentioned challenges a Bayesian framework along with two new tools is proposed. The Bayesian framework consists of three main steps: a. Quantification of uncertainty b. Evaluation of key uncertainty contributors c. Design of experiment strategies, focussed on decision making rather than the traditional parameter uncertainty reduction To quantify technical uncertainty using expert knowledge, existing elicitation methods in the literature (outside chemical engineering domain) are used. To illustrate the importance of quantifying technical uncertainty, a bio-refinery case study is considered. The case study is an alternative for producing ethanol as a value added product in a Kraft mill producing pulp from softwood. To produce ethanol, a hot water pre-extraction of hemi-cellulose is considered, prior to the pulping stage. Using this case study, the methodology to quantify technical uncertainty using experts' knowledge is demonstrated. To limit the cost of R&D investment for selection or rejection of an R&D alternative, it is essential to evaluate the key uncertainty contributors. Global sensitivity analysis (GSA) is a tool which can be used to evaluate the key uncertainties. But quite often global sensitivity analysis fails to differentiate between the uncertainties and assigns them equal global sensitivity index. To counter this failing of GSA, a new method conditional global sensitivity (c-GSA) is presented, which is able to differentiate between the uncertainties even when GSA fails to do so. To demonstrate the value of c-GSA many small examples are presented. The third and the last key method in the Bayesian framework is the decision oriented design of experiment. Traditional 'Design of Experiment' (DOE) approaches focus on minimization of parameter error variance. In this work, a new "decision-oriented" DOE approach is proposed that takes into account how the generated data, and subsequently, the model developed based on them will be used in decision making. By doing so, the parameter variances get distributed in a manner such that its adverse impact on the targeted decision making is minimal. Results show that the new decision-oriented DOE approach significantly outperforms the standard D-optimal design approach. The new design method should be a valuable tool when experiments are conducted for the purpose of making R&D decisions. Finally, to demonstrate the importance of the overall Bayesian framework a bio-refinery case study is considered. The case study consists of the alternative to introduce a hemi-cellulose pre-extraction stage prior to pulping in a thermo-mechanical pulp mill. Application of the Bayesian framework to address this alternative, results in significant improvement in the prediction of the true potential value of the alternative.