A decision support system for energy saving in Waste Water Treatment Plants

Waste Water Treatment Plants (WWTPs) are complex facilities, in which an efficient energy management can produce relevant benefits for the environment and the economy. Today, big data can be used for a more efficient plant management, enabling high-frequency assessment and ultimately a more efficient use of resources. In order to achieve this, a computer-based support is necessary to analyse the enormous amount of data that WWTP sensors can produce. When this PhD project started, the literature review showed that, in the WWTP domain, the few available decision support systems (DSSs) were promising but still with large room for improvements; in fact, these tools were plant-specific, focussed mainly on process parameters and (most of them) working with low-frequency aggregated data (yearly data). This thesis instead proposes a cooperative decision support system called Shared Knowledge Decision Support System (SK-DSS). SK-DSS is plant generic, i.e. able to simultaneously work with many WWTPs and based on key performance indicators. SK-DSS analyses the processes occurring in the plants and provide case-based solutions. Moreover, this DSS provides a platform to enable the plant managers to exchange information and cooperate. This thesis proposes the model of SK-DSS, a web-application, and applications to improve the energy performance of pump, blowers and biogas

Knowledge engineering complex decision support system in managing rheumatoid arthritis.

Background: The management of rheumatoid arthritis (RA) involves partially recursive attempts to make optimal treatment decisions that balance the risks of the treatment to the patient against the benefits of the treatment, while monitoring the patient closely for clinical response, as inferred from prior and residual disease activity, and unwanted drug effects, including abnormal laboratory findings. To the extent that this process is logical, based on best available evidence and determined by considered opinion, it should be amenable to capture within a Clinical Decision Support Systems (CDSSs). The formalisation of logical transformations and their execution by computer tools at point of patient encounter holds the promise of more efficient and consistent use of treatment rules and more reliable clinical decision making. Research Setting: The early Rheumatoid Arthritis (eRA) clinic of the Royal Adelaide Hospital (RAH) with approximately 20 RA patient visits per week, and involving 160 patients with a median duration of treatment of more than 4.5 years. Methods: The study applied a Knowledge Engineering approach to interpret the complexities of RA management, in order to implement a knowledge-based CDSS. The study utilised Knowledge Acquisition processes to elicit and explicitly define the RA management rules underpinning the development of the CDSS; the processes were (1) conducting a comprehensive literature review of RA management, (2) observing clinic consultations and (3) consulting with local clinical experts/leaders. Bayes’ Theorem and Bayes Net were used to generate models for assessing contingent probabilities of unwanted events. A questionnaire based on 16 real patient cases was developed to test the concordance agreement between CDSS generated guidance in response to real-life clinical scenarios and decisions of rheumatologists in response to the scenarios. Results: (1) Complex RA management rules were established which included (a) Rules for Changes in Dose/Agent and (b) Drug Toxicity Monitoring Rules. (2) A computer interpretable dynamic model for implementing the complex clinical guidance was found to be applicable. (3) A framework for a methotrexate (MTX) toxicity prediction model was developed, thereby allowing missing risk ratios (probabilities) to be identified. (4) Clinical decision-making processes and workflows were described. Finally, (5) a preliminary version of the CDSS which computed Rules for Changes in Dose/Agent and Drug Toxicity Monitoring Rules was implemented and tested. One hundred and twenty-eight decisions collected from the 8 participating rheumatologists established the ability of the CDSS to match decisions of clinicians accustomed to application of Rules for Changes in Dose/Agent; rheumatologists unfamiliar with the rules displayed lower concordance (0.7857 vs. 0.3929, P = 0.0027). Neither group of rheumatologists matched the performance of the CDSS in making decisions based on highly complex Drug Toxicity Monitoring Rules (0.3611 vs. 0.4167, P = 0.7215). Conclusion: The study has made important contributions to the development of a CDSS suitable for routine use in the eRA clinic setting. Knowledge Acquisition processes were used to elicit domain knowledge, and to refine, validate and articulate eRA management rules, that came to form the knowledge base of the CDSS. The development of computer interpretable guideline models underpinned the CDSS development. The alignment of CDSS guidance in response to clinical scenarios with questionnaire responses of rheumatologists familiar with and accepting of the management rules (and divergence with responses by rheumatologists not familiar with the rules) indicates that the CDSS can be used to guide toward evidence-based considered opinion. The poor correlation between CDSS generated guidance regarding out of range blood results and response of rheumatologists to questions regarding toxicity scenarios, underlines the value of computer aided guidance when decisions involve greater complexity. It also suggests the need for attention to rule development and considered opinion in this area. Discussion: Effective utilisation of extant knowledge is fundamental to knowledgebased systems in healthcare. CDSSs development for chronic disease management is a complex undertaking which is tractable using Knowledge Engineering and Knowledge Acquisition approaches coupled with modelling into computer interpretable algorithms. Complexities of drug toxicity monitoring were addressed using Bayes’ Theorem and Bayes Net for making probability based decisions under conditions of uncertainty. While for logistic reasons the system could not be developed to full implementation, preliminary analyses support the utility of the approach, both for intensifying treatment on a response contingent basis and also for complex drug toxicity monitoring. CDSSs are inherently suited to iterative refinements based on new knowledge including that arising from analyses of the data they capture during their use. This study has achieved important steps toward implementation and refinement.Thesis (Ph.D.) -- University of Adelaide, School of Medicine, 201

Development of a decision support system through modelling of critical infrastructure interdependencies : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Emergency Management at Massey University, Wellington, New Zealand

Critical Infrastructure (CI) networks provide functional services to support the wellbeing of a community. Although it is possible to obtain detailed information about individual CI and their components, the interdependencies between different CI networks are often implicit, hidden or not well understood by experts. In the event of a hazard, failures of one or more CI networks and their components can disrupt the functionality and consequently affect the supply of services. Understanding the extent of disruption and quantification of the resulting consequences is important to assist various stakeholders' decision-making processes to complete their tasks successfully. A comprehensive review of the literature shows that a Decision Support System (DSS) integrated with appropriate modelling and simulation techniques is a useful tool for CI network providers and relevant emergency management personnel to understand the network recovery process of a region following a hazard event. However, the majority of existing DSSs focus on risk assessment or stakeholders' involvement without addressing the overall CI interdependency modelling process. Furthermore, these DSSs are primarily developed for data visualization or CI representation but not specifically to help decision-makers by providing them with a variety of customizable decision options that are practically viable. To address these limitations, a Knowledge-centred Decision Support System (KCDSS) has been developed in this study with the following aims: 1) To develop a computer-based DSS using efficient CI network recovery modelling algorithms, 2) To create a knowledge-base of various recovery options relevant to specific CI damage scenarios so that the decision-makers can test and verify several ‘what-if’ scenarios using a variety of control variables, and 3) To bridge the gap between hazard and socio-economic modelling tools through a multidisciplinary and integrated natural hazard impact assessment. Driven by the design science research strategy, this study proposes an integrated impact assessment framework using an iterative design process as its first research outcome. This framework has been developed as a conceptual artefact using a topology network-based approach by adopting the shortest path tree method. The second research outcome, a computer-based KCDSS, provides a convenient and efficient platform for enhanced decision making through a knowledge-base consisting of real-life recovery strategies. These strategies have been identified from the respective decision-makers of the CI network providers through the Critical Decision Method (CDM), a Cognitive Task Analysis (CTA) method for requirement elicitation. The capabilities of the KCDSS are demonstrated through electricity, potable water, and road networks in the Wellington region of Aotearoa New Zealand. The network performance has been analysed independently and with interdependencies to generate outage of services spatially and temporally. The outcomes of this study provide a range of theoretical and practical contributions. Firstly, the topology network-based analysis of CI interdependencies will allow a group of users to build different models, make and test assumptions, and try out different damage scenarios for CI network components. Secondly, the step-by-step process of knowledge elicitation, knowledge representation and knowledge modelling of CI network recovery tasks will provide a guideline for improved interactions between researchers and decision-makers in this field. Thirdly, the KCDSS can be used to test the variations in outage and restoration time estimates of CI networks due to the potential uncertainty related to the damage modelling of CI network components. The outcomes of this study also have significant practical implications by utilizing the KCDSS as an interface to integrate and add additional capabilities to the hazard and socio-economic modelling tools. Finally, the variety of ‘what-if’ scenarios embedded in the KCDSS would allow the CI network providers to identify vulnerabilities in their networks and to examine various post-disaster recovery options for CI reinstatement projects

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

The Principles Of Developing A Management Decision Support System For Scientific Employees

Employees engaged in mental work have become the most valuable assets of any organization in the 21st century. The satisfaction of those involved in mental work requires the provision of objectivity and transparency in their decision-making. This, in turn, entails the development of scientifically motivated decision making mechanisms and scientific-methodological approaches to evaluate their performance based on innovative technologies.The main goal of this article is in development of the scientific and methodological framework for the establishment of a decision support system to manage the employees engaged in mental work and operating in uncertainty. In this regard, initially, the question of evaluating the activities of scientific workers is examined, its characteristic features are determined, and the fuzzy relation model is proposed as a multi-criterion issue formed in uncertainty. Taking into consideration the hierarchical structure of the criteria that allows evaluating the activities of scientific workers, a phased solution method based on an additive aggregation method is proposed. In accordance with the methodology, a functional scheme of the decision support system to manage the scientific personnel is developed. The working principle of each block and the interaction of the blocks are described. The rules for the employees\u27 management decisions are shown by referring to the knowledge production model.Based on the proposed methodological approach, the implementation phases of the decision support system for the management of the scientific workers of the Institute of Information Technology of ANAS are described. To evaluate the employees\u27 performance, the tools to collect initial information, evaluate the system of criteria, define their importance coefficients and mathematical descriptions are provided. Some results of the system software are presented. The opportunities of the system based on the proposed methodology to support enterprise mangers to make scientifically justified decisions are provided

Coastal Priority Ranking in Oil Spill Response Decision Support Mechanism

Millions of tons of oil are produced in the world every year and over half of it is transported to the users by means of marine routes. Based on statistics, a best estimate of oil spill is more than 3 million tons per year. Oil spills cause disastrous impacts on the environment, ecology and socio-economic activities. The right decision has to be made in the event of an oil spill to facilitate prompt action, considering the priorities of protection, to prevent environmental damages. Interest in having modern, technological management system in semi-structured fields such as disastrous incidents is increasing rapidly. Response decision support is a mechanism utilizing a knowledge-based plan to choose the most suitable method of response by analyzing the various sensitivity factors, parameters affecting oil spill impacts, environmental concerns in oil spill response, and consequence monitoring and clean-up operations in the shortest time. Environmental sensitivity index (ESI), a traditional scale, is mostly a static scale for evaluation of coastal situation. It requires calibration along with oil nature and impact in each spill case to be able of priority displaying in action. This study aimed to develop a semiautomatic knowledge-based decision support mechanism to retrieve experts’ knowledge for prioritization in responding to oil spill events. A tool was needed to classify information about knowledge and expertise in this field and follow the rational logic of master minds and could be transferable. The knowledge and expertise from knowledgeable participants were obtained through questionnaires and direct interviews as well as information from literatures. Three objectives were covered by the study including ranking of sensitivity-oil-response criteria, development of coastal priority ranking (CPR) scale, and establishment of a validated computer-based mechanism for oil spill response (OSR-DSM). Analyses of questions were conducted using Delphi method, Likert scaling, and repertory grid analysis. The evaluation of knowledge level provided the normalized weights (from 0.09 to 1.0) for respondents’ knowledge and these weights were applied to criteria ranking. Considering two objects of environment and oil, priority ranking matrix was established and CPR scale was calculated based on the fact that various “low/ medium/ high” impacting scenarios of oil can affect the corresponding “low/ medium/ high” sensitive resources. One program was designed to visualize DSM with computation of ESI, coastal sensitivity, oil impact, and CPR values as well as reporting on response alternatives. The advantage of CPR scale method was its ability for a more dynamic quantitative evaluation of priorities in application time rather than only explaining sensitivity indices of area. The scale for CPR was evaluated ranging from 35 to 469 and the values were qualitatively categorized from low priority to medium, high, very high and extremely high priorities. Three major categories were renowned for responses alternatives - on-sea response or preventive activities, shoreline protective activities, and on-coast response or cleanup activities. Results were verified to present the inclusiveness, accuracy, and system algorithm. The verification activity involved exploring the knowledge base, coding of reasoning processes / inference engine, technical performance, ability for development, and interface. A total of 80 percent of users in the verification phase believed that development of such mechanism was a right approach for supporting the right decision in oil spill responses, either by increasing the speed and accuracy in evaluation or reducing the cost. Verification research could attain rates of over 50 percent in all five categories. General rates given to the mechanism by two groups of users were 82 and 85 percent with a + 3.66 percent of uncertainty that was issued a high verification value. This study has resulted in two main products: - coastal priority ranking scale (CPR) and oil spill response decision supporting mechanism (OSR-DSM). It is intended to facilitate the oil spill response process while at the same time improves the decision-making quality by applying the effective knowledge and expertise in oil spill response procedures. Definition of knowledge criteria leading to classification of knowledgeable participants, as well as numerical verification frame for qualitative knowledge-base mechanism were two significant outputs of this study

The necessities for building a model to evaluate Business Intelligence projects- Literature Review

In recent years Business Intelligence (BI) systems have consistently been rated as one of the highest priorities of Information Systems (IS) and business leaders. BI allows firms to apply information for supporting their processes and decisions by combining its capabilities in both of organizational and technical issues. Many of companies are being spent a significant portion of its IT budgets on business intelligence and related technology. Evaluation of BI readiness is vital because it serves two important goals. First, it shows gaps areas where company is not ready to proceed with its BI efforts. By identifying BI readiness gaps, we can avoid wasting time and resources. Second, the evaluation guides us what we need to close the gaps and implement BI with a high probability of success. This paper proposes to present an overview of BI and necessities for evaluation of readiness. Key words: Business intelligence, Evaluation, Success, ReadinessComment: International Journal of Computer Science & Engineering Survey (IJCSES) Vol.3, No.2, April 201