Representing human expertise by the OWL web ontology language to support knowledge engineering in decision support systems

Clinical decision support systems (CDSSs) often base their knowledge and advice on human expertise. Knowledge representation needs to be in a format that can be easily understood by human users as well as supporting ongoing knowledge engineering, including evolution and consistency of knowledge. This paper reports on the development of an ontology specification for managing knowledge engineering in a CDSS for assessing and managing risks associated with mental-health problems. The Galatean Risk and Safety Tool, GRiST, represents mental-health expertise in the form of a psychological model of classification. The hierarchical structure was directly represented in the machine using an XML document. Functionality of the model and knowledge management were controlled using attributes in the XML nodes, with an accompanying paper manual for specifying how end-user tools should behave when interfacing with the XML. This paper explains the advantages of using the web-ontology language, OWL, as the specification, details some of the issues and problems encountered in translating the psychological model to OWL, and shows how OWL benefits knowledge engineering. The conclusions are that OWL can have an important role in managing complex knowledge domains for systems based on human expertise without impeding the end-users' understanding of the knowledge base. The generic classification model underpinning GRiST makes it applicable to many decision domains and the accompanying OWL specification facilitates its implementation.

The Digital Observatory for Protected Areas (DOPA) as a support tool for protected area planning, financing and reporting

The Joint Research Centre (JRC) of the European Commission has developed, in consultation with many partners, the DOPA as a global reference information system to support decision making on protected areas (PAs) and biodiversity conservation. The DOPA brings together the World Database on Protected Areas with other reference datasets on species, habitats, ecoregions, threats and pressures, to deliver critical indicators at country level and PA level that can inform gap analyses, PA planning and reporting. These indicators are especially relevant to Aichi Targets 11 and 12, and have recently contributed to CBD country dossiers and capacity building on these targets. DOPA also includes eConservation, a new module that provides a means to share and search information on conservation projects, and thus allows users to see “who is doing what where”. So far over 5000 projects from the World Bank, GEF, CEPF, EU LIFE Programme, CBD LifeWeb Initiative and others have been included, and these projects can be searched in an interactive mapping interface based on criteria such as location, objectives, timeframe, budget, the organizations involved, target species etc. This seminar will provide an introduction to DOPA and eConservation, highlight how these services are used by the CBD and others, and include ample time for discussion.

Modulating membership grades to gain consensus for fuzzy set uncertainty values in a clinical decision support system

This paper deals with a very important issue in any knowledge engineering discipline: the accurate representation and modelling of real life data and its processing by human experts. The work is applied to the GRiST Mental Health Risk Screening Tool for assessing risks associated with mental-health problems. The complexity of risk data and the wide variations in clinicians' expert opinions make it difficult to elicit representations of uncertainty that are an accurate and meaningful consensus. It requires integrating each expert's estimation of a continuous distribution of uncertainty across a range of values. This paper describes an algorithm that generates a consensual distribution at the same time as measuring the consistency of inputs. Hence it provides a measure of the confidence in the particular data item's risk contribution at the input stage and can help give an indication of the quality of subsequent risk predictions. © 2010 IEEE.

Public-oriented interactive environmental decision support system

With the application of GIS methodologies to spatial data, researchers can now identify patterns of occurrence for many social problems including health-issues and crime. Further more, since this type of data also contains clues as to the underlying causes of social problems, it can be used to make well-educated and consequently, more effective policy decisions.

Research Monograph: Building Online Participatory Systems: Towards the Community Based Interactive Environmental Decision Support Systems

The importance of broadening community participation in environmental decision-making is widely recognized and lack of participation in this process appears to be a perennial problem. In this context, there have been calls from some academics for the more extensive use of geographic information systems (GIS) and distance learning technologies, accessible via the Internet, as a possible means to inform and empower communities. However, a number of problems exist. For instance, at present the scope for online interaction between policy-makers and citizens is currently limited. Contemporary web-based environmental information systems suffer from this lack of interactivity on the one hand and on the other hand from the apparent complexity for the lay user. This paper explores the issue of online community participation at the local level and attempts to construct a framework for a new (and potentially more effective) model of online participatory decision-making. The key components, system architecture and stages of such a model are introduced. This model, referred to as a ‘Community Based Interactive Environmental Decision Support System’, incorporates advanced information technologies, distance learning and community involvement tools which will be applied and evaluated in the field through a pilot project in Tokyo in the summer of 2002.

National practices in investment decision making in road sector

This document provides the findings of a national review of investment decision-making practices in road asset management. Efforts were concentrated on identifying the strategic objectives of agencies in road asset management, establishing and understanding criteria different organisations adopted and ascertaining the exact methodologies used by different sate road authorities. The investment objectives of Australian road authorities are based on triple-bottom line considerations (social, environmental, economic and political). In some cases, comparing with some social considerations, such as regional economic development, equity, and access to pubic service etc., Benefit-Cost Ratio has limited influence on the decision-making. Australian road authorities have developed various decision support tools. Although Multi-Criteria Analysis has been preliminarily used in case by case study, pavement management systems, which are primarily based on Benefit Cost Analysis, are still the main decision support tool. This situation is not compatible with the triple-bottom line objectives. There is need to fill the gap between decision support tools and decision-making itself. Different decision criteria should be adopted based on the contents of the work. Additional decision criteria, which are able to address social, environmental and political impacts, are needed to develop or identify. Environmental issue plays a more and more important role in decision-making. However, the criteria and respective weights in decision-making process are yet to be clearly identified. Social and political impacts resulted from road infrastructure investment can be identified through Community Perceptions Survey. With accumulative data, prediction models, which are similar as pavement performance models, can be established. Using these models, the decision-makers are able to foresee the social and political consequences of investment alternatives.

Decision support in using fibre reinforced polymer (FRP) composites in rehabilitation of concrete bridge structures

This paper compares and reviews the recommendations and contents of the guide for the design and construction of externally bonded FRP systems for strengthening concrete structures reported by ACI committee 440 and technical report of Externally bonded FRP reinforcement for RC structures (FIB 14) in application of carbon fiber reinforced polymer (CFRP) composites in strengthening of an aging reinforced concrete headstock. The paper also discusses the background, limitations, strengthening for flexure and shear, and other related issues in use of FRP for strengthening of a typical reinforced concrete headstock structure such as durability, de-bonding, strengthening limits, fire and environmental conditions. A case study of strengthening of a bridge headstock using FRP composites is presented as a worked example in order to illustrate and compare the differences between these two design guidelines when used in conjunction with the philosophy of the Austroads (1992) bridge design code.

Decision support system architecture for consultant selection

Decision Support System (DSS) has played a significant role in construction project management. This has been proven that a lot of DSS systems have been implemented throughout the whole construction project life cycle. However, most research only concentrated in model development and left few fundamental aspects in Information System development. As a result, the output of researches are complicated to be adopted by lay person particularly those whom come from a non-technical background. Hence, a DSS should hide the abstraction and complexity of DSS models by providing a more useful system which incorporated user oriented system. To demonstrate a desirable architecture of DSS particularly in public sector planning, we aim to propose a generic DSS framework for consultant selection. It will focus on the engagement of engineering consultant for irrigation and drainage infrastructure. The DSS framework comprise from operational decision to strategic decision level. The expected result of the research will provide a robust framework of DSS for consultant selection. In addition, the paper also discussed other issues that related to the existing DSS framework by integrating enabling technologies from computing. This paper is based on the preliminary case study conducted via literature review and archival documents at Department of Irrigation and Drainage (DID) Malaysia. The paper will directly affect to the enhancement of consultant pre-qualification assessment and selection tools. By the introduction of DSS in this area, the selection process will be more efficient in time, intuitively aided qualitative judgment, and transparent decision through aggregation of decision among stakeholders.

Building information modelling project decision support framework

Building Information Modelling (BIM) is an information technology [IT] enabled approach to managing design data in the AEC/FM (Architecture, Engineering and Construction/ Facilities Management) industry. BIM enables improved interdisciplinary collaboration across distributed teams, intelligent documentation and information retrieval, greater consistency in building data, better conflict detection and enhanced facilities management. Despite the apparent benefits the adoption of BIM in practice has been slow. Workshops with industry focus groups were conducted to identify the industry needs, concerns and expectations from participants who had implemented BIM or were BIM “ready”. Factors inhibiting BIM adoption include lack of training, low business incentives, perception of lack of rewards, technological concerns, industry fragmentation related to uneven ICT adoption practices, contractual matters and resistance to changing current work practice. Successful BIM usage depends on collective adoption of BIM across the different disciplines and support by the client. The relationship of current work practices to future BIM scenarios was identified as an important strategy as the participants believed that BIM cannot be efficiently used with traditional practices and methods. The key to successful implementation is to explore the extent to which current work practices must change. Currently there is a perception that all work practices and processes must adopt and change for effective usage of BIM. It is acknowledged that new roles and responsibilities are emerging and that different parties will lead BIM on different projects. A contingency based approach to the problem of implementation was taken which relies upon integration of BIM project champion, procurement strategy, team capability analysis, commercial software availability/applicability and phase decision making and event analysis. Organizations need to understand: (a) their own work processes and requirements; (b) the range of BIM applications available in the market and their capabilities (c) the potential benefits of different BIM applications and their roles in different phases of the project lifecycle, and (d) collective supply chain adoption capabilities. A framework is proposed to support organizations selection of BIM usage strategies that meet their project requirements. Case studies are being conducted to develop the framework. The results of the preliminary design management case study is presented for contractor led BIM specific to the design and construct procurement strategy.

Intelligent urban management systems : online decision support systems for ubiquitous cities

A successful urban management support system requires an integrated approach. This integration includes bringing together economic, socio-cultural and urban development with a well orchestrated, transparent and open decision making mechanism. The chapter emphasizes the importance of integrated urban management to better tackle the climate change, and to achieve sustainable urban development and sound urban growth management. This chapter introduces recent approaches on urban management systems, such as intelligent urban management systems, that are suitable for ubiquitous cities. The chapter discusses the essential role of online collaborative decision making in urban and infrastructure planning, development and management, and advocates transparent, fully democratic and participatory mechanisms for an effective urban management system that is particularly suitable for ubiquitous cities. This chapter also sheds light on some of the unclear processes of urban management of ubiquitous cities and online collaborative decision making, and reveals the key benefits of integrated and participatory mechanisms in successfully constructing sustainable ubiquitous cities.

Decision support system for contractor pre-qualification : artificial neural network model

The selection criteria for contractor pre-qualification are characterized by the co-existence of both quantitative and qualitative data. The qualitative data is non-linear, uncertain and imprecise. An ideal decision support system for contractor pre-qualification should have the ability of handling both quantitative and qualitative data, and of mapping the complicated nonlinear relationship of the selection criteria, such that rational and consistent decisions can be made. In this research paper, an artificial neural network model was developed to assist public clients identifying suitable contractors for tendering. The pre-qualification criteria (variables) were identified for the model. One hundred and twelve real pre-qualification cases were collected from civil engineering projects in Hong Kong, and eighty-eight hypothetical pre-qualification cases were also generated according to the “If-then” rules used by professionals in the pre-qualification process. The results of the analysis totally comply with current practice (public developers in Hong Kong). Each pre-qualification case consisted of input ratings for candidate contractors’ attributes and their corresponding pre-qualification decisions. The training of the neural network model was accomplished by using the developed program, in which a conjugate gradient descent algorithm was incorporated for improving the learning performance of the network. Cross-validation was applied to estimate the generalization errors based on the “re-sampling” of training pairs. The case studies show that the artificial neural network model is suitable for mapping the complicated nonlinear relationship between contractors’ attributes and their corresponding pre-qualification (disqualification) decisions. The artificial neural network model can be concluded as an ideal alternative for performing the contractor pre-qualification task.