Cybercartography as a Theoretical Framework for the Design of an Expert-Topographic Map Service

A knowledge-based expert system model working on the basis of a geographical information system (GIS) was applied to predict fishing ground spots in the coastal waters of South and Central Sulawesi. The model is designed by the integration of multisource data to answer ‘what?’, ‘where?’, and ‘why?’ questions of the fishing ground location. Despite the fact that GIS is a powerful tool for dealing with the first two questions, GIS is inferior for answering the ‘why?’ question in geo-studies. One of the possible ways of overcoming the inferiority of GIS for answering the ‘why?’ question of geo-studies is by integrating an expert system in a GIS to form a knowledge-based expert system GIS model. In this study, we used a series of sea surface temperature (SST) satellite data, sea surface chlorophyll-a (SSC) and turbidity derived from MODIS Aqua in the period 2003–2005 as input data, to understand the temporal and seasonal variability of the marine environment of the study area, and identified the oceanographic phenomena, i.e. upwelling, front or eddy. A spatial configuration map of the predicted fishing ground spots was then developed and integrated using a knowledge-based expert system GIS model generated by the Erdas Macro Language (EML) of Erdas Imagine 9.0 software. To verify this result, a series of in situ fishing ground spot data of the study area were collected for similar periods, and they were then analysed using a simple statistical method. The result shows that the predicted fishing ground spots generated by the knowledge-based expert system GIS model corresponded well with in situ data with a high accuracy level of 85%. This result has demonstrated that the knowledge-based expert system GIS model can be applied to predict, localize and determine fishing ground spots in which their accuracy level will be determined by the completeness of spatial knowledge of the domain expertise and the sophistication level of the programming utilities being used.

The loss of professional experience and expertise in the domain of the earth sciences may prove to be one of the most serious outcomes of the boom-and-bust cyclic nature of the volatile energy and mining industries. Promising new applications of powerful computer systems, known as ''expert systems'' or ''knowledge-based systems,'' are predicted for use in the earth sciences. These systems have the potential capability to capture and preserve the invaluable knowledge basis essential to the evaluation of U.S. energy and mineral resources.

The focus of this chapter is the provision of decision support modeled, at least in part, on human expertise. This chapter is divided into sections, each concerned with some aspect of the relationship between human expertise, statistical modeling, and knowledge-based systems, in particular expert systems. The first section provides a comparison of human expert judgment with statistical models, particularly linear regression models, concluding that statistical techniques have been shown to be more accurate than human experts given knowledge of which variables to include in the analysis and the relevant data. The second section provides a brief introduction to expert systems and a comparison of the accuracy of expert systems with human experts. Like statistical models, some expert systems have been shown to be more accurate than human experts. The third section discusses the circumstances under which symbolic representation of knowledge (as rules or frames) or purely statistical approaches are likely to be more useful in building decision aids. Since the utility of a decision support system is a combined function of both the cost of system development and the change in performance of the decision maker(s) using the system, this analysis necessarily raises considerations relating to knowledge engineering and utility of system explanations as well as that of accuracy. The final section summarizes the relative merits and demerits of statistical and symbolic styles of reasoning, emphasizing the complementarity of the two approaches, and provides a brief illustration of how statistical and symbolic reasoning may be combined within a knowledge-based decision support system.

Knowledge mined from clinical data can be used for medical diagnosis and prognosis. By improving the quality of knowledge base, the efficiency of prediction of a knowledge-based system can be enhanced. Designing accurate and precise clinical decision support systems, which use the mined knowledge, is still a broad area of research. This work analyses the variation in classification accuracy for such knowledge-based systems using different rule lists. The purpose of this work is not to improve the prediction accuracy of a decision support system, but analyze the factors that influence the efficiency and design of the knowledge base in a rule-based decision support system. Three benchmark medical datasets are used. Rules are extracted using a supervised machine learning algorithm (PART). Each rule in the ruleset is validated using nine frequently used rule interestingness measures. After calculating the measure values, the rule lists are used for performance evaluation. Experimental results show variation in classification accuracy for different rule lists. Confidence and Laplace measures yield relatively superior accuracy: 81.188% for heart disease dataset and 78.255% for diabetes dataset. The accuracy of the knowledge-based prediction system is predominantly dependent on the organization of the ruleset. Rule length needs to be considered when deciding the rule ordering. Subset of a rule, or combination of rule elements, may form new rules and sometimes be a member of the rule list. Redundant rules should be eliminated. Prior knowledge about the domain will enable knowledge engineers to design a better knowledge base.

A distributed knowledge-based system for the optimum utilisation of South African wool

ISJ Editorial

In Memoriam: Dr. Clive L. Dym (1942–2016)

PurposeService design is a multidisciplinary approach that plays a key role in fostering service innovation. However, the lack of a comprehensive understanding of its multiple perspectives hampers this potential to be realized. Through an activity theory lens, the purpose of this paper is to examine core areas that inform service design, identifying shared concerns and complementary contributions.Design/methodology/approachThe study involved a literature review in two stages, followed by a qualitative study based on selected focus groups. The first literature review identified core areas that contribute to service design. Based on this identification, the second literature review examined 135 references suggested by 13 world-leading researchers in this field. These references were qualitatively analyzed using the NVivo software. Results were validated and complemented by six multidisciplinary focus groups with service research centers in five countries.FindingsSix core areas were identified and characterized as contributing to service design: service research, design, marketing, operations management, information systems and interaction design. Data analysis shows the various goals, objects, approaches and outcomes that multidisciplinary perspectives bring to service design, supporting them to enable service innovation.Practical implicationsThis paper supports service design teams to better communicate and collaborate by providing an in-depth understanding of the multiple contributions they can integrate to create the conditions for new service.Originality/valueThis paper identifies and examines the core areas that inform service design, their shared concerns, complementarities and how they contribute to foster new forms of value co-creation, building a common ground to advance this approach and leverage its impact on service innovation.

This paper provides an overview of the burgeoning new field of expert (knowledge-based) systems. This survey, is tutorial in nature, intended to convey thegestalt of such systems to engineers who are newly exposed to the field. The discussion includes definitions, basic concepts, expert system architecture, descriptions of some of the programming tools and environments with which knowledge-based systems can be built, and approaches to knowledge acquisition. Some currently extant expert systems are describeden passant, including a few developed for engineering purposes. Comments follow on the engineering of knowledge, as both cultural and social processes. The paper closes with an assessment of the roles that expert systems can play in engineering analysis, design, planning, and education.

A freight transport model for integrated network, Service, and policy design

With the development of society and the progress of human civilization, the application of science and technology in people’s daily production and life is becoming more and more extensive. The application of computer information science and technology in logo design of digital era has gradually become a development trend in the future. Signs generally have long-term use value, once established, they will not change easily. However, with the rapid development of this era, the design of modern signs needs to follow the development of the digital era to design. In order to better help the logo design of the digital era in the current era, this paper puts forward the method of applying computer information science and technology to the logo design in the digital era. Through in-depth study of the current status of logo design in the digital era, the process of logo design in the digital era is simulated, so as to formulate a set of logo design most suitable for the digital era in this era Methods of calculation. Through the analysis, the method proposed in this paper successfully provides a new development idea for the application of computer information science and technology in the digital era logo design.

Expert systems (ES) and database systems (DBS) are major components of information systems and important assets to companies. The development of these systems represent users' knowledge in the application systems. As computer technologies evolve, and as users requirements change, there is a need to upgrade these system to meet the new application requirements. To preserve the knowledge of the existing information systems, a methodology for integrating ES and DBS into an expert database system (EDS) is proposed. The integrated EDS is a knowledge based system (KBS) which derives and stores knowledge in a frame model consisting of a class header, attributes, methods and constraints. It extracts the ES rules and DBS data for an application into coupling classes at run time only. The attributes of the coupling classes are matched with synonyms in a synonym table which resolves their naming and semantic conflicts with user assistance in knowledge acquisition. The resultant EDS is a KBS ready for application development.

Is it time for computer-assisted decision making to improve the quality of food and nutrition services?

There is a need to integrate knowledge based systems (KBS) with information systems (IS) technical solutions, which implies that KBS and IS development methodologies should be less isolated from each other. KBS and IS development methodologies are generally examined in terms of their similarities and differences. There is divergence at the feasibility and analysis stages, convergence at the design and coding stages, divergence during testing, convergence at the implementation stage, and divergence during maintenance. There are more similarities than there are differences between IS and KBS methodologies, particularly during the strategic planning stage. It is argued that linking KBS strategic planning to the planning element of an IS methodology will go further towards ensuring that the whole of the business is considered, leading to better integrated IS/KBS solutions. Application selection activities of two well-known KBS methodologies are briefly analysed. We show that these activities, which precede the feasibility study, do not consider the strategic aspects of the use of KBS (and IS in general) in business organizations. It is argued that a KBS strategy should be formulated in relation to the IS strategy and the business strategy, therefore increasing the convergence between IS and KBS methodologies.

The rapid development of data processing, data management, and artificial intelligence techniques made it possible to design systems based on a large amount of expert knowledge. The following sections will give an overview of this area. First the combination of data processing, building large information systems, and integrating knowledge-based systems will be illustrated. A definition of the terms ‘knowledge-based systems’ and ‘expert systems’ is suggested by choosing examples among the growing number of existing systems, and by specifying components or modules of such a system. One chapter will be dedicated to a system for the automatic analysis of heart scintigrams developed at the University of Erlangen. The last chapter reports on experiences, possibilities for use and further expansions.