View specification and manipulation for a semantic data model

Semantic data models are increasing in popularity and use, but they are also becoming increasingly complex and difficult to manage. In this paper we extend the definition of a semantic data model to give users the power to specify and manipulate views. Our model supports both high-level object-class views and high-level relationship-set views. To define these views, we extend the traditional method of view definition (through query formulation) to also include view definition by a dominant object class, by an independent object class, and by a relational object class. A set of operators to specify and manipulate views is also defined. These operators allow a user to create and destroy views, implode and explode views, hide and expose semantic-model elements. We also provide algorithms to extract an instance graph for a high-level object in a semantic-model view and to transform a semantic-model view hierarchy into an equivalent atomic model. Implications and applications of views in our model are also discussed.     

Developing an explanation component for a knowledge-based system: Discussion

The articles in this special issue cover a wide range, and represent many different conceptions of “explanation” and what explanation research should be about. In this short discussion article, I will address some of the implications of this work for those trying to develop a practical explanation system. In particular I will focus on how explanation needs are determined, how detailed explanation content is chosen, and how the explanations provided relate to the problem solving activities of the system.     

Developing a marketing decision model using a knowledge-based system

This paper describes using a knowledge-based system for developing a marketing decision model. The approach used in this study uses a decision table as a knowledge engineering tool. The decision table is used as a means of representing a set of decision rules to construct a developed marketing decision model. To support the modeling process, Prologa, an existing decision table engineering workbench, is used. The developed marketing decision model is used to determine the entrance time of a new product into market by utilizing knowledge-based systems. Presentation of a new product to the market at the best time will provide an advantage to competing companies and will increase their market share.     

An interactive knowledge-based system for forecasting new product reliability

During the development phases of a new product, special attention is usually directed to the allocation of reliability improvement programs. This paper will present an interactive knowledge-based decision support system (DSS) for optimizing this allocation process. The DSS utilizes an extension to the modified exponential reliability growth mode, which was developed to quantify and monitor reliability during the product development phases.

This paper addresses the reliability of complex electronic systems. It determines the rate of growth, and the level of initial reliability required in order to meet the expected market requirement.     

End-user manipulation of a knowledge-based system: a study of an expert's practice

The advent of end-user manipulation of knowledge-based systems (EUKMS) provides new opportunities for addressing the problems of encapsulating domain expertise. Interfaces which enable the expert, a professional and/or scientific practitioner, to create, refine and evaluate rules about the constituent elements of their knowledge provide a means of circumventing some of the current barriers to successful knowledge encapsulation. The critical feature of the design of such systems is the provision of facilities for the automatic conversion of the expert's rules into code. In a study of scientific work involving the capture of phonetics expertise in a knowledge-based system, key aspects of a speech scientist's working practice were identified. This paper discusses that use of the Speech Knowledge Interface system (SKI) in the context of investigations into the construction of an enhanced model of speech production for a speaker independent, continuous speech recognizer. Evidence that providing the expert with an appropriate interface to a knowledge-based system stimulates questions about existing knowledge and gives rise to new insights into the scope of the investigations, was found. Thus, the process of knowledge externalization, both of knowledge which was only partially realized and knowledge that was perceived as "new" by the expert, was facilitated by the interaction with the system.     

Experience of using a lightweight formal specification method for a commercial embedded system product line

A simple specification method is introduced and the results of its application to a series of projects in Philips are reported. The method is principally designed to ensure that that every unusual scenario is considered in a systematic way. In practice, this has led to high-quality specifications and accelerated product development. While the straightforward tabular notation used has proved readily understandable to non-technical personnel, it is also a formal method, producing a model of system behaviour as a finite state machine. In this respect, the notation is unusual in being designed to preserve as far as possible a view of the overall system state and how this changes. The notation also features a constraint table which may be described as a kind of spreadsheet for invariants to help define the states of the system.

Developing a knowledge-based early warning system for fish disease/health via water quality management

Fish disease is becoming the major factor damaging the sustainable development of aquaculture and the quality security of aquatic product in China. Many researchers provide assistances and advice in fish disease diagnosis and appropriate treatment measures. However, the risk of misdiagnosis, incorrectly treatment or over-treatment also exists due to carrying out after disease outbreaks. So it is necessary to develop an early warning system. Taking into account that fish disease outbreak is mainly caused by suboptimal water quality in north of China, the best way is to develop decision aids tool that can assess and early warning the risk of fish disease based on water quality management. This paper introduces a knowledge-based early warning system for fish disease via water quality management. The architecture, the process for knowledge engineering, main components and their functions, especially a series of water quality management mathematic models are described. At present, the system is in pilot in the city of Tianjin in China. The stage achievements in developing the intellectual system of fish disease early warning are summarized.     

ISIS—a knowledge-based system for factory scheduling

Abstract: Analysis of the job shop scheduling domain has indicated that the crux of the scheduling problem is the determination and satisfaction of a large variety of constraints. Schedules are influenced by such diverse and conflicting factors as due date requirements, cost restrictions, production levels, machine capabilities and substitutability, alternative production processes, order characteristics, resource requirements, and resource availability. This paper describes ISIS, a scheduling system capable of incorporating all relevant constraints in the construction of job shop schedules. We examine both the representation of constraints within ISIS, and the manner in which these constraints are used in conducting a constraint-directed search for an acceptable schedule. The important issues relating to the relaxation of constraints are addressed. Finally, the interactive scheduling facilities provided by ISIS are considered.     

Comparing a knowledge-based and a data-driven method in querying data streams for system fault detection: A hydraulic drive system application

The field of fault detection and diagnosis has been the subject of considerable interest in industry. Fault detection may increase the availability of products, thereby improving their quality. Fault detection and diagnosis methods can be classified in three categories: data-driven, analytically based, and knowledge-based methods.     

KBMS: a knowledge-based system for modeling software systemspecifications

Recent research results in software engineering have shown that the process of formalizing and automating software specification activity is moving towards knowledge-based specification systems. A modeling approach and a knowledge-based system that assist in the requirement acquisition, development, and maintenance of such software system specifications are presented     

TUNEX: a knowledge-based system for performance tuning of the UNIXoperating system

TUNEX, an expert system developed for performance tuning of the UNIX operating system, is described. TUNEX was developed on UNIX system V. It uses the properties, commands and utilities of this version. The tuning activities it is concerned with include: (1) adjusting operating system tunable parameters, such as number of disk buffers; (2) running maintenance routines, i.e. reorganizing file systems; (3) developing operation rules, such as off-peak hour runs of backups; and (4) modifying hardware, buying an additional disk drive. The structure of TUNEX is presented and performance analysis modules which provide quantitative information to this tool are briefly described. The overhead in the resource usage introduced by the performance monitoring and tuning tool itself is discussed; the author points to the areas in which additional resources are required by TUNEX     

Design and validation of a knowledge-based system for screeningproduct innovations

The authors' research goal is to develop and validate an expert system that screens innovations prior to commercialization. This is an important research issue because business corporations are highly dependent on innovations for their growth and profitability, yet most corporations suffer from a high rate of new product failure. Few of the existing decision support systems have alleviated this problem, partly because of their inability to deal with nonmathematical (logical) relationships. An expert system for new product planning could save organizations tremendous amounts of resources (such as dollars, time and scientific talent) spent on product failures. The design of the proposed knowledge-based system is built upon the authors' earlier work in this area. The authors have addressed several critical research issues in the development of such a system: choice of the appropriate sources of knowledge, resolution of conflict among human experts chosen for knowledge acquisition, use of knowledge programming techniques that can accommodate uncertainty, and multiple methods of system validation. The research makes several contributions to marketing theory and practice. Most notably, the development of such systems contributes to effective product planning in organizations and enhances resource efficiency. Further, it generates guidelines for capturing and using expertise in highly unstructured decision-making situations such as product management     

Designing a knowledge-based system for benchmarking: A DEA approach

First developed by Xerox in 1979, benchmarking provides measurement and comparison to improve processes and achieve higher performance. Benchmarking has proven a powerful tool for total quality management and process improvement. Successful benchmarking implementation is based on an effective benchmarking tool. To effectively implement benchmarking processes, this work proposes an integrated framework for the benchmarking tool and knowledge-based system using the data envelopment analysis (DEA) method, and then develops an intellectual benchmarking knowledge-based system (BKBS) for benchmarking, performance evaluation and process improvement. Accordingly, this work illustrates how the benchmarking knowledge-based system (BKBS) is implemented in a medical center. This system can help determine the particular benchmarking partners to evaluate the relative efficiency and fill the gaps between the benchmarking partners in the healthcare industry. Finally, the intellectual benchmarking knowledge-based system offers a very fast way to implement the benchmarking processes.     

Developing a novel inverse data envelopment analysis (DEA) model for evaluating after-sales units

This paper proposes a novel model of inverse data envelopment analysis (IDEA) based on the slack-based measure (SBM) approach. The developed inverse SBM model can maintain relative efficiency of decision making units (DMUs) with new input and output. This model can also measure the input and output volumes when a decision maker (DM) increases efficiency score. The inverse SBM model is a kind of multi-objective non-linear programming (MONLP) problem, which is not easy to solve. Therefore, we suggest a linear programming model for solving inverse SBM model. In this model efficiency score of DMU under evaluation remains unchanged. Furthermore, we suggest an optimal combination of inputs and outputs in the production possibility set (PPS). A case study is presented to demonstrate the efficacy of our proposed model.