知识管理与决策支持系统

知识管理是现代企业管理理论发展的新阶段,传统的信息管理手段面临严峻的考验,已无法满足知识管理的需求。本文分析了决策支持系统在知识管理中的地位和作用,提出了基于知识管理的决策支持系统模型,并且对实现这种系统的关键技术进行了分析和评价。     

A fuzzy, knowledge-based decision support tool for production operations management

Abstract: Production operations managers frequently have to make decisions based on vague, imprecise knowledge. Any software tool developed to aid their decision making needs to take into account the approximate nature of the information available to them and the inexact knowledge to which individual facts are applied. Much of this knowledge is expressed as vague, linguistic articulations. A convenient framework for dealing with such approximate knowledge is fuzzy logic and fuzzy set theory. As a specific example, a system was developed for providing decision support in the Just-in-Time area of production operations management.     

Towards knowledge-based systems in clinical practice: development of an integrated clinical information and knowledge management support system

Given that clinicians presented with identical clinical information will act in different ways, there is a need to introduce into routine clinical practice methods and tools to support the scientific homogeneity and accountability of healthcare decisions and actions. The benefits expected from such action include an overall reduction in cost, improved quality of care, patient and public opinion satisfaction. Computer-based medical data processing has yielded methods and tools for managing the task away from the hospital management level and closer to the desired disease and patient management level. To this end, advanced applications of information and disease process modelling technologies have already demonstrated an ability to significantly augment clinical decision making as a by-product. The wide-spread acceptance of evidence-based medicine as the basis of cost-conscious and concurrently quality-wise accountable clinical practice suffices as evidence supporting this claim. Electronic libraries are one-step towards an online status of this key health-care delivery quality control environment. Nonetheless, to date, the underlying information and knowledge management technologies have failed to be integrated into any form of pragmatic or marketable online and real-time clinical decision making tool. One of the main obstacles that needs to be overcome is the development of systems that treat both information and knowledge as clinical objects with same modelling requirements. This paper describes the development of such a system in the form of an intelligent clinical information management system: a system which at the most fundamental level of clinical decision support facilitates both the organised acquisition of clinical information and knowledge and provides a test-bed for the development and evaluation of knowledge-based decision support functions.     

Restructuring decision tables for elucidation of knowledge

Decision tables are widely used in many knowledge-based and decision support systems. They allow relatively complex logical relationships to be represented in an easily understood form and processed efficiently. This paper describes second-order decision tables (decision tables that contain rows whose components have sets of atomic values) and their role in knowledge engineering to: (1) support efficient management and enhance comprehensibility of tabular knowledge acquired by knowledge engineers, and (2) automatically generate knowledge from a tabular set of examples. We show how second-order decision tables can be used to restructure acquired tabular knowledge into a condensed but logically equivalent second-order table. We then present the results of experiments with such restructuring. Next, we describe SORCER, a learning system that induces second-order decision tables from a given database. We compare SORCER with IDTM, a system that induces standard decision tables, and a state-of-the-art decision tree learner, C4.5. Results show that in spite of its simple induction methods, on the average over the data sets studied, SORCER has the lowest error rate.     

Eliciting knowledge and transferring it effectively to aknowledge-based system

The knowledge acquisition bottleneck impeding the development of expert systems is being alleviated by the development of computer-based knowledge acquisition tools. These work directly with experts to elicit knowledge, and structure it appropriately to operate as a decision support tool within an expert system. However, the elicitation of expert knowledge and its effective transfer to a useful knowledge-based system is complex and involves diverse activities. The complete development of a decision support system using knowledge acquisition tools is illustrated. The example is simple enough to be completely analyzed but exhibits enough real-world characteristics to give significant insights into the processes and problems of knowledge engineering     

Knowledge-based clinical pathway for medical quality improvement

Clinical pathways have been adopted for various diseases in clinical departments for quality improvement as a result of standardization of medical activities in treatment process. Using knowledge-based decision support on the basis of clinical pathways is a promising strategy to improve medical quality effectively. However, the clinical pathway knowledge has not been fully integrated into treatment process and thus cannot provide comprehensive support to the actual work practice. Therefore this paper proposes a knowledge-based clinical pathway management method which contributes to make use of clinical knowledge to support and optimize medical practice. We have developed a knowledge-based clinical pathway management system to demonstrate how the clinical pathway knowledge comprehensively supports the treatment process. The experiences from the use of this system show that the treatment quality can be effectively improved by the extracted and classified clinical pathway knowledge, seamless integration of patient-specific clinical pathway recommendations with medical tasks and the evaluating pathway deviations for optimization.     

CIMOSA: enterprise engineering and integration

Enterprises are rather complex systems which have to be managed for their internal affairs, but more importantly for the many relations to the different environments in which they are operating. Today, these environments are changing much more rapidly and the need for relevant information becomes of paramount importance in the decision making processes at all levels of enterprise management. Fluctuations in market demands, technology evolution and changing regulations require very flexible enterprise operations, capable of reacting to those changes. These reactions must be based on relevant and up-to-date information which must be supported by new decision support technology. The challenges in decision support concern the identification of relevant information, easy access and intelligent use of this information. Building and maintaining the enterprise knowledge base and enabling its efficient use for decision support are major tasks of enterprise engineering. Enterprise integration and its subsequent operation in the global environment of customers, suppliers and regulatory bodies will heavily depend on the availability and the continuous extension of this knowledge base. Enterprise modelling will play an important role in creating the knowledge base and in using it for enterprise integration and operational decision support. The paper discusses enterprise engineering as an enterprise life-cycle oriented discipline for identification, design, and implementation of enterprises and their continuous evolution. Current problems in the field are identified and initiatives are presented.     

Ontology-based empirical knowledge verification for professional virtual community

A professional virtual community provides an interactive platform for enterprise experts to create and share their empirical knowledge cooperatively, and the platform contains a tremendous amount of hidden empirical knowledge that knowledge experts have preserved in the discussion process. Therefore, enterprise knowledge management highly prioritises how to verify the empirical knowledge effectively before archiving it into enterprise knowledge repository for reuse. This work develops a novel scheme of ontology-based empirical knowledge verification for professional virtual community to assist domain experts in a professional virtual community to verify the logics of empirical knowledge, thus ensuring the quality of empirical knowledge and providing accurate knowledge decision support for knowledge workers. In particular, this work has the following objectives: propose an empirical knowledge verification model for a professional virtual community, design an ontology-based empirical knowledge verification process, develop techniques related to the ontology-based empirical knowledge verification and implement an ontology-based empirical knowledge verification mechanism with an illustrative example of securities trading. Results of this study facilitate efforts within the professional virtual community to verify empirical knowledge in order to provide knowledge workers with logic-correct empirical knowledge for decision support.     

面向多目标应用的土壤空间决策支持平台*

由于环境和人为因素的综合影响,土壤功能在人类时间尺度上可以产生较快的转变,此开发能够准确反映和预测土壤信息时空变化的决策支持平台,对于土地管理者和政策制定者而言是必需的。在解决多源异构数据集成、模型管理和配置、土壤分类参比查询、科学计算可视化、土壤知识自学习等关键问题的基础上,通过集成地理信息系统和framework技术,构建了开放的土壤空间决策支持平台,形成了多目标土壤决策应用的解决方案和敏捷开发模式。     

Process-centered knowledge model and enterprise ontology for the development of knowledge management system

Among many enterprise assets, knowledge is treated as a critical driving force for attaining enterprise performance goals. This is because knowledge facilitates the better business decision makings in a timely fashion. However, since knowledge is created and utilized during the execution of business processes, if knowledge is separated from the business process context, it does not lead to the ability to take the right action for target performance.This paper proposes the framework for process-centered knowledge model and enterprise ontology for the context-rich and networked knowledge storage and retrieval required during task execution. The enterprise knowledge object for a process-centered knowledge model is classified into two types: process knowledge and task support knowledge. In the proposed enterprise ontology, which represents major enterprise concepts, and the relationships between them, all domain concepts are related to the “process” concept, both directly and indirectly. As a result, networked and sophisticated knowledge, rather than single-level knowledge, is provided to the participant of unit activity.In order to show the applicability of the proposed framework, a process-centered KMS (knowledge management system) was also developed, which is classified into 3 parts: (1) project management sub-system based on process knowledge. (2) Knowledge management sub-system for maintaining task support knowledge. (3) Infrastructure sub-system which supports the above two sub-systems.     

The Carnot Heterogeneous Database Project: Implemented Applications

The Carnot project was an ambitious research project inheterogeneous databases. It integrated a variety of techniques toaddress a wide range of problems in achieving interoperation inheterogeneous environments. Here we describe some of the majorimplemented applications of this project. These applications concern(a) accessing a legacy scientific database, (b) automating a workflowinvolving legacy systems, (c) cleaning data, and (d) retrievingsemantically appropriate information from structured databases inresponse to text queries. These applications support scientificdecision support, business process management, data integrityenhancement, and analytical decision support, respectively. Theydemonstrate Carnot‘s capabilities for (a) heterogeneous queryprocessing, (b) relaxed transaction and workflow management, (c)knowledge discovery, and (d) heterogeneous resource modelintegration.     

MULTI-AGENT SYSTEMS FOR DECISION SUPPORT: A CASE STUDY IN THE TRANSPORTATION MANAGEMENT DOMAIN

This article describes how agent and knowledge technology can be used to build advanced software systems that support operational decision making in complex domains. In particular, we present an abstract architecture and design guidelines for agent-based decision support systems. We illustrate our approach with a case study in the transportation management domain.     

Decision making with a fuzzy ontology

Knowledge mobilisation is a transition from the prevailing knowledge management technology that has been widely used in industry for the last 20?years to a new methodology and some innovative methods for knowledge representation, formation and development and for knowledge retrieval and distribution. Knowledge mobilisation aims at coming to terms with some of the problems of knowledge management and at the same time to introduce new theory, new methods and new technology. More precisely, this paper presents an outline of a fuzzy ontology as an enhanced version of classical ontology and demonstrates some advantages for practical decision making. We show that a number of soft computing techniques, e.g. aggregation functions and interval valued fuzzy numbers, will support effective and practical decision making on the basis of the fuzzy ontology. We demonstrate the knowledge mobilisation methods with the construction of a support system for finding the best available wine for a number of wine drinking occasions using a fuzzy wine ontology and fuzzy reasoning methods; the support system has been implemented for a Nokia N900 smart phone.     

生产计划问题的决策模型研究

生产决策问题一直以来都是一个重要的决策问题。它涉及到营销策略、市场预测、风险分析,以及管理经验等方面的知识。文章结合数据仓库和联机分析技术,提出了一种生产计划问题的决策模型,以求提供一个生产计划问题的决策支持解决方案。文中首先分析了生产计划问题的决策模式,在此基础上提出了生产计划问题的决策模型,并讨论了模型的建立方法,以及求解模型过程中所涉及的算法等问题。     

A soft knowledge modeling approach for geographically dispersed financial organizations

Fund management is a major function for most financial sector enterprises with geographically dispersed activities. The risk associated to such managerial decisions affects directly the continuity, profitability and reputation of the enterprise. This paper presents a knowledge modeling methodology tool to act as a decision support mechanism for geographically dispersed financial enterprises. The underlying research addresses the problem of financial information capture and representation by utilizing the soft computing characteristics of Fuzzy Cognitive Maps (FCMs). By using FCMs, the proposed mechanism generates a hierarchical and dynamic network of interconnected financial knowledge concepts, simulates the operational efficiency of distributed organizational models with imprecise relationships and quantifies the impact of geographically dispersed activities to the overall business performance. Generic adaptive maps offer an alternative approach to financial management based on strategic level knowledge modeling and integrate hierarchical FCMs into the decision making model of typical financial sector enterprises. Finally, this paper discusses experiments with the proposed mechanism and comments on its usability.     

Knowledge management component in managing human resources for enterprises

Both human resources and knowledge are valuable assets for enterprises; therefore, effective management of these assets becomes inevitably critical for business success. While human resource management systems have been a research topic for more than two decades, knowledge management systems applied to human resource management are relatively new to both academia and industry. This study examines the use of knowledge management in a business environment such as human resource management. The evolution of information systems and information processing in the human resource management domain is presented, and a knowledge-based decision support system for human resource management is proposed.     

Das BONITAS-System

Tool support for professional credit-rating is currently dominated by data-oriented approaches. This results in primarily focusing on hard facts in the financial institutions’ process of evaluating credit inquiries of their customers. Content objects and arguments which cannot algorithmically be mapped onto rigid data-tables (?soft facts”), however, with the potential of having a decisive impact on credit assessment, tend to be not sufficiently and appropriately placed into consideration. The BONITAS system overcomes this structural lack of information processing in traditional credit management systems in adding to the traditional approach innovative groupware-enabled capabilities. Compound documents in a messaging context are used to be integrative containers for all relevant information. These documents support both information gathering and evaluation of ?hard” data and ?soft” facts as well as semi-structured information gathering workflows allowing for a continuum of assessment-specific process options. The creation of valid and marketable business knowledge in credit management processes is basically team-driven. Thus, the essential features of groupware-based information management systems, i.e. their outstanding capabilities in communication, collaboration and coordination, represent critical baselines for improving the decision quality in credit-rating. An important goal of the BONITAS system is to aim at creating a constantly improved knowledge base on credit management workflows. This knowledge base is continuously being maintained in the operational progress of customer projects. It enables the financial institution to judge on the financial strength of upcoming clients in a holistic and anticipating evaluation process superior to traditional much more static assessments.     

基于知识的城市交通拥挤疏导决策支持系统的构造

本文首先分析了当发生交通拥挤时，城市道路 交通状况的特点，表明建立智能化城市交通拥挤疏导决策支持系统的重要性．在此基础上， 提出了系统的设计思想，系统结构、功能及其实现方法．该系统融合了丰富的交通拥挤管理 知识，采用定性推理和定量计算相结合的工作方式进行决策问题的求解，最终实现一个基于 知识的决策系统，并且遵照本文提出的系统构造，在苏南某城市开发的实际系统已投入运行 ，实践表明系统构造是有效的．