工程数据库系统中的知识管理

本文提出了工程数据库系统中的知识管理的概念，将建立在符号对象模型基础之上的知识分为三类：工程策略的管理，存取控制和规划的管理，本文着重讨论了复杂对象推理的特点，建立了从符号对象标识符，型继承和语义关系等概念，支持集合和局部不一致数据的处理，能更好地满足工程应用的要求。     

面向对象数据库中对象的存储和操作算法

面向对象数据库管理系统的核心是把现实世界的描述为对象，数据库的存储，操作和管理都以对象为依据。对象可以是简单的，也可以是复杂的。复杂对象中引用了其它的对象。结合我们开发的面向对象的工程数据库管理系统论述了对复杂对象的物理存储，内存映象方法和对象操作方法的实现技术。     

Extended object model for product configuration design

This paper presents an extended object model for case-based reasoning (CBR) in product configuration design. In the extended object model, a few methods of knowledge expression are adopted, such as constraints, rules, objects, etc. On the basis of extended object model, case representation model for CBR is applied to product configuration design system. The product configuration knowledge can be represented by the extended object. The model can support all the processes of CBR in product configuration design, such as case representation, indexing, retrieving, and case revising. The presented model is an extension of the traditional object-oriented model by including the relationship class used to express the relation between the cases, constraints class used in the product configuration knowledge representation, index class used in case retrieving, and solution class used in case revising. Therefore, the product configuration knowledge used in the product configuration design can be represented by using this model. In the end, a metering pump product configuration design system is developed on the basis of the proposed product configuration model to support customized products.     

Performance analysis of parallel object-oriented query processing algorithms

Advanced application domains such as computer-aided design, computer-aided software engineering, and office automation are characterized by their need to store, retrieve, and manage large quantities of data having complex structures. A number of object-oriented database management systems (OODBMS) are currently available that can effectively capture and process the complex data. The existing implementations of OODBMS outperform relational systems by maintaining and querying cross-references among related objects. However, the existing OODBMS still do not meet the efficiency requirements of advanced applications that require the execution of complex queries involving the retrieval of a large number of data objects and relationships among them. Parallel execution can significantly improve the performance of complex OO queries. In this paper, we analyze the performance of parallel OO query processing algorithms for various benchmark application domains. The application domains are characterized by specific mixes of queries of different semantic complexities. The performance of the application domains has been analyzed for various system and data parameters by running parallel programs on a 32-node transputer based parallel machine developed at the IBM Research Center at Yorktown Heights. The parallel processing algorithms, data routing techniques, and query management and control strategies have been implemented to obtain accurate estimation of controlling and processing overheads. However, generation of large complex databases for the study was impractical. Hence, the data used in the simulation have been parameterized. The parallel OO query processing algorithms analyzed in this study are based on a query graph approach rather than the traditional query tree approach. Using the query graph approach, a query is processed by simultaneously initiating the execution at several object classes, thereby, improving the parallelism. During processing, the algorithms avoid the execution of time-consuming join operations by making use of the object references among the objects. Further, the algorithms do not generate any temporary data, thereby, reducing disk accesses. This is accomplished by marking the selected objects and by employing a two-phase query processing strategy.     

A transaction management framework for collaborative engineering

This paper discusses the various novel functionalities of a transaction management system for collaborative engineering applications. These features include grouped and shared transactions, dynamic database partitioning, communicative locking, dependency maintenance, update notification schemes, and version management of data configurations. It also establishes protocols for data consistency maintenance on the basis of application semantics and encapsulation of nonserializable data sharing in local databases rather than by the notion of global database consistency. Finally, it presents strategies for implementing these features using a commercial object oriented database management system (OODBMS) environment (ONTOS).     

面向对象数据库管理系统的对象管理系统的研究

数据库是实现CAD/CAM系统集成的主要方法和工具。当前,面向对象数据库管理系统已成为数据库管理系统研究的主要方向,而对象管理是实现面向对象数据库管理系统的关键技术。本文结合华中理工大学CAD中心开发的面向对象数据库管理系统GHEODB的对象管理系统GHOMS,研究了对象管理系统的结构组成并给出了相关算法和加速机制     

PROTOTYPICAL CASES FOR RETRIEVAL, REUSE, AND KNOWLEDGE MAINTENANCE IN BIOMEDICAL CASE-BASED REASONING

Representing biomedical knowledge is an essential task in biomedical informatics intelligent systems. Case-based reasoning (CBR) holds the promise to represent contextual knowledge in a way that was not possible before with traditional knowledge-based methods. One main issue in biomedical CBR is dealing with the rate of generation of new knowledge in biomedical fields, which often makes the content of a case base partially obsolete. This article proposes to make use of the concept of prototypical case to ensure that a CBR system would keep update with current research advances in the biomedical field. Prototypical cases have served various purposes in biomedical CBR systems, among which to organize and structure the memory, to guide the retrieval as well as the reuse of cases, and to serve as bootstrapping a CBR system memory when real cases are not available in sufficient quantity and/or quality. This paper emphasizes the different roles prototypical cases can play in CBR systems, and presents knowledge maintenance as a very important novel role for these prototypical cases.     

An object-oriented VLSI CAD framework

It is shown, in the context of an actual prototype VLSI CAD framework called Cbase, how concepts such as data abstraction, property and operation inheritance, object specialization/generalization, code reusability, and polymorphism offered by object-oriented database management systems (OODBMSs) support rapid prototyping. The work was conducted on Vbase, a commercially available OODBMS, but the results and conclusions apply to any database with comparable capabilities. To implement the prototype user interface, the X11 toolkit, a windowing package that is also built around the object paradigm, was used     

ActiveCBR: An Agent System That Integrates Case-Based Reasoning and Active Databases

Case-based reasoning (CBR) is an artificial intelligence (AI) technique for problem solving that uses previous similar examples to solve a current problem. Despite its success, most current CBR systems are passive: they require human users to activate them manually and to provide information about the incoming problem explicitly. In this paper, we present an integrated agent system that integrates CBR systems with an active database system. Active databases, with the support of active rules, can perform event detection, condition monitoring, and event handling (action execution) in an automatic manner. The integrated ActiveCBR system consists of two layers. In the lower layer, the active database is rule-driven; in the higher layer, the result of action execution of active rules is transformed into feature–value pairs required by the CBR subsystem. The layered architecture separates CBR from sophisticated rule-based reasoning, and improves the traditional passive CBR system with the active property. The system has both real-time response and is highly exible in knowledge management as well as autonomously in response to events that a passive CBR system cannot handle. We demonstrate the system efficiency and effectiveness through empirical tests. Received 21 April 2000 / Revised 12 June 2000 / Accepted in revised form 14 July 2000     

Towards Automated Case Knowledge Discovery in the M 2 Case-Based Reasoning System

In this paper we present the M ² Case-Based Reasoning (CBR) system. The M ² system addresses a number of issues that present methodologies for CBR systems have shied away from. We discuss techniques for removing the knowledge acquisition bottleneck when acquiring case knowledge. Here, case knowledge refers to the complementary knowledge structures, cases (more specific in nature) and adaptation rules (more general). We address the use of negative cases for updating the case knowledge as well as for refining the similarity measures. In particular we discuss in detail, showing experimental results, the use of Data Mining within the M ² system to build the case base from a database containing operational data, and discover adaptation rules. A methodology to monitor the competence of the CBR system and to utilize negative cases for updating the CBR system to enhance its competence is also discussed. The M ² CBR system also employs Rough Set and Fuzzy Set theories to further enhance its capabilities within real-world applications as well as providing a richer and truer model of human reasoning.     

Knowledge engineering issues in developing a case-based reasoning application

Case-Based Reasoning (CBR) has emerged from research in cognitive psychology as a model of human memory and remembering. It has been embraced by researchers of AI applications as a methodology that avoids some of the knowledge acquisition and reasoning problems that occur with other methods for developing knowledge-based systems. In this paper we propose that, in developing knowledge based systems, knowledge engineering addresses two tasks. There is a problem analysis task that produces the problem representation and there is the task of developing the inference mechanism. CBR has an impact on the second of these tasks but helps less with the first. We argue that in some domains this problem analysis process can be significant and propose an iterative methodology for addressing it. To evaluate this, we describe the application of case-based reasoning to the problem of aircraft conflict resolution in a system called ISAC. We describe the application of this iterative methodology and assess the knowledge engineering impact of CBR.     

CASE-BASED REASONING AND KNOWLEDGE DISCOVERY IN MEDICAL APPLICATIONS WITH TIME SERIES

This paper discusses the role and integration of knowledge discovery (KD) in case-based reasoning (CBR) systems. The general view is that KD is complementary to the task of knowledge retaining and it can be treated as a separate process outside the traditional CBR cycle. Unlike knowledge retaining that is mostly related to case-specific experience, KD aims at the elicitation of new knowledge that is more general and valuable for improving the different CBR substeps. KD for CBR is exemplified by a real application scenario in medicine in which time series of patterns are to be analyzed and classified. As single pattern cannot convey sufficient information in the application, sequences of patterns are more adequate. Hence it is advantageous if sequences of patterns and their co-occurrence with categories can be discovered. Evaluation with cases containing series classified into a number of categories and injected with indicator sequences shows that the approach is able to identify these key sequences. In a clinical applica-tion and a case library that is representative of the real world, these key sequences would improve the classification ability and may spawn clinical research to explain the co-occurrence between certain sequences and classes.     

Autonomic self healing and recovery informed by environment knowledge

An important goal of autonomic computing is the development of computing systems that are capable of self healing with a minimum of human intervention. Typically, recovery from even a simple fault will require knowledge of the environment in which a computing system operates. To meet this need, we present an approach to self healing and recovery informed by environment knowledge that combines case based reasoning (CBR) and rule based reasoning. Specifically, CBR is used for fault diagnosis and rule based reasoning for fault remediation, recovery, and referral. We also show how automated information gathering from available sources in a computing system’s environment can increase problem solving efficiency and help to reduce the occurrence of service failures. Finally, we demonstrate the approach in an intelligent system for fault management in a local printer network.     

Unifying data,behaviours, and messages in object-oriented databases

This paper introduces the Knowhow-Bearing Objects (KBO) model. KBO objects have both a data and a behavioural side. This allows us to treat behaviours as objects, so that a database approach can be taken to manage these objects. In particular, we are able to model different kinds of behaviour invocation and to use a database query language to manipulate and query persistent behaviour collections. Examples of complex query construction using an object algebra are given.     

可重构知识管理系统中案例与规则的集成推理方法

传统上的知识管理工具往往在设计时就固定了知识结构,这样的系统不但缺乏通用性,而且也限制了知识检索与推理的效率。介绍一个基于本体的可重构知识管理系统,知识作为本体概念的对象实例,利用本体模型的可定制性,解决了以往知识类型不能扩展的问题。详细阐述了结合案例推理与规则推理的集成推理方法,通过规则学习算法支持了规则库的动态扩充与调整,并将本体类的语义关系应用于推理方法,进一步提高了推理的效率。最后介绍了该系统在某飞机设计研究院的应用情况和今后的研究方向。     

一种CBR与RBR相结合的快速预案生成系统

将范例推理(case based reasoning,CBR)与规则推理(rule based reasoning,RBR)两种人工智能技术相结合,实现一种快速预案生成系统.它有效地解决了单纯RBR系统在预案生成过程中的时间延迟缺陷和知识库难以获取的瓶颈.通过CBR工具,能够把以前发生的紧急事件和解决方案生成预案.一旦新的事件发生,首先从预案库中进行案例的相似性检索,如果没有检索到预案或者检索到的预案匹配度很低,再采用RBR系统对紧急事件进行规则推理,然后把推理结果重新存入预案库.实验数据表明,这种方法对单纯RBR系统在时间响应上进行了有效的优化.另外,因为案例的获取比专家系统推理规则的获取容易得多,它同时解决了RBR系统推理规则难以获取的瓶颈.根据这种思想,实现了CBR与RBR结合的快速预案生成系统.目前,它已经应用到抗洪抢险的预案生成和城市应急联动的决策支持上,效果表明它在预案生成速度以及实际可操作性上都具有明显优势.