Mining for knowledge in databases: The INLEN architecture,initial implementation and first results

The architecture of an intelligent multistrategy assistant for knowledge discovery from facts, INLEN, is described and illustrated by an exploratory application. INLEN integrates a database, a knowledge base, and machine learning methods within a uniform user-oriented framework. A variety of machine learning programs are incorporated into the system to serve as high-levelknowledge generation operators (KGOs). These operators can generate diverse kinds of knowledge about the properties and regularities existing in the data. For example, they can hypothesize general rules from facts, optimize the rules according to problem-dependent criteria, determine differences and similarities among groups of facts, propose new variables, create conceptual classifications, determine equations governing numeric variables and the conditions under which the equations apply, deriving statistical properties and using them for qualitative evaluations, etc. The initial implementation of the system, INLEN 1b, is described, and its performance is illustrated by applying it to a database of scientific publications.     

Decomposition of information systems for production management

This paper approaches the issue of decentralization and decomposition of information systems from two angles, viz. from an organizational and from an infological point of view. Current information systems tend to become more and more integrated. However, this integration causes organizational complexity, which, in turn, becomes prohibitive for organizational change. Thus, there is a need for decomposition of the information system from an organizational point of view. A strategy for such a decomposition in a production environment is given.     

Knowledge management for consumer-focused product design

As the automotive industry adopts a consumer focus in its product development strategy, it offers broader product ranges, shorter model lifetimes and the ability to process orders in arbitrary lot sizes. This offers the ability to conduct early product design and development trade-off analysis among these competing objectives. A distributed knowledge-based system, which analyzes, verifies, stores, and retrieves process definitions, is needed to manage the complexity of workflows. The use of information technologies and networking capabilities is essential in the dissemination of product knowledge in order to integrate the decision-making process among heterogeneous and distributed partners/units. This paper offers insights into a knowledge management approach that enables implementing a consumer-focused product design philosophy by integrating capabilities for intelligent information support and group decision-making utilizing a common enterprise network model and knowledge interface through shared ontologies. An automotive supply chain case study is utilized in illustrating the proposed approach.     

Knowledge management by example

Knowledge has many components such as data, constraints, queries, transactions, and derivation rules. Data is the only component that can be managed effectively in large quantities. All other components are in their infancy in terms of tools and techniques for efficient storage and retrieval, implementation and execution, and user specification and design. One approach to manage all components of knowledge in large quantities is to reduce them all to data. Many components of knowledge can be expressed in terms of examples, and examples are data. As such, all these components can be stored and retrieved efficiently in large quantities, their execution reduces to data comparison and can be done in parallel, and they can be specified, designed, and modified by end users since examples are more intuitive and easy to manipulate than general procedures.     

Using single-layered neural networks for the extraction of conjunctive rules and hierarchical classifications

Machine Learning is an area concerned with the automation of the process of knowledge acquisition. Neural networks generally represent their knowledge at the lower level, while knowledge based systems use higher level knowledge representations. The method we propose here, provides a technique which automatically allows us to extract production rules from the lower level representation used by a single-layered neural networks trained by Hebb's rule. Even though a single-layered neural network can not model complex, nonlinear domains, their strength in dealing with noise has enabled us to produce correct rules in a noisy domain.     

Database management and analysis tools of machine induction

This paper surveys machine induction techniques for database management and analysis. Our premise is that machine induction facilitates an evolution from relatively unstructured data stores to efficient and correct database implementations.     

基于模拟退火算法的知识获取方法的研究

从优化角度提出了从事例中获取知识的机器学习方法。该方法利用模拟退火算法,按照预定的优化目标,从事例中生成最优的产生式规则,给出其算法,并以旋转机械故障诊断知识获取为例,阐述了基于模拟退火算法的知识获取机制及其实现方法。     

Imputation of Missing Data in Industrial Databases

A limiting factor for the application of IDA methods in many domains is the incompleteness of data repositories. Many records have fields that are not filled in, especially, when data entry is manual. In addition, a significant fraction of the entries can be erroneous and there may be no alternative but to discard these records. But every cell in a database is not an independent datum. Statistical relationships will constrain and, often determine, missing values. Data imputation, the filling in of missing values for partially missing data, can thus be an invaluable first step in many IDA projects. New imputation methods that can handle the large-scale problems and large-scale sparsity of industrial databases are needed. To illustrate the incomplete database problem, we analyze one database with instrumentation maintenance and test records for an industrial process. Despite regulatory requirements for process data collection, this database is less than 50% complete. Next, we discuss possible solutions to the missing data problem. Several approaches to imputation are noted and classified into two categories: data-driven and model-based. We then describe two machine-learning-based approaches that we have worked with. These build upon well-known algorithms: AutoClass and C4.5. Several experiments are designed, all using the maintenance database as a common test-bed but with various data splits and algorithmic variations. Results are generally positive with up to 80% accuracies of imputation. We conclude the paper by outlining some considerations in selecting imputation methods, and by discussing applications of data imputation for intelligent data analysis.     

中小型软件企业知识管理的研究

越来越多的我国中小型软件企业认识到员工的知识是企业最有价值的资产,而且在企业中实施知识管理是十分必要的。首先概括了这些企业在知识管理方面的现状,提出了一个适用于中小型软件企业的知识管理体系,说明了知识管理与CMM／CMMI的关系,并给出了知识管理与软件过程管理的结合模型,介绍了一个软件企业知识管理系统的模型框架,分享了实施知识管理的一些经验。     

极小极大规则学习及在决策树规则简化中的应用

文中在粗糙集理论中的约简概念的启发下提出极小规则和极大规则的概念及极小极大规则学习。     

Knowledge discovery from industrial databases

The proliferation of large masses of data has created many new opportunities for those working in science, engineering and business. The field of data mining (DM) and knowledge discovery from databases (KDD) has emerged as a new discipline in engineering and computer science. In the modern sense of DM and KDD the focus tends to be on extracting information characterized as knowledge from data that can be very complex and in large quantities. Industrial engineering, with the diverse areas it comprises, presents unique opportunities for the application of DM and KDD, and for the development of new concepts and techniques in this field. Many industrial processes are now automated and computerized in order to ensure the quality of production and to minimize production costs. A computerized process records large masses of data during its functioning. This real-time data which is recorded to ensure the ability to trace production steps can also be used to optimize the process itself. A French truck manufacturer decided to exploit the data sets of measures recorded during the test of diesel engines manufactured on their production lines. The goal was to discover knowledge in the data of the test engine process in order to significantly reduce (by about 25%) the processing time. This paper presents the study of knowledge discovery utilizing the KDD method. All the steps of the method have been used and two additional steps have been needed. The study allowed us to develop two systems: the discovery application is implemented giving a real-time prediction model (with a real reduction of 28%) and the discovery support environment now allows those who are not experts in statistics to extract their own knowledge for other processes.     

农业智能系统的知识获取技术研究

作者及其团队长期针对农业领域的知识获取技术进行了系列性研究.阐述了运用智能引导、机器学习、数据挖掘、智能计算等技术的人工和自动/半自动的知识获取方法.这些方法能够有效地获取领域知识,发现隐含模式,进行知识精化.研发了知识获取工具.这些方法和工具反映了知识获取技术对农业信息工程所起的重要作用.     

实现人工神经网络知识增殖能力的一种方法

具有知识增殖能力的神经学习系统是人工神经网络发展的一个重要方向,备受研究人员的关注．传统上对神经学习系统知识的增殖或重用研究偏重于对个体网络的改造,根据知识积累和继承的思想,引入自治神经网络(autonomous artificial neural network,AANN)的理念,以此作为构造知识可增殖神经学习系统的基础,利用群体网络的方法成功解决了神经学习系统的拓展和知识增殖问题．AANN和一般神经网络的区别在于其自治能力,采用AANN模块构造的神经学习系统,具有知识增殖能力,其可靠性、可拓展性和灵活性都得到提高．实验结果表明,该方法构造的群体网络系统可有效继承其模块所学习的知识．     

Knowledge Management in Societies of Intelligent Adaptive Agents

A model is developed of the emergence of the knowledge level in asociety of agents where agents model and manage other agents as resources,and manage the learning of other agents to develop such resources. It isargued that any persistent system that actively creates the conditions forits persistence is appropriately modeled in terms of the rationalteleological models that Newell defines as characterizing the knowledgelevel. The need to distribute tasks in agent societies motivates suchmodeling, and it is shown that if there is a rich order relationship ofdifficulty on tasks that is reasonably independent of agents then it isefficient to model agents competencies in terms of their possessingknowledge. It is shown that a simple training strategy of keeping an agent'sperformance constant by allocating tasks of increasing difficulty as anagent adapts optimizes the rate of learning and linearizes the otherwisesigmoidal learning curves. It is suggested that this provides a basis forassigning a granularity to knowledge that enables learning processes to bemanaged simply and efficiently.     

利用类比知识的基于解释的学习

本文介绍如何将示例中包含的新的知识通过基于解释的学习加入到原来不完善的领域知识库中去。整个学习过程是在示例的引导下依据领域理论和类比知识进行推理的纯演绎过程。因此，经过改进的领域理论可以保持其正确性。系统的原型在ＳＵＮ工作站上用ＱＵＩＮＴＵＳ ＰＲＯＬＯＧ实现。     

基于机器学习的服装款式知识库设计

基于机器学习的服装制造流程管理与控制系统的开发应用,可以摆脱长期以来服装制造流程的手工操作方式,缩短生产周期,达到智能化制衣的目的。而基于机器学习的服装智能化制造的实现又依赖于服装制造流程中各环节所对应的服装知识库信息的支撑。款式设计作为服装制造流程中不可缺少的一部分同样也依赖于款式知识库中信息的支撑。本文主要描述款式知识库的设计原理、设计方法和它的应用。     

远程生产管理系统在钢铁厂中的应用

远程生产管理系统可以实时采集、查看生产数据。通过此系统,用户可以在线对比、分析生产线数据,判断生产线状况。本系统能够将数据保存下来,为解决问题提供了数据,节约时间,提高工作效率。     

知识管理的支撑技术及实现框架模型

知识已成为企业重要的生产投入要素,知识管理能将企业战略、信息系统和企业核心能力知识有机结合起来,而成为新的研究热点。该文分析了信息管理与知识管理的关系,指出知识管理是信息管理的新阶段,阐述了不同知识管理环节的支撑技术和工具,提出了一种知识管理系统的分层框架模型,在保证与传统信息系统兼容的前提下简化了系统的实施过程,实现从信息管理向知识管理在技术上的平稳过渡。