Knowledge discovery in molecular databases

An approach to knowledge discovery in complex molecular databases is described. The machine learning paradigm used is structured concept formation, in which object's described in terms of components and their interrelationships are clustered and organized in a knowledge base. Symbolic images are used to represent classes of structured objects. A discovered molecular knowledge base is successfully used in the interpretation of a high resolution electron density map     

Knowledge discovery workbench for exploring business databases

We describe the Knowledge Discovery Workbench, an interactive system for database exploration. We then illustrate KDW capabilities in data clustering, summarization, classification, and discovery of changes. We also examine extracting dependencies from data and using them to order the multitude of data patterns. © 1992 John Wiley & Sons, Inc.     

Knowledge discovery in databases using lattices

The rapid pace at which data gathering, storage and distribution technologies are developing is outpacing our advances in techniques for helping humans to analyse, understand, and digest the vast amounts of resulting data. This has led to the birth of knowledge discovery in databases (KDD) and data mining—a process that has the goal to selectively extract knowledge from data. A range of techniques, including neural networks, rule-based systems, case-based reasoning, machine learning, statistics, etc. can be applied to the problem. We discuss the use of concept lattices, to determine dependences in the data mining process. We first define concept lattices, after which we show how they represent knowledge and how they are formed from raw data. Finally, we show how the lattice-based technique addresses different processes in KDD, especially visualization and navigation of discovered knowledge.     

Knowledge discovery in time series databases

Adding the dimension of time to databases produces time series databases (TSDB) and introduces new aspects and difficulties to data mining and knowledge discovery. In this correspondence, we introduce a general methodology for knowledge discovery in TSDB. The process of knowledge discovery in TSDR includes cleaning and filtering of time series data, identifying the most important predicting attributes, and extracting a set of association rules that can be used to predict the time series behavior in the future. Our method is based on signal processing techniques and the information-theoretic fuzzy approach to knowledge discovery. The computational theory of perception (CTP) is used to reduce the set of extracted rules by fuzzification and aggregation. We demonstrate our approach on two types of time series: stock-market data and weather data.     

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.     

Knowledge discovery in distributed databases using evidence theory

Distributed databases allow us to integrate data from different sources which have not previously been combined. The Dempster–Shafer theory of evidence and evidential reasoning are particularly suited to the integration of distributed databases. Evidential functions are suited to represent evidence from different sources. Evidential reasoning is carried out by the well‐known orthogonal sum. Previous work has defined linguistic summaries to discover knowledge by using fuzzy set theory and using evidence theory to define summaries. In this paper we study linguistic summaries and their applications to knowledge discovery in distributed databases. © 2000 John Wiley & Sons, Inc.     

A general formulation of structural topology optimization for maximizing structural stiffness

This paper presents a general formulation of structural topology optimization for maximizing structure stiffness with mixed boundary conditions, i.e. with both external forces and prescribed non-zero displacement. In such formulation, the objective function is equal to work done by the given external forces minus work done by the reaction forces on prescribed non-zero displacement. When only one type of boundary condition is specified, it degenerates to the formulation of minimum structural compliance design (with external force) and maximum structural strain energy design (with prescribed non-zero displacement). However, regardless of boundary condition types, the sensitivity of such objective function with respect to artificial element density is always proportional to the negative of average strain energy density. We show that this formulation provides optimum design for both discrete and continuum structures.     

Systems for knowledge discovery in databases

Knowledge-discovery systems face challenging problems from real-world databases, which tend to be dynamic, incomplete, redundant, noisy, sparse, and very large. These problems are addressed and some techniques for handling them are described. A model of an idealized knowledge-discovery system is presented as a reference for studying and designing new systems. This model is used in the comparison of three systems: CoverStory, EXPLORA, and the Knowledge Discovery Workbench. The deficiencies of existing systems relative to the model reveal several open problems for future research     

Knowledge discovery in deductive databases with large deductionresults: the first step

Deductive databases have the ability to deduce new facts from a set of existing facts by using a set of rules. They are also useful in the integration of artificial intelligence and databases. However, when recursive rules are involved, the number of deduced facts can become too large to be practically stored, viewed or analyzed. This seriously hinders the usefulness of deductive databases. In order to overcome this problem, we propose four methods to discover characteristic rules from a large number of deduction results without actually having to store all the deduction results. This paper presents the first step in the application of knowledge discovery techniques to deductive databases with large numbers of deduction results     

Abstract-driven pattern discovery in databases

The problem of discovering interesting patterns in large volumes of data is studied. Patterns can be expressed not only in terms of the database schema but also in user-defined terms, such as relational views and classification hierarchies. The user-defined terminology is stored in a data dictionary that maps it into the language of the database schema. A pattern is defined as a deductive rule expressed in user-defined terms that has a degree of uncertainty associated with it. Methods are presented for discovering interesting patterns based on abstracts which are summaries of the data expressed in the language of the user     

Revisable knowledge discovery in databases

This article introduces the idea of using nonmonotonic inheritance networks for the storage and maintenance of knowledge discovered in data (revisable knowledge discovery in databases). While existing data mining strategies for knowledge discovery in databases typically involve initial structuring through the use of identification trees and the subsequent extraction of rules from these trees for use in rule-based expert systems, such strategies have difficulty in coping with additional information which may conflict with that already used for the automatic generation of rules. In the worst case, the entire automatic sequence may have to be repeated. If nonmonotonic inheritance networks are used instead of rules for storing knowledge discovered in databases, additional conflicting information can be inserted directly into such structures, thereby bypassing the need for recompilation. © 1996 John Wiley & Sons, Inc.     

A graphical user-interface for knowledge discovery in databases

This paper describes a graphical user-interface for database-oriented knowledge discovery systems, DBLEARN, which has been developed for extracting knowledge rules from relational databases. The interface, designed using a query-by-example approach, provides a graphical means of specifying knowledge-discovery tasks. The interface supplies a graphical browsing facility to help users to perceive the nature of the target database structure. In order to guide users' task specification, a cooperative, menu-based guidance facility has been integrated into the interface. The interface also supplies a graphical interactive adjusting facility for helping users to refine the task specification to improve the quality of learned knowledge rules.     

一种改进的网络拓扑发现方法

在对基于ICMP的网络拓扑发现、基于ARP的网络拓扑发现和利用SNMP访问MIB(管 理信息库)路由表的网络拓扑发现三种方法的分析研究基础上,提出了一个经过改进的网络拓扑发 现方法,此方法能够准确、完整、高效地发现网络主干拓扑和子网内的设备,并详细描述了网络拓扑发 现的数据结构和算法。     

A virtual mart for knowledge discovery in databases

The Web has profoundly reshaped our vision of information management and processing, enlightening the power of a collaborative model of information production and consumption. This new vision influences the Knowledge Discovery in Databases domain as well. In this paper we propose a service-oriented, semantic-supported approach to the development of a platform for sharing and reuse of resources (data processing and mining techniques), enabling the management of different implementations of the same technique and characterized by a community-centered attitude, with functionalities for both resource production and consumption, facilitating end-users with different skills as well as resource providers with different technical and domain specific capabilities. We first describe the semantic framework underlying the approach, then we demonstrate how this framework is exploited to give different functionalities to users through the presentation of the platform functionalities.     

Developing topology discovery in Event-B

We present a formal development in Event-B of a distributed topology discovery algorithm. Distributed topology discovery is at the core of several routing algorithms and is the problem of each node in a network discovering and maintaining information on the network topology. One of the key challenges in developing this algorithm is specifying the problem itself. We provide a specification that includes both safety properties, formalizing invariants that should hold in all system states, and liveness properties that characterize when the system reaches stable states. We prove these properties by appropriately combining proofs of invariants, event refinement, event convergence, and deadlock freedom. The combination of these features is novel and should be useful for formalizing and developing other kinds of semi-reactive systems, which are systems that react to, but do not modify, their environment. Our entire development has been formalized and machine checked using the Rodin tool.     

数据库中的模糊逻辑规则发现

关联规则是数据库中的知识发现（KDD）领域的重要研究课题。模糊关联规则可以用自然语言来表达人类知识,近年来受到KDD研究人员的普遍关注。但是,目前大多数模糊关联规则发现方法仍然沿用经典关联规则发现中常用的支持度和置信度测度。事实上,模糊关联规则可以有不同的解释,而且不同的解释对规则发现方法有很大影响。从逻辑的观点出发,定义了模糊逻辑规则、支持度、蕴含度及其相关概念,提出了模糊逻辑规则发现算法,该算法结合了模糊逻辑概念和Apriori算法,从给定的定量数据库中发现模糊逻辑规则。     

Benchmarking optimization solvers for structural topology optimization

The purpose of this article is to benchmark different optimization solvers when applied to various finite element based structural topology optimization problems. An extensive and representative library of minimum compliance, minimum volume, and mechanism design problem instances for different sizes is developed for this benchmarking. The problems are based on a material interpolation scheme combined with a density filter. Different optimization solvers including Optimality Criteria (OC), the Method of Moving Asymptotes (MMA) and its globally convergent version GCMMA, the interior point solvers in IPOPT and FMINCON, and the sequential quadratic programming method in SNOPT, are benchmarked on the library using performance profiles. Whenever possible the methods are applied to both the nested and the Simultaneous Analysis and Design (SAND) formulations of the problem. The performance profiles conclude that general solvers are as efficient and reliable as classical structural topology optimization solvers. Moreover, the use of the exact Hessians in SAND formulations, generally produce designs with better objective function values. However, with the benchmarked implementations solving SAND formulations consumes more computational time than solving the corresponding nested formulations.     

A framework for knowledge discovery and evolution in databases

A concept for knowledge discovery and evolution in databases is described. The key issues include: using a database query to discover new rules; using not only positive examples (answer to a query), but also negative examples to discover new rules; and harmonizing existing rules with the new rules. A tool for characterizing the exceptions in databases and evolving knowledge as a database evolves is developed     

数据链路层拓扑发现算法的研究

阐述了网络层拓扑发现与数据链路层拓扑发现之间的区别,分析了数据链路层拓扑发现的研究现状和存在的不足。为改进这一不足,在总结子网内部的直接连接定理和间接连接定理的基础上,提出了一种新的数据链路层拓扑发现算法,并结合该算法,利用树的后序遍历算法作为拓扑图形的显示方法,开发了具有数据链路层拓扑发现功能的网络拓扑系统。