基于参数比Petri网的主动数据库的行为研究

1 引言在主动数据库系统(Active Database System:简称ADS)中当特定的事件或某些条件成立时,ECA(Event-Condition-Action)便触发一些动作。形式上,一个ECA规则是由事件E、条件C和动作A组成的,事件可以是单个或组合的,条件可以是布尔表达式或产生True/False的数据库查询,动作可以是数据库操作的执行或应用程序的执行。尽管主动数据库     

主动数据仓库规则模型及其相关语法的研究

主动数据库的简单规则形式不符合主动数据仓库(ADW)的决策特点,而基于ADW的分析规则在交互性、精确性、表达能力和扩展性等方面存在不足.根据ADW规则渐进分析分布决策的特点,结合ECA规则形式,定义了一种主动多维分析规则模型及相应语法(AMAR)作为ADW规则的表述和执行方式.AMAR不仅可以应对ADW规则的诸多挑战,还提供了对立方体、事件、规则,结果之间复杂对应关系的支持,同时具备了更好的可维护性和可执行性.     

主动数据库中基于图的规则的指称语义研究

1 引言主动数据库的主动特性一般使用E-C-A(Event-Condi-tion-Action)规则模型描述。我们用CA规则表示ECA规则模型中的Condition和Action部分,ECA规则语言的语义可描述为:事件E的发生可触发CA规则的执行,该CA规则的执行又可导致其它事件的发生,进而触发其它CA规则的执行,形成触发规则集。主动规则源于人工智能的知识表示的产生式系统(OPS5),许多主动数据库规则系统的执行语义均源于OPS5产生式规则语言的recognize-act cycle算法(如图     

基于ECA 规则的仿真系统研究设计

分析冷连轧仿真系统的主动性需求，结合数据库在仿真系统中的作用，提出基于主动数据库的仿真系统设计。对主动规则的原理和执行进行分析，给出了基于主动规则——ECA规则的仿真系统的设计和运行机制，用ECA规则描述应用系统执行操作和事件处理，完成系统的主动性行为。并以冷连轧仿真系统中物料跟踪在一机架前的事件动作为例，运用ECA规则进行设计。     

一种基于ECA规则的Web Service工作流模型的研究

工作流技术的深入应用要求工作流管理系统增强Web服务功能,基于Petri网工作流过程模型存在无法表述状态变迁过程或状态变迁边缘时刻事件的缺陷.给出了基于ECA规则Web Service工作流模型形式化定义,基于ECA规则的过程模型以事件推动工作流实例的执行,通过严格定义事件的语义来保证工作流的正确执行和监控并支持工作流在运行中修改实例.     

直觉模糊粗糙推理的规则库完备性研究

针对规则库检验问题,提出了直觉模糊粗糙规则库的完备性检验方法.首先,引入直觉模糊粗糙集的概念,给出了直觉模糊粗糙规则库的完备性定义;其次,针对直觉模糊粗糙规则库的完备性,提出了完备性检验算法,指出如何设计规则库才能满足完备性要求;最后,通过实例验证了算法的合理性和有效性.     

基于主动数据库的移动P2P查询处理

提出了一个基于主动数据库的移动代理构造及管理系统模型。它采用主动数据库中的事件—条件—活动（ECA）规则来定义代理的逻辑结构并利用其触发器机制来实施代理的执行和管理，不仅大大简化了支持即时或连续查询的P2P系统的体系结构而且通过主动数据库的安全机制能确保从自治数据库中获取信息的安全性和有效性。给出了移动P2P系统中节点的体系结构，代理的结构、管理与生存周期并给出了一个应用实例。     

主动数据库技术在PRAFRIP系统中的应用

分析PRAFRIP(继电保护及故障信息分析处理)系统的主动性需求,结合数据库在继电保护故障信息分析处理系统中的作用,提出基于主动数据库的系统设计.对主动规则的原理和执行进行分析,给出基于主动规则一ECA的系统的设计和运行机制.研究表明,采用主动数据库技术的PRAFRIP系统具有较高的效率,并且数据库系统的性能大为提高.     

移动数据库中的事件及描述

在移动计算环境中,移动主机具有移动性,移动主机与无线网络的连接状态具有多样性.这些特性要求服务于移动主机的移动数据库系统作出反应和相应的处理.在建立一个主动数据库ECA(event condition action)规则系统模型的基础上,对各种移动事件进行定义,给出了事件的时态逻辑描述,为主动数据库的事件触发提供了新的事件空间,为移动数据库系统的主动处理提供了有效的支持.     

ECA规则中复合事件的CCPN建模与检测

事件的检测是主动数据库ECA规则执行中首要的一步,也是最重要的一步.采用条件着色PETRI网（CCPN）模型对ECA规则中的复合事件进行建模,进而通过检测复合变迁的使能状态和验证与该变迁相关联的条件,达到检测复合事件的目的.该方法既简化了模型结构的复杂性,又实现了模型的通用性.     

Improving Expressivity of Inductive Logic Programming by Learning Different Kinds of Fuzzy Rules

Introducing fuzzy predicates in inductive logic programming may serve two different purposes: allowing for more adaptability when learning classical rules or getting more expressivity by learning fuzzy rules. This latter concern is the topic of this paper. Indeed, introducing fuzzy predicates in the antecedent and in the consequent of rules may convey different non-classical meanings. The paper focuses on the learning of gradual and certainty rules, which have an increased expressive power and have no simple crisp counterpart. The benefit and the application domain of each kind of rules are discussed. Appropriate confidence degrees for each type of rules are introduced. These confidence degrees play a major role in the adaptation of the classical FOIL inductive logic programming algorithm to the induction of fuzzy rules for guiding the learning process. The method is illustrated on a benchmark example and a case-study database.     

从Fuzzy Taxonomic数值型数据库中挖掘一般化关联规则

挖掘关联规则是数据挖掘研究的一个重要方面．基于属性内通常还存在更高层次的抽象，即呈现出Taxonomic结构这一事实，Srikant和Agrawal等人提出了在确定的Taxonomic结构下挖掘泛化布尔型关联规则的挖掘算法．但在实际应用中，往往这种Taxonomic结构还呈现出模糊性；着重研究了在这种模糊Taxonomic结构下如何从数值型数据库中挖掘一般化关联规则的问题，提出了，一种新的Fuzzy Taxonomic数值型数据库模型，并提出了相应的规则发现方法，两个实例数据库表明了新模型的有效性和灵活性．     

Mining fuzzy temporal association rules by item lifespans

Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. In real-world applications, transactions may contain quantitative values and each item may have a lifespan from a temporal database. In this paper, we thus propose a data mining algorithm for deriving fuzzy temporal association rules. It first transforms each quantitative value into a fuzzy set using the given membership functions. Meanwhile, item lifespans are collected and recorded in a temporal information table through a transformation process. The algorithm then calculates the scalar cardinality of each linguistic term of each item. A mining process based on fuzzy counts and item lifespans is then performed to find fuzzy temporal association rules. Experiments are finally performed on two simulation datasets and the foodmart dataset to show the effectiveness and the efficiency of the proposed approach.     

模糊Horn子句规则挖掘算法研究

模糊关联规则可以用自然语言来表达人类知识,受到数据挖掘与知识发现研究人员的广泛关注。但是,目前大多数模糊关联规则挖掘方法仍然基于经典关联规则的支持度和可信度测度。从模糊蕴涵的观点出发,定义了模糊Horn子句规则、支持度、蕴涵强度以及相关概念,提出了模糊Horn子句规则挖掘算法。该算法可以分解为3个步骤。首先,将定量数据库转换为模糊数据库。其次,挖掘模糊数据库中所有支持度不小于指定最小支持度阂值的频繁项目集。一旦得到了所有频繁项目集,就可以用一种直接的方法生成所有蕴涵强度不小于指定最小蕴涵强度阂值的模糊Horn子句规则。     

A multi-level conceptual data reduction approach based on the Lukasiewicz implication

Starting from fuzzy binary data represented as tables in the fuzzy relational database, in this paper, we use fuzzy formal concept analysis to reduce the tables size to only keep the minimal rows in each table, without losing knowledge (i.e., association rules extracted from reduced databases are identical at given precision level). More specifically, we develop a fuzzy extension of a previously proposed algorithm for crisp data reduction without loss of knowledge. The fuzzy Galois connection based on the Lukasiewicz implication is mainly used in the definition of the closure operator according to a precision level, which makes data reduction sensitive to the variation of this precision level.     

Application of fuzzy logic and variable precision rough set approach in a remote monitoring manufacturing process for diagnosis rule induction

Rough set has been shown to be a valuable approach to mine rules from a remote monitoring manufacturing process. In this research, an application of the fuzzy set theory with the fuzzy variable precision rough set approach for mining the causal relationship rules from the database of a remote monitoring manufacturing process is presented. The membership function in the fuzzy set theory is used to transfer the data entries into fuzzy sets, and the fuzzy variable precision rough set approach is applied to extract rules from the fuzzy sets. It is found that the induced rules are identical to the practical knowledge and fault diagnosis thinking of human operators. The induced rules are then compared with the rules induced by the original rough set approach. The comparison shows that the rules induced by the fuzzy rough set are expressed in linguistic forms, and are evaluated by plausibility and future effectiveness measures. The fuzzy rough set approach, being less sensitive to noisy data, induces better rules than the original rough set approach.     

Fuzzy classifier design using genetic algorithms

A new method for design of a fuzzy-rule-based classifier using genetic algorithms (GAs) is discussed. The optimal parameters of the fuzzy classifier including fuzzy membership functions and the size and structure of fuzzy rules are extracted from the training data using GAs. This is done by introducing new representation schemes for fuzzy membership functions and fuzzy rules. An effectiveness measure for fuzzy rules is developed that allows for systematic addition or deletion of rules during the GA optimization process. A clustering method is utilized for generating new rules to be added when additions are required. The performance of the classifier is tested on two real-world databases (Iris and Wine) and a simulated Gaussian database. The results indicate that highly accurate classifiers could be designed with relatively few fuzzy rules. The performance is also compared to other fuzzy classifiers tested on the same databases.     

区间值关系数据库上模糊关联规则的预测方法

讨论了区间值关系数据库上模糊关联规则的挖掘算法与预测方法。采用一种比RFCM算法省时的FCMdd算法将记录在属性的取值划分成若干个模糊集,并提出区间值关系数据库上模糊关联规则的挖掘算法。仿真实例说明挖掘算法能够通过挖掘有意义的模糊关联规则来发现区间值关系数据库中蕴涵的关联性。区间值关系数据库上模糊关联规则的预测方法改进了标准可加性模型,并通过遗传算法调整模糊关联规则中三角模糊数的参数来提高预测的精度。     

A fuzzy inference system for fault detection and isolation: Application to a fluid system

This work focuses on the design and implementation of a fuzzy inference system for fault detection and isolation (FDI) which can learn from example fault data, and the determination of a suitable optimisation strategy for the membership functions. A FDI system was developed which is based on adaptive fuzzy rules. A number of optimisation strategies were then applied; it was found that an evolutionary algorithm not only produced the best results but did so with relatively little processing effort and with excellent consistency.The adaptive fuzzy system, thus optimised, was tested against a neural network, which was trained to produce analogue outputs as an indication of fault magnitude. The fuzzy solution produced the best accuracy.We can conclude that an adaptive fuzzy inference system for FDI, using an evolutionary algorithm to learn from examples, can provide an accurate and readily comprehensible solution to diagnosing and evaluating fluid process plant faults.     

Development of genetic algorithm-based fuzzy rules design for metal cutting data selection

Fuzzy rules optimization is always a problem for a complex fuzzy model. For a simple 2-inputs-1-output fuzzy model, the designer has to select the most optimum set of fuzzy rules from more than 10 000 combinations. The authors have developed fuzzy models for machinability data selection (Int. J. Flexible Autom. Integrated Manuf. 5 (1 and 2) (1997) 79). There are more than 2×10²⁹ possible sets of rules for each model. The situation would be more complicated if there were a further increase in the number of inputs and/or outputs. The fuzzy rules (Turning Handbook of High-Efficiency Metal Cutting, General Electric Co., Detroit) were selected based on trial and error and/or intuition. Genetic optimization has been suggested in this paper to further optimize the fuzzy rules. The development of a Fuzzy Genetic Optimization algorithm is presented and discussed. An object-oriented library to handle fuzzy rules optimization with genetic optimization has been developed. The effect of constraint rules is also presented and discussed. Comparisons between the results from the optimized models and literature are made.