一种海量规则模式匹配方法

基于各种海量规则信息处理的需求,提出了一种海量规则模式匹配方法。设计了海量规则模式匹配方法的基本算法步骤,研究了各种规则节点的匹配处理方法。最后总结了海量规则模式匹配方法的特点。海量规则模式匹配算法部分拓展了现有规则匹配处理模式,提出了新的匹配处理方法。对比结果表明,该方法具有较好的效果。     

关联规则中Apriori算法的研究与改进

关联规则反映了大量数据中项集间的相互依存性和关联性。Apriori算法是关联规则挖掘中的经典算法,目前已有很多的改进版本,但大多存在多次扫描数据库,项集生成瓶颈和模式匹配频繁的问题,算法效率比较低。本文深入的分析研究关联规则Apriori算法,改进候选频繁项目集的连接和剪枝策略,改进对事务的处理方式,减少模式匹配所需的时间开销,并给出了改进算法。     

基于语义Web规则的软件体系结构

针对目前软件系统面向大规模和复杂业务处理的要求,研究对业务流程和逻辑进行形式化的逻辑描述和逻辑处理,引入Mandarax规则引擎进行业务逻辑的推理处理,实现业务逻辑的集中处理。作为一个可复用的系统构架设计的基于规则的软件体系结构,使用Prova建立规则层,利用规则引擎实现业务逻辑的推理处理,通过规则层应用实验的实现证明基于规则的软件系统结构的可行性和有效性,在一定程度上完善了软件工程设计技术的理论层次。     

基于Snort的入侵检测系统性能优化

通过对Snort的规则匹配方式和模式匹配算法进行分析,为了提高基于Snort的入侵检测系统检测效率,提出了在规则匹配过程中充分利用处理函数的参数之间的关系,从而动态减少无效匹配次数,在模式匹配阶段采用改进的模式匹配算法提高匹配速度,从根本上优化了入侵检测系统的检测性能。     

基于频繁模式树的普遍化关联规则挖掘

提出了基于频繁模式树的普遍化关联规则挖掘算法 MGAR- FP,充分利用频繁模式树的性质 ,避免大量候选模式的生成和频繁模式匹配 ,提高了挖掘的效率和速度 .实验表明 ,算法是有效的 ,比传统的普遍化关联规则挖掘算法Cum ulate快     

基于可拓规则的故障诊断专家系统推理机的研究

针对传统产生式规则在知识表示、匹配冲突等方面存在的局限,提出了一种将可拓规则用于故障诊断专家系统推理机的方法;该方法重点研究了可拓规则的匹配原理和可拓推理机算法思想,提出了匹配度计算方法并用来计算故障条件与规则前件的匹配度;根据研究表明,利用可拓规则进行推理,不仅在知识表示上比传统产生式规则推理有所提高,而且还解决了传统专家系统容易出现匹配冲突等问题;最后以AMU故障推理为例,说明可拓推理机具有推理速度快、效率高等优点,取得了较好的推理效果.     

基于Spark的OWL语义规则并行化推理算法

随着语义网的快速发展,语义数据也高速增长,传统单机推理系统无法满足推理需求,而已有的并行推理算法在推理完备性和稳定性上存在明显不足。提出的基于Spark的并行推理算法(PROS)从以下3点进行了优化：(1)通过分析OWL Horst规则依赖关系,结合数据的分类结果将规则分四类。(2)四类规则分别设计了区域最优的规则执行顺序,进一步提高了并行推理的执行效率。(3)将Sameas规则考虑到迭代中,显著提高了算法的推理能力。实验结果表明,相比已有并行推理算法,PROS并行推理算法在保证推理完备性和稳定性上表现更加出色,推理效率亦有小幅提高;同时PROS相比单机推理算法大大缩短了推理时间,处理大规模数据展现出优良的并行扩展性。     

Rete算法的应用研究

对专家系统和基于规则的专家系统进行了研究,尤其对基于规则的专家系统中的模式匹配算法进行了深入分析和理解.在此理论基础上,将一种高效的模式匹配算法——Rete算法引入到实际的故障的诊断系统中,以保证故障诊断的高效性和准确性.为此在实际系统中,借助规则引擎将故障信息规则化,并将故障诊断流程以层次分明的XML文档进行表示,来模拟领域专家进行故障诊断和故障排除的功能.实践表明,该故障诊断系统可以提高定位故障的速度和准确度.     

Snort规则检测及其优化分析

入侵检测系统IDS的规则检测的效率直接关系到IDS系统的性能.本文对开放源码入侵检测系统Snort的规则检测进行了分析,从标准的规则检测所存在的问题出发,比较了已有的模式匹配算法,最后讨论了一种基于优化规则集的检测方案.     

基于规则推理网络的分类模型

为了缓解神经网络的“黑盒子”机制引起的算法可解释性低的问题,基于使用证据推理算法的置信规则库推理方法(以下简称RIMER)提出了一个规则推理网络模型.该模型通过RIMER中的置信规则和推理机制提高网络的可解释性.首先证明了基于证据推理的推理函数是可偏导的,保证了算法的可行性;然后,给出了规则推理网络的网络框架和学习算法,利用RIMER中的推理过程作为规则推理网络的前馈过程,以保证网络的可解释性;使用梯度下降法调整规则库中的参数以建立更合理的置信规则库,为了降低学习复杂度,提出了“伪梯度”的概念;最后,通过分类对比实验,分析了所提算法在精确度和可解释性上的优势.实验结果表明,当训练数据集规模较小时,规则推理网络的表现良好,当训练数据规模扩大时,规则推理网络也能达到令人满意的结果.     

Fuzzy Interpolative Reasoning for Sparse Fuzzy Rule-Based Systems Based on ${bm alpha}$-Cuts and Transformations Techniques

In sparse fuzzy rule-based systems, the fuzzy rule bases are usually incomplete. In this situation, the system may not properly perform fuzzy reasoning to get reasonable consequences. In order to overcome the drawback of sparse fuzzy rule-based systems, there is an increasing demand to develop fuzzy interpolative reasoning techniques in sparse fuzzy rule-based systems. In this paper, we present a new fuzzy interpolative reasoning method via cutting and transformation techniques for sparse fuzzy rule-based systems. It can produce more reasonable results than the existing methods. The proposed method provides a useful way to deal with fuzzy interpolative reasoning in sparse fuzzy rule-based systems.      

基于Snort的模式匹配算法研究

随着网络的迅速发展,网络安全问题日益突出,入侵检测技术的应用越来越广泛,对Snort入侵检测系统来说,模式匹配算法仍是其使用最多的基本算法,模式匹配算法的效率直接影响到入侵检测系统的性能。该文介绍了KMP和BM算法,并对其进行了比较,并对BM算法的进行了改进,提高了模式匹配的速度。     

Developing maintainable expert systems using case-based reasoning

Abstract: Maintainability problems associated with traditional software systems are exacerbated in rule-based systems. The very nature of that approach — separation of control knowledge and data-driven execution — hampers maintenance. While there are widely accepted techniques for maintaining conventional software, the same is not true for rule-based systems. In most situations, both a knowledge engineer and a domain expert are necessary to update the rules of a rule-based system. This paper presents, first, an overview of the software engineering techniques and object-oriented methods used in maintaining rule-based systems. It then discusses alternate paradigms for expert system development. The benefits of using case-based reasoning (from the maintenance point of view) are illustrated through the implementation of a case-based scheduler. The main value of the scheduler is that its knowledge base can be modified by the expert without the assistance of a knowledge engineer. Since changes in application requirements can be given directly to the system by the expert, the effort of maintaining the knowledge base is greatly reduced.     

R++: adding path-based rules to C++

Object-oriented languages and rule-based languages offer two distinct and useful programming abstractions. However, previous attempts to integrate data-driven rules into object-oriented languages have typically achieved an uneasy union at best. R++ is a new, closer integration of the rule-based and object-oriented paradigms that extends C++ with a single programming construct, the path-based rule, as a new kind of class member. Path-based rules-data-driven rules that are restricted to following pointers between objects-are like automatic methods that are triggered by changes to the objects they monitor. Path-based rules provide a useful level of abstraction that encourages a more declarative style of programming and are valuable in object-oriented designs as a means of modeling dynamic collections of interdependent objects. Unlike more traditional pattern-matching rules, path-based rules are not at odds with the object-oriented paradigm and offer performance advantages for many natural applications     

Object/rule integration in CLIPS

Abstract: This paper gives a brief overview of the C Language Integrated Production System (CLIPS) with a focus on the object-oriented features. The advantages of an object data representation over the traditional working memory element (WME), i.e. facts, are discussed and the implementation of the Rete inference algorithm in CLIPS is presented in detail. A few methods for achieving pattern-matching on objects with the current inference engine are given and, finally, the paper examines the modifications necessary to the Rete algorithm to allow direct object pattern-matching.     

A tuned hybrid intelligent fruit fly optimization algorithm for fuzzy rule generation and classification

Fuzzy rule-based systems (FRBSs) are well-known soft computing methods commonly used to tackle classification problems characterized by uncertainties and imprecisions. We propose a hybrid intelligent fruit fly optimization algorithm (FOA) to generate and classify fuzzy rules and select the best rules in a fuzzy if–then rule system. We combine a FOA and a heuristic algorithm in a hybrid intelligent algorithm. The FOA is used to create, evaluate and update triangular fuzzy rule-based and orthogonal fuzzy rule-based systems. The heuristic algorithm is used to calculate the certainty grade of the rules. The parameters in the proposed hybrid algorithm are tuned using the Taguchi method. An experiment with 27 benchmark datasets and a tenfold cross-validation strategy is designed and carried out to compare the proposed hybrid algorithm with nine different FRBSs. The results show that the hybrid algorithm proposed in this study is significantly more accurate than the nine competing FRBSs.

     

A Gaussian mixture model based discretization algorithm for associative classification of medical data

Knowledge-based systems such as expert systems are of particular interest in medical applications as extracted if-then rules can provide interpretable results. Various rule induction algorithms have been proposed to effectively extract knowledge from data, and they can be combined with classification methods to form rule-based classifiers. However, most of the rule-based classifiers can not directly handle numerical data such as blood pressure. A data preprocessing step called discretization is required to convert such numerical data into a categorical format. Existing discretization algorithms do not take into account the multimodal class densities of numerical variables in datasets, which may degrade the performance of rule-based classifiers. In this paper, a new Gaussian Mixture Model based Discretization Algorithm (GMBD) is proposed that preserve the most frequent patterns of the original dataset by taking into account the multimodal distribution of the numerical variables. The effectiveness of GMBD algorithm was verified using six publicly available medical datasets. According to the experimental results, the GMBD algorithm outperformed five other static discretization methods in terms of the number of generated rules and classification accuracy in the associative classification algorithm. Consequently, our proposed approach has a potential to enhance the performance of rule-based classifiers used in clinical expert systems.     

Geometric constraint solving with geometric transformation

This paper proposes two algorithms for solving geometric constraint systems. The first algorithm is for constrained systems without loops and has linear complexity. The second algorithm can solve constraint systems with loops. The latter algorithm is of quadratic complexity and is complete for constraint problems about simple polygons. The key to it is to combine the idea of graph based methods for geometric constraint solving and geometric transformations coming from rule-based methods.     

Optimizing real-time equational rule-based systems

Analyzing and reducing the execution-time upper bound of real-time rule-based expert systems is a very important task because of the stringent timing constraints imposed on this class of systems. We present a new runtime optimization to reduce the execution-time upper bound of real-time rule-based expert systems. In order to determine rules to be evaluated at runtime, a predicate dependency list, which consists of a predicate, its active rule set and corresponding inactive rule set, is created for each predicate in a real-time rule-based program. Based on the predicate dependency list and the current value of each variable, the new runtime optimization dynamically selects rules to be evaluated at runtime. For the timing analysis of the proposed algorithm, we introduce a predicate-based rule dependency graph, a predicate-based enable-rule graph, and their construction algorithm. We also discuss the bounded time of the equational logic rule-based program using the predicate-based rule dependency graph as well as the predicate-based enable-rule graph. The implementation and performance evaluation of the proposed algorithm using both synthetic and practical real-time rule-base programs are also presented. The performance evaluation shows that the runtime optimizer reduces the number of rule evaluations and predicate evaluations as well as the response time upper bound significantly, and the new algorithm yields better execution-time upper bound compared to other optimization methods.     

A computational paradigm that integrates rule-based and model-based reasoning in expert systems

This article presents a new computational paradigm that integrates rule-based and model-based reasoning in expert systems. Our experience in expert systems research and development indicates that the rule-based technique is simple, elegant, and efficient; whereas the model-based approach is complex but powerful, CPU-consuming but robust. Combining both the rule-based and the model-based methods into one paradigm means having the best of both worlds. to achieve this goal, we have extended the Prolog unification algorithm to accommodate semantic unification. the resulting computational procedure is named R.M. This new inference procedure uses rule-based reasoning by default, and it automatically invokes model-based reasoning when all the rules become inapplicable, but it returns to rule-based reasoning whenever the rules become usable again. the idea behind this problem-solving strategy is to achieve maximum efficiency as well as robustness in expert systems. Examples are used throughout the article to illustrate our notions. the article also sketches an application in the domain of telecommunication networks maintenance and describes our experimental results.