关联图与主动规则集的终止性分析

终止性反映了主动数据库系统良好的行为特性.目前有关主动规则集终止性分析的主要依据是触发图和活化图,其分析结果是保守的.为表达一个规则的动作可能使另一规则的条件为假的事实,引入了惰化图(deactivationgraph),并与触发图和活化图相结合,定义了更为一般的关联图(relationshipgraph).同时,给出了基于关联图的主动规则集终止性静态分析算法,提高了终止性判定的准确性.     

基于触发路径的主动规则集终止性分析

为满足用户对网络服务的个性化、定制化和主动化需求,主动规则成为解决这些问题的关键技术.研究了在网络环境下基于规则的复杂应用中,大量规则集同时触发所带来的规则终止性问题,提出的分析方法确保主动规则能够有效运行,以提供更加灵活的主动服务.讨论了以静态分析方法为主的主动规则终止性分析相关工作,随后给出问题描述和相关形式化定义.分析了基于关联图的终止性分析方法的保守性,引入触发路径和有限触发环概念,提出了基于触发路径的两种终止情形分析方法,提高了规则集终止性分析的准确性,采用两阶段分析算法保证了分析效率.与相关分析方法的实验比较说明,文中方法能够更准确高效地检测主动规则集的终止性,并适应基于主动规则的其它应用.     

XML中的主动规则的模型及其可终止性分析

在对XML数据模型主动机制研究的基础上，结合规则实例提出了一种新的分析规则终止性的静态判定算法。此算法基于触发环的概念，首先对触发环中每一个被修改的节点产生一个递归等式，然后通过展开递归等式并检验其可满足性，就可以分析规则集的可终止性，这样不仅可以得到更高的精确度及更小的复杂度，而且可以得到有效的判定主动规则可终止性的条件。     

Discovering Neglected Conditions in Software by Mining Dependence Graphs

Neglected conditions are an important but difficult-to-find class of software defects. This paper presents a novel approach to revealing neglected conditions that integrates static program analysis and advanced data mining techniques to discover implicit conditional rules in a code base and to discover rule violations that indicate neglected conditions. The approach requires the user to indicate minimal constraints on the context of the rules to be sought, rather than specific rule templates. To permit this generality, rules are modeled as graph minors of enhanced procedure dependence graphs (EPDGs), in which control and data dependence edges are augmented by edges representing shared data dependences. A heuristic maximal frequent subgraph mining algorithm is used to extract candidate rules from EPDGs, and a heuristic graph matching algorithm is used to identify rule violations. We also report the results of an empirical study in which the approach was applied to four open source projects (openssl, make, procmail, amaya). These results indicate that the approach is effective and reasonably efficient.     

半扩展规则下分解的定理证明方法

基于扩展规则的定理证明方法在一定意义上是与归结原理对偶的方法,通过子句集能否推导出所有极大项来判定可满足性.IER(improved extension rule)算法是不完备的算法,在判定子句集子空间不可满足时,并不能判定子句集的满足性,算法还需重新调用ER(extension rule)算法,降低了算法的求解效率.通过对子句集的极大项空间的研究,给出了子句集的极大项空间分解后子空间的求解方法.通过对扩展规则的研究,给出了极大项部分空间可满足性判定方法PSER(partial semi-extension rule).在IER算法判定子空间不可满足时,可以调用PSER算法判定子空间对应的补空间的可满足性,从而得到子句集的可满足性,避免了不能判定极大项子空间可满足性时需重新调用ER算法的缺点,使得IER算法更完备.在此基础上,还提出DPSER(degree partial semi-extension rule)定理证明方法.实验结果表明:所提出的DPSER和IPSER的执行效率较基于归结的有向归结算法DR、IER及NER算法有明显的提高.     

The design and implementation of the Ariel active database rulesystem

Describes the design and implementation of the Ariel DBMS and its tightly-coupled forward-chaining rule system. The query language of Ariel is a subset of POSTQUEL (the POSTGRES QUEry Language), extended with a new production-rule sublanguage. Ariel supports traditional relational database query and update operations efficiently, using a System R-like query processing strategy. In addition, the Ariel rule system is tightly coupled with query and update processing. Ariel rules can have conditions based on a mix of selections, joins, events and transitions. For testing rule conditions, Ariel makes use of a discrimination network composed of a special data structure for testing single-relation selection conditions efficiently, and a modified version of the TREAT algorithm, called A-TREAT, for testing join conditions. The key modification to TREAT (which could also be used in the Rete algorithm) is the use of virtual α-memory nodes which save storage since they contain only the predicate associated with the memory node instead of copies of data matching the predicate. In addition, the notions of tokens and α-memory nodes are generalized to support event and transition conditions. The rule-action executor in Ariel binds the data matching a rule's condition to the action of the rule at rule fire time, and executes the rule action using the query processor     

Interference analysis in multiple rule firing systems

Many multiple rule firing models are being formulated to improve the performance of forward chaining production systems. Multiple rule firing can possibly compromise working memory consistency, due to interference between actions of one rule and conditions of another. Techniques based on data dependency graphs of production systems have been proposed for compile time and run time interference detection. These techniques are highly memory intensive requiring large storage space and impose a high computational overhead. We formulate interference as a non-empty join of condition and action elements in a production system. The RETE net is used to compute the joins and detect interference, thus obviating the need for data dependency graphs. This technique is less memory intensive requiring negligible storage space when compared to data dependency graph techniques.     

粗集理论中基于规则及其参数的分类识别

粗集理论的提出为获取if...then...形式的规则提供了重要方法,但是现有的方法一般没有考虑决策表中属性的权重信息。此外,在利用规则进行分类识别时,还可能用到其他方面的信息,如导出规则的等价类、规则前件的长度等。因此,笔者从规则的支持量、精度、适用度、权重、导出规则的等价类和前件长度几个参数对一条规则进行全面描述。利用规则及其参数,提出了几种分类识别的策略。     

PEARD: A Prototype Environment for Active Rule Debugging

This research has investigated dynamic, execution-based rule analysis through the development of a Prototype Environment for Active Rule Debugging, called PEARD. PEARD simulates the execution of active database rules, supporting the Event-Condition-Action rule paradigm. Rule definition is flexible, where changes to rules can be applied immediately during a debugging session without recompiling the system. A breakpoint debugging tool allows breakpoints to be set so that the state of variables may be inspected and changed anytime a breakpoint is reached during rule execution. A rule visualization tool displays the rule triggering process in graph form, supporting different visualization granularities to help the user to understand rule execution. Color coding is also used as part of the visualization tool to help the user see where the different parts of an ECA rule are executed due to deferred coupling modes. Users can examine different parts of the rule graph display to inspect the state of a transaction at different rule execution points. Other debugging features include a means for detecting potential cycles in rule execution and a utility to examine different rule execution paths from the same point in the rule triggering process. Our experience with PEARD has helped to identify some of the useful functional components of an active rule debugging tool and to identify research directions for future active rule development environments.This research was partially supported by NSF Grant No. IRI-9410993.