一个基于粗糙集的XML函数依赖发现算法

数据依赖是数据库的一个重要概念。函数依赖是一种常见的数据依赖关系，是数据语义的重要组成部分。随着XML文档的大量出现，这一概念被引入到XML的领域中。本文在约束限制范围的基础上，给出了XML函数依赖的定义。引入粗糙集解决XML数据不完整的特点，给出XML函数依赖的判定定理。并且提出了一个发现XML文档中最小非平凡函数依赖的算法。该算法基于一致集的概念，通过不可分辨关系划分元组集减少求一致集的运算次数，使用逐层求精的算法来计算最小非平凡XML函数依赖集的左部。通过该算法得到的XML函数依赖的语义信息对数据存储模式设计、查询优化和更新异常检查来说是十分重要的。     

最小归纳依赖关系在采掘聚类关联规则中的应用研究

数据集中的冗余属性会降低数据采掘结果的解释能力及精度。该文介绍了在非项目集类型的数据集中采掘聚类关联规则的基本原理,并利用RoughSet理论对属性间的归纳依赖关系进行了分析,提出了一个在数据集中发现没有冗余属性的最小归纳依赖关系的算法,使采掘出的关联规则的精度及解释能力都达到令人满意的水平。     

一种用户任务级别上的数据库入侵检测方法

通常，更新数据库中一个数据项之前可能读或者写一些数据项，更新一个数据项后，可能也有一些数据项被更新。依据数据之间的这种依赖关系，本文提出一种用户任务级别 的数据依赖挖掘算法，从数据库日志中挖掘出用户任务事务中存在的数据依赖关系。如果在检测阶段发现同一用户任务中有事务与挖掘出的数据依赖关系不符合，则可判断此
此用户任务中有异常活动出现。     

农田信息系统中时态数据冗余问题的研究

数据冗余问题一直是时态关系数据库存储时不可避免的问题。分析了几种时态关系模型在数据存储上的缺陷,结合函数依赖的概念和时态关系模型。在以时态函数依赖度为依据的基础上提出了一种时态函数依赖关系模型,并将此模型成功应用于农田信系统中,使农田信息数据冗余大量减少,并在一定程度上缩短了数据库操作查询时间,提高了效率。     

概念格的内涵缩减与数据库推理依赖

值依赖是数据库推理问题研究的一个新课题.首先介绍了形式背景和概念格,提出了值依赖的形式概念模型.将数据属性的安全敏感级别引入到值依赖研究中,提出推理依赖及α极大推理依赖概念,并建立了形式概念格的内涵缩减与数据库推理依赖之间的关系.进一步证明了由概念格的内涵缩减推导出数据库中完备的、无冗余的α极大推理依赖集.最后提出并实例验证了发现数据库中全部推理依赖集的算法.推理依赖是关系数据库中最重要的属性依赖关系之一,其研究对检测和消除数据库推理通道具有十分重要的意义.     

基于数据依赖的过程合理性分析

软件过程模型的很多语义上的错误和歧义都是由数据依赖关系引起的,例化阶段数据依赖关系的研究,对过程的执行效率有很大影响.首先介绍了过程模型SPM,在其基础上,提出了数据依赖的概念,并给出一种活动-数据关系;然后通过对该关系进行分析,提出了保证过程模型实例语义合理的基本规则;最后应用有穷状态自动机理论,实现了保证这些规则的算法.     

数据依赖与数据库规范化

任何一个数据库都会存在不同程度的数据冗余和更新异常等问题,为从源头上减少数据库的数据冗余和更新异常等问题,在数据库的设计过程中应进行规范化。通过对数据依赖和规范化理论进行研究,比较二者之间的关系来说明数据库的规范化过程,并以实例来分析规范化过程。分析比较发现规范化过程的实质是消除不合理的数据依赖。     

数据质量检测规则挖掘方法

数据质量规则是检测数据库质量的关键。为从关系数据库中自动发现数据质量规则,并以其为依据检测错误数据,研究质量规则表示形式及其评估度量,提出以数据项分组及其可信度为依据的最小质量规则计算准则、挖掘算法以及采用质量规则检测错误数据的思路。该数据质量规则形式借鉴关联规则的可信度评估机制、条件函数依赖的表达能力,统一描述函数依赖、条件函数依赖、关联规则等,具有简洁、客观、全面、检测异常数据准确等特性。与相关研究相比,降低挖掘算法的时间复杂度,提高检错率。用实验证明该方法的有效性和正确性。     

基于近邻传播聚类和TANE算法的高校数据中函数依赖的发现

针对高校实际数据质量检测过程中数据集存在缺失值以及发现的函数依赖个数较少且不准确的问题，提出了一种结合近邻传播（AP）聚类算法和TANE算法的高校函数依赖发现方法（APTANE）。首先，对数据集中的中文字段进行列剖析，将中文字段值用对应的数值来表示；其次，使用AP聚类算法对数据集中的缺失值进行填补；最后，使用TANE算法从处理好的数据集中自动发现出满足非平凡、最小要求的函数依赖。实验结果表明，在使用AP聚类算法对真实的高校数据集进行修复之后，相比于直接使用函数依赖自动发现算法，发现的函数依赖个数增加到了80个，经过缺失值填补后所发现的函数依赖在表示字段间关联关系时也更加准确，减少了领域专家的工作量，提升了高校数据所拥有数据的质量。     

一种多概念层数值关联规则采掘方法

目前,数据采掘已成为人工智能、数据库等领域的重要研究课题。数据采掘是从大量的数据中自动高效地提取未知的、可用的、可信的、可理解的知识的数据处理新技术。为了便于理解,数据采掘的结果可以用人们熟悉的概念来表示。某一领域中的概念按其内涵和外延往往形成一定的关系。在数据采掘中,主要有概念格和概念层次两种描述这种关系的方法,概念格主     

Discovering branching and fractional dependencies in databases

The discovery of dependencies between attributes in databases is an important problem in data mining, and can be applied to facilitate future decision-making. In the present paper some properties of the branching dependencies are examined. We define a minimal branching dependency and we propose an algorithm for finding all minimal branching dependencies between a given set of attributes and a given attribute in a relation of a database. Our examination of the branching dependencies is motivated by their application in a database storing realized sales of products. For example, finding out that arbitrary p products have totally attracted at most q new users can prove to be crucial in supporting the decision making.In addition, we also consider the fractional and the fractional branching dependencies. Some properties of these dependencies are examined. An algorithm for finding all fractional dependencies between a given set of attributes and a given attribute in a database relation is proposed. We examine the general case of an arbitrary relation, as well as a particular case where the problem of discovering the fractional dependencies is considerably simplified.     

属性依赖规则在概念格构建中的应用

在实际形式背景中,属性依赖关系的存在是不可避免的。因此,研究把属性依赖规则应用于概念格构建中是有意义的。通过对属性依赖规则表示和约简的定义,描述了一种新的构建概念格的方法,即把形式背景和约简后属性依赖规则作为输入数据,同时考虑其属性蕴含关系,从而在不需要构建整个概念格的情况下,产生基于属性依赖规则的概念格。其目的在于加快概念格的产生,使其在满足属性依赖规则的情况下,构建概念层次,发现潜在的信息。     

Existence dependency: The key to semantic integrity betweenstructural and behavioral aspects of object types

In object-oriented conceptual modeling, the generalization/specialization hierarchy and the whole/part relationship are prevalent classification schemes for object types. This paper presents an object-oriented conceptual model where, in the end, object types are classified according to two relationships only. Existence dependency and generalization/specialization. Existence dependency captures some of the interesting semantics that are usually associated with the concept of aggregation (also called composition or Part Of relation), but in contrast with the latter concept, the semantics of existence dependency are very precise and its use clear cut. The key advantage of classifying object types according to existence dependency are the simplicity of the concept, its absolute unambiguity, and the fact that it enables to check conceptual schemes for semantic integrity and consistency. We will first define the notion of existence dependency and claim that it is always possible to classify objects according to this relationship, thus removing the necessity for the Part Of relation and other kinds of associations between object types. The second claim of this paper is that existence dependency is the key to semantic integrity checking to a level unknown to current object-oriented analysis methods. In other words: Existence dependency allows us to track and solve inconsistencies in an object-oriented conceptual schema     

基于粗集理论的贝叶斯故障诊断方法研究

贝叶斯统计推断方法是故障诊断技术领域一项重要的技术,在统计模式识别领域具有广泛的应用;针对朴素贝叶斯方法的缺点,提出了基于粗集理论的贝叶斯诊断方法,该方法利用历史诊断记录,综合考虑故障征兆和故障原因之间的依赖关系,基于粗集方法进行了故障征兆属性信息的约简,得到了故障征兆和故障原因的最小描述;通过属性约简,改善了贝叶斯方法中要求的属性信息之间的独立性限制,实验结果表明,基于粗集理论的贝叶斯故障诊断方法对于简化诊断模型,减少算法执行时间,提高诊断速度具有重要作用.     

An Infornation-Theoretic Analysis of Relational Databases—Part I: Data Dependencies and Information Metric

Database design is based on the concept of data dependency, which is the interrelationship between data contained in various sets of attributes. In particular, functional, multivalued and acyclic join, dependencies play an essential role in the design of database schemas. The basic definition of an information metric and how this notion can be used in relational database are discussed in this paper. We use Shannon entropy as an information metric to quantify the information associated with a set of attributes. Thus, we prove that data dependencies can be formulated in terms of entropies. These formulas make the numerical computation and testing of data dependencies feasible. Among the different types of data dependencies, the acyclic join dependency is most important to the design of a relational database schema. The acyclic join dependency, with multivalued dependency as a special case, impose a constraint on the information-preserving decomposition of a relation. It is interesting that this constraint on a relation is similar to Gibbs' condition for separating physical systems in statistical mechanics. They both assert that entropy is preserved during the decomposition process. That is, the entropies of the corresponding set of attributes must satisfy the inclusion–exclusion identity.     

基于Extend的多态PMS仿真模型

针对现有解决多态多阶段任务系统(PMS)的方法存在过程复杂、缺乏统一排序的问题,以最小割集为基础,提出基于Extend的多态PMS仿真模型。通过结构函数表达各阶段的逻辑关系,在已知基本设备失效分布函数的情况下,利用抽样统计思想处理多态PMS中的阶段依赖和模式依赖关系。相比DEP-BDD解析算法,应用该模型求解PMS能在保证可靠度误差较小、稳定性较好的基础上,使建模分析过程更简单。     

Discovering Dependencies via Algorithmic Mutual Information: A Case Study in DNA Sequence Comparisons

Algorithmic mutual information is a central concept in algorithmic information theory and may be measured as the difference between independent and joint minimal encoding lengths of objects; it is also a central concept in Chaitin's fascinating mathematical definition of life. We explore applicability of algorithmic mutual information as a tool for discovering dependencies in biology. In order to determine significance of discovered dependencies, we extend the newly proposed algorithmic significance method. The main theorem of the extended method states thatd bits of algorithmic mutual information imply dependency at the significance level 2^–d+O(1). We apply a heuristic version of the method to one of the main problems in DNA and protein sequence comparisons: the problem of deciding whether observed similarity between sequences should be explained by their relatedness or by the mere presence of some shared internal structure, e.g., shared internal repetitive patterns. We take advantage of the fact that mutual information factors out sequence similarity that is due to shared internal structure and thus enables discovery of truly related sequences. In addition to providing a general framework for sequence comparisons, we also propose an efficient way to compare sequences based on their subword composition that does not require any a priori assumptions about k-tuple length.     

基于推理通道的函数依赖推理控制

数据库推理控制是数据库安全领域的重要研究内容.函数依赖关系在推理攻击中通常会起到重要作用.文中对基于函数依赖关系的推理控制问题进行了研究,在推理通道思想的基础上,提出了一种基于最小函数依赖集构造推理通道的方法,以及一种基于推理通道的动态组合通道实例的方法,实现了在数据库运行阶段针对函数依赖关系的动态推理控制.上述方法已应用于国产数据库神舟OSCAR安全版中,实践表明,上述方法能够有效地防御基于函数依赖关系的推理攻击.