基于BCDM的双时态关系代数

双时态概念数据模型BCDM是一种重要的时态数据库模型，基于BCDM模型，建立了一种双时态关系代数．首先，对时态数据库事务时间和有效时间进行规范化的定义，给出BCDM中时间元素的规范化定义和演算描述；而后，定义时态映射，用以描述BCDM中的双时态元组，并形式描述时态映射的演算；最后，用时态映射定义的元组对双时态关系进行定义，并由此给出双时态关系代数运算的形式化描述．     

BCDM双时态信息规范化及实现

为简化双时态数据模型(BCDM)时态属性表达、减少存储空间、提高查询效率,按照双时态信息的3种表达形式,针对有效时间区间更新历史是否保留的两种情况,讨论双时态数据的合并描述、优化算法、合并传统双时态序偶为"事务时间区间+有效时间区间"的表达机制,给出规范的BCDM双时态形式定义。复杂性分析表明,规范的BCDM双时态标签具有明显的低存储性和高查询效率。     

基于BCDM的含有变量的双时态关系代数研究*

在BCDM(双时态概念数据模型)中,时间变元Now和UC的存在解决了时态记录的频繁更新问题;但是时态变元的存在又给数据库的操作带来某种不确定性甚至是错误,同时引起关系代数运算的歧义。基于BCDM,通过绑定时间变元,用时态映射的集合定义时态关系,由此建立了一种含有时间变元的双时态关系代数体系。该关系代数体系在双时态运算下是封闭的。     

基于对象时态稳定性分类特征的双时态ER模型

对双时态数据库建模时以客观事物的时态特征为切入点,定义有关时态表示的双时态点、双时态组合集和双时态标签,通过分析客观对象的时态稳定性,将其划分为时态值稳定对象和时态值变对象。参照数据库概念结构设计基本方法,给出一种基于对象时态稳定性分类特征的双时态ER模型,并定义了相关的基本图元及建立规则,该模型可有效减少因模式中存在大量与时态无关属性而造成的数据冗余现象。最后给出该模型与应用较为普遍的BCDM模型相互转化的方法,使其具有推广应用价值。     

网络营销平台中的时态数据处理

网络营销中的很多信息都是时变的。目前常见的快照数据库并不能很好地对其描述和处理。文章引入了时态数据库的理论,采用了基于BCDM模型的双时态数据库,并将混合式时态应用模式应用于实际开发中。实践证明,时态数据库在处理与时间相关的数据时表现出独特的优势。     

面向用户的BCDM时态数据的更新研究

BCDM(Bitemporal cencept data model)是一种同时支持事务时间和有效时间的双时态数据模型,该模型不仅反映了客观事物的数据变迁情况,而且也记录了数据对象在数据库中的各种操作历史.以医院信息管理系统HIS的电子病历为例,介绍了BCDM双时态医疗数据的优化存储技术,分析了BCDM双时态医疗数据的增加、删除和修改等操作语义,进一步,为这些操作语义设计了相应的面向医护人员的数据更新算法.实际应用表明,该算法的可行性和有效性,从而为医护人员提供了简捷透明的BCDM双时态医疗数据操作方式.     

一种基于UML的双时态概念模型

随着数据库技术的发展,信息系统对时鸯信息的处理需求越来越强烈。尽管出现了一些时态数据库管理系统,如TimeDB,但这些时态数据模型仍存在着一些不足。该文参考双时态概念数据模型BCDM,对UML类图进行扩展,提出了一种支持时态信息概念建模的概念模型UTCM,UTCM能够支持对时态信息的概念建模,并易于在成熟的DBMS产品上进行实现;     

时态跨度的绑定问题探讨

时态跨度是时态应用中重要但又非常特殊的时态原语：一则,在不同的时态上下文中,其语义内涵往往呈现出不确定性;再者,其灵活的刻画形式又表现出一些不规范性,使得时态应用中难以有效地驾驭其序关系,影响时态运算的精确性。从时态跨度的序关系入手,分析了时态跨度绑定操作的必要性,提出了通过将时态原语和时态跨度映射到仿射空间中,利用仿射变换构建时态跨度绑定的方法,并实现了与时态粒点、非间断时态粒区、间断时态粒区间的绑定和运算。     

一种改进的时态数据模型的设计与应用

在现有的时态数据模型基础上,通过对时态数据的冗余存储与查询效率进行折衷处理,提出了一种改进的时态数据模型,并进一步为该模型的增删改等操作设计了相应的更新算法。实际应用表明,改进模型在增加了约16%的数据冗余存储后其时态查询性能获得了近58%的提升。     

有效的BCDM时态信息简化方法

时态信息是管理决策中的重要支持信息,需求的不同造就了时态信息不同的表示方法.随着数据库与信息技术的深入发展,越来越需要简单高效的时态数据模型以方便时态信息的表示与处理.介绍了BCDM模型及其时态表示思想,讨论了模型中事务时间和有效时间表示在查询优化时存在的问题,提出了一种基于BCDM的时态信息简化方法,不但能够有效的保存数据库现有的时态信息,而且能够大大简化有效时间的表示,为时态数据的查询优化奠定了良好基础.     

神经网络模型的透明化及输入变量约简

由于神经网络很容易实现从输入空间到输出空间的非线性映射,因此,神经网络应用者往往未考虑输入变量和输出变量之间的相关性,直接用神经网络来实现输入变量与输出变量之间的黑箱建模,致使模型中常存在冗余变量,并造成模型可靠性和鲁棒性差。提出一种透明化神经网络黑箱特性的方法,并用它剔除模型中的冗余变量。该方法首先利用神经网络释义图可视化网络;再利用连接权法计算神经网络输入变量的相对贡献率,判断其对输出变量的重要性;最后利用改进的随机化测验对连接权和输入变量贡献率进行显著性检验,修剪模型,并以综合贡献度和相对贡献率均不显著的输入变量的交集为依据,剔除冗余变量,实现NN模型透明化及变量选择。实验结果表明,该方法增加了模型的透明度,选择出了最佳输入变量,剔除了冗余输入变量,提高了模型的可靠性和鲁棒性。因此,该研究为神经网络模型的透明化及变量约简提供了一种新的方法。     

A transformation-based synthesis of temporal specifications

This paper is concerned with specifications expressed in propositional temporal logic with finite past extension. The variables of the specifications are partitioned into input variables, the value of which is determined by the environment, output variables, the value of which may be required by the environment, and internal variables. We present a method to synthesise temporal specifications, i.e. to rewrite them into a form from which a valuation for the output variables may be generated from the input values without backtracking. The transformed specification characterises the same set of input/output functions as the original specification, but in a more explicit way. A simple interpreter chooses the actual function to be used.This work was supported by SERC under project number GR/F/30123 (MetateM).     

Data mining with Temporal Abstractions: learning rules from time series

A large volume of research in temporal data mining is focusing on discovering temporal rules from time-stamped data. The majority of the methods proposed so far have been mainly devoted to the mining of temporal rules which describe relationships between data sequences or instantaneous events and do not consider the presence of complex temporal patterns into the dataset. Such complex patterns, such as trends or up and down behaviors, are often very interesting for the users. In this paper we propose a new kind of temporal association rule and the related extraction algorithm; the learned rules involve complex temporal patterns in both their antecedent and consequent. Within our proposed approach, the user defines a set of complex patterns of interest that constitute the basis for the construction of the temporal rule; such complex patterns are represented and retrieved in the data through the formalism of knowledge-based Temporal Abstractions. An Apriori-like algorithm looks then for meaningful temporal relationships (in particular, precedence temporal relationships) among the complex patterns of interest. The paper presents the results obtained by the rule extraction algorithm on a simulated dataset and on two different datasets related to biomedical applications: the first one concerns the analysis of time series coming from the monitoring of different clinical variables during hemodialysis sessions, while the other one deals with the biological problem of inferring relationships between genes from DNA microarray data.     

Looking into the seeds of time: Discovering temporal patterns in large transaction sets

This paper studies the problem of mining frequent itemsets along with their temporal patterns from large transaction sets. A model is proposed in which users define a large set of temporal patterns that are interesting or meaningful to them. A temporal pattern defines the set of time points where the user expects a discovered itemset to be frequent. The model is general in that (i) no constraints are placed on the interesting patterns given by the users, and (ii) two measures—inclusiveness and exclusiveness—are used to capture how well the temporal patterns match the time points given by the discovered itemsets. Intuitively, these measures indicate to what extent a discovered itemset is frequent at time points included in a temporal pattern p, but not at time points not in p. Using these two measures, one is able to model many temporal data mining problems appeared in the literature, as well as those that have not been studied. By exploiting the relationship within and between itemset space and pattern space simultaneously, a series of pruning techniques are developed to speed up the mining process. Experiments show that these pruning techniques allow one to obtain performance benefits up to 100 times over a direct extension of non-temporal data mining algorithms.     

Granularity reduction in temporal document databases

With rapidly decreasing storage costs, temporal document databases are now a viable solution in many contexts. However, storing an ever-growing database can still be too costly, and as a consequence it is desirable to be able to physically delete old versions of data. Traditionally, this has been performed by an operation called vacuuming, where the oldest versions are physically deleted or migrated from secondary storage to less costly tertiary storage. In temporal document databases on the other hand, it is often more appropriate to remove intermediate versions instead of removing the oldest versions. We call this operation granularity reduction. In this paper we describe the concept of granularity reduction, and present six strategies for selecting the document versions to eliminate. Three of the strategies have been implemented in the V2 temporal document database system, and in this context we discuss the cost of applying the strategies.     

一种检测程序控制流故障的方法

本文介绍一种在容错处理器中实现控制流故障检测的方法。处理器的容错机制是通过修改超标量体系结构,利用时间冗余技术实现的。处理器支持两个指令流并发执行,本文提出的控制流检测算法是通过比较两个时间冗余的指令流的执行结果实现的,与同类实现方案相比,此方法可以进一步节省硬件资源以及额外的处理器执行时间。