基于概率的有效时间不确定的时态数据库挖掘

关于时态数据库中有效时间不确定性的时态关系,Snodgrass采用概率的方法给出了不确定时刻的Before时态关系。该文定义了一种新的不确定时刻、不确定时间区间的同时关系,并给出了它们的交,运用同时关系对病历数据库进行挖掘。     

基于间断区间的时态知识表示

一般,用逻辑形式表示时态信息的方法是命题附加一个时间点或时间区间。文［１］指出,时间区间表示单个事件带间断区间是困难的,不过,文［１］定义两个间断区间的时态关系为一个矩阵,其计算量相当之大以至该方法不实用二本文给出一个基于间断区间的时态知识表示模型,它将两个间断区间的时态关系分为２０种,其计算量与Ａｌｌｅｎ的区间演算属同一数量级。     

信息系统中业务规则与约束的时态化研究

在OCL和ECA规则的基础上，通过对业务对象与ECA规则的时态化关系的分析，提出了业务规则与约束的一种时态化扩展建模方法，该方法引入了时态化算子和时间区间等概念来实现对时态化对象和属性的历史信息的引用和时态化建模。     

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。     

基于间断区间的时间本体表示及应用

通过间断区间的时态理论扩展了OWL-Time,用在本体中表达单个事件带间断时区的情况。在本体中定义了间断区间概念以及间断区间的时态关系,并以物流领域运输事件的表示为例阐述了领域中基于间断区间的时间知识在本体中的表示方式。通过定义Jena中的原语、规则以及扩展对推理模型的操作构建了相应的推理机,对推理机进行实验检验,不仅验证了推理机运行的正确性,同时也保证了时间知识在本体和规则中表示的正确性,解决了带间断区间的时态知识在语义网中表示和推理的问题。     

多媒体数据库的时态索引技术

针对多媒体数据库中具有时态特性的媒体对象，提出一种基于区间的索引机制，目的是为了提高时间检索的性能。这种时态索引可以快速查找任意时间段中的时态对象，它能有效地支持多种时态关系操作符；支持多种时态集函数；支持时态ＪＯＩＮ操作等等。该索引支持媒体展示过程。     

一个入侵特征的时间语义模型

入侵特征由系统属性和事件序列组成,时序关系是描述它们的关键。ISITL是一种基于Allen的时段时态逻辑和一阶谓词逻辑的入侵特征形式化描述语言,它将系统属性和事件与相应的时段紧密相连,时段间的相互关系用13个基本函数和3个扩展函数来描述。在基于多代理的计算机免疫系统MACIS中,根据ISITL描述设计的检测器确保了较低的“漏报率”和“误报率”。     

基于时态编码和线序划分的时态XML 索引

研究了一种基于时态编码和线序划分的时态XML索引机制.首先,提出一种基于扩展先序编码的时态编码方案,通过该编码可确定结点间的结构关系;其次,在深入分析时间区间关系的基础上引入线序划分的概念,并讨论了获取线序划分的算法;然后,建立了整合路径结构信息和时态约束信息的时态结构摘要,并在此基础上建立了时态XML索引结构—TempSumIndex,同时研究了基于TempSumlndex的时态XML查询和增量式更新算法;最后,对TempSumlndex和现有时态XML索引技术的基本性能进行了详细的实验评估.实验结果表明,TempSumlndex具有更为优越的性能.     

时态数据库的沿革,特色与代表人物：—时态数据库二十年回顾之一

1 时态数据库产生的背景伴随着物理流的信息流包含着事件的时态信息(Temporal Information),其中有时刻信息(InstantInformation),时间区间信息(Interval Information)和相对时间信息(之前、之后、重叠)等等。传统的数据管理系统把时态数据作为用户定义时间字段进行存贮和管理,只反应了一个对象在某一个时刻的状态(快照),不联系其过去和未来。日益广泛的数据库应用要求了管理被处理事件的历史性信息,和系统中元事件的时态信息,例如查删改事务的时刻,时态信息数据反映了事物发生发展的过程,有助于(揭示事物发展的本质规律和这些数据有助于提高数据库系统的可靠性和效率。     

人工智能应用中基于区间代数的时态推理

时态表示和推理是人工智能领域的重要研究内容之一,它的应用范围分布很广,从逻辑基础研究到知识系统的应用。区间代数是一种独立的与领域无关的时态理论。用区间代数能表示不确定的时态关系,可以很方便的用于时态推理,表达能力强;时态关系的区间表示比较直观,可理解性强;同时区间代数可以进一步扩展到二维空间领域,即将区间代数拓展为矩阵代数,实现二维空间推理。在一维时态推理中,将时态的区间表示和矩阵表示相结合,在提高计算效率的同时,保持了形象直观的时态表示。     

入侵特征的时间语义分析及实现

入侵特征对于入侵检测系统至关重要，它们往往由系统属性和事件序列组成，时序关系是描述它们的关键。ISITL(Intrusion Signatures based on Interval Temporal Logic)是一种较高抽象程度的入侵特征形式化描述语言，它对Allen的时段时态逻辑进行了实时描述的扩充，从而加强了其入侵特征的描述能力。在ISITL中，所有的系统属性和事件都与相应的时段紧密相连，其相互关系用13个基本函数和3个扩展函数来描述。与其它入侵特征描述语言相比，ISITL具有简单易用，描述能力强等优点。     

时态外推推理诊断模型

本文通过引入时态一致覆盖集的概念,提出一个基于时态推理与覆盖技术的时态外推推理诊断模型TGSC.TGSC利用模糊集描述有关征兆、故障及相互间关系的时态知识,文中建立了TGSC的基本框架,给出了基于假设图搜索的诊断问题的求解方法,并给出计算示例。     

时态覆盖集诊断模型TGSC

本文通过引入时态一致覆盖集的概念，提出了一个基于时态推理与覆盖技术的时态覆盖集诊断模型ＴＧＳＣ。ＴＧＳＣ利用模糊描述有关征兆、故障及相互间关系的时态知识。文中建立了ＴＧＳＣ的基本框架。给出了基于假设图搜索的诊断问题求解方法，并伴以计算示例。     

O2iJoin: An Efficient Index-Based Algorithm for Overlap Interval Join

Time intervals are often associated with tuples to represent their valid time in temporal relations, where overlap join is crucial for various kinds of queries. Many existing overlap join algorithms use indices based on tree structures such as quad-tree, B⁺-tree and interval tree. These algorithms usually have high CPU cost since deep path traversals are unavoidable, which makes them not so competitive as data-partition or plane-sweep based algorithms. This paper proposes an efficient overlap join algorithm based on a new two-layer flat index named as Overlap Interval Inverted Index (i.e., O2i Index). It uses an array to record the end points of intervals and approximates the nesting structures of intervals via two functions in the first layer, and the second layer uses inverted lists to trace all intervals satisfying the approximated nesting structures. With the help of the new index, the join algorithm only visits the must-be-scanned lists and skips all others. Analyses and experiments on both real and synthetic datasets show that the proposed algorithm is as competitive as the state-of-the-art algorithms.     

Morph-fitting: An effective technique of approximation

An algorithm has been developed and programmed which fits a minimum number of basic patterns, morphs, to a sequence of data points. The dependent variable is given as a scalar value; the independent variable is ‘time-like’ and can represent continuous or discrete time or an event counter. The morphs fitted are an indefinite number of occurrences of trends (straight lines), step functions, and sudden changes (peaks of short time duration). Delay functions span over periods characterized by uninterpretable events.A useful by-product of our investigations is a set of optimum decision rules concerning the boundary points between sequential regression functions.     

Learning and parsing video events with goal and intent prediction

In this paper, we present a framework for parsing video events with stochastic Temporal And–Or Graph (T-AOG) and unsupervised learning of the T-AOG from video. This T-AOG represents a stochastic event grammar. The alphabet of the T-AOG consists of a set of grounded spatial relations including the poses of agents and their interactions with objects in the scene. The terminal nodes of the T-AOG are atomic actions which are specified by a number of grounded relations over image frames. An And-node represents a sequence of actions. An Or-node represents a number of alternative ways of such concatenations. The And–Or nodes in the T-AOG can generate a set of valid temporal configurations of atomic actions, which can be equivalently represented as the language of a stochastic context-free grammar (SCFG). For each And-node we model the temporal relations of its children nodes to distinguish events with similar structures but different temporal patterns and interpolate missing portions of events. This makes the T-AOG grammar context-sensitive. We propose an unsupervised learning algorithm to learn the atomic actions, the temporal relations and the And–Or nodes under the information projection principle in a coherent probabilistic framework. We also propose an event parsing algorithm based on the T-AOG which can understand events, infer the goal of agents, and predict their plausible intended actions. In comparison with existing methods, our paper makes the following contributions. (i) We represent events by a T-AOG with hierarchical compositions of events and the temporal relations between the sub-events. (ii) We learn the grammar, including atomic actions and temporal relations, automatically from the video data without manual supervision. (iii) Our algorithm infers the goal of agents and predicts their intents by a top-down process, handles events insertion and multi-agent events, keeps all possible interpretations of the video to preserve the ambiguities, and achieves the globally optimal parsing solution in a Bayesian framework. (iv) The algorithm uses event context to improve the detection of atomic actions, segment and recognize objects in the scene. Extensive experiments, including indoor and out door scenes, single and multiple agents events, are conducted to validate the effectiveness of the proposed approach.     

Temporal relational data model

This paper incorporates a temporal dimension to nested relations. It combines research in temporal databases and nested relations for managing the temporal data in nontraditional database applications. A temporal data value is represented as a temporal atom; a temporal atom consists of two parts: a temporal set and a value. The temporal atom asserts that the value is valid over the time duration represented by its temporal set. The data model allows relations with arbitrary levels of nesting and can represent the histories of objects and their relationships. Temporal relational algebra and calculus languages are formulated and their equivalence is proved. Temporal relational algebra includes operations to manipulate temporal data and to restructure nested temporal relations. Additionally, we define operations to generate a power set of a relation, a set membership test, and a set inclusion test, which are all derived from the other operations of temporal relational algebra. To obtain a concise representation of temporal data (temporal reduction), collapsed versions of the set-theoretic operations are defined. Procedures to express collapsed operations by the regular operations of temporal relational algebra are included. The paper also develops procedures to completely flatten a nested temporal relation into an equivalent 1 NF relation and back to its original form, thus providing a basis for the semantics of the collapsed operations by the traditional operations on 1 NF relations