时态查询语言对处理不明确时间间隔的扩展

关于时态数据库的先前工作,主要是建立在时态属性的时间间隔和事件的开始/终止点都十分清楚的基础上.然而,在许多实际情况中,事件的时间界限并不十分清楚.因此,表示不明确时间,并给出处理不明确时间查询的方法,是对时态数据库中目前仍缺少的一个有效的扩展.讨论了时态数据库处理不明确时间存在的一些问题,并描述不明确时间间隔的方法.之后扩展了各种时态运算,并给出了与不明确时间相关的新运算符.也提出了能够处理不明确时间的查询语言扩展.在此讨论的时态查询语言对处理不明确时间间隔的扩展和丰富时态数据库的适应性有一定作用.     

不明确时间间隔的表示及时态运算的扩展

讨论了时态数据库处理不明确时间存在的一些问题，并给出描述不明确时间间隔的表示方法，在此基础上给出了各种时态运算的扩展，丰富了时态数据库的适应性。     

不确定时态信息表示及时态运算的扩展

时态信息表示和查询是时态数据库的研究重点,但现有的时态数据库模型在处理不确定时态信息方面仍存在较大困难。而现实生活中很多时态信息都是不确定的,故提出了一种不确定时态信息的表示方法,并扩展时态运算使其支持不确定时态信息的处理和查询,从而扩展了时态数据库的适用范围。     

电子病历管理系统中的时态数据处理

讨论了在非时态DBMS中处理电子病历的时态数据的方法,并给出了在非时态DBMS中处理不明确时间间隔的OVERLAPT运算的方法以及主键的选择。     

时态面向对象数据库查询算法研究

时态关系数据库在关系数据库的基础上扩充了对时间数据的处理、而时态面向对象数据库则是在面向对象数据库的基础上扩充了对时间数据的处理。在时态数据基态修正模型的基础上,讨论了时态面向对象数据库查询优化策略,并给出相应的算法。     

双时态XML查询中now语义失真的研究与扩展

本文研究如何有效地查询基于"单属性表示时态信息"的XML文档的时态信息操作,并选择XML功能较为强大的查询语言XQuery进行时态查询扩展.文中先计论如何解决时态数据库中特殊元素now的语义失真的问题,然后对XQuery进行双时态扩展,最后给出扩展后的查询实例.     

基于不确定时态信息的索引技术

索引技术是时态数据库查询优化的重要方法之一。分析了时态数据库中的不确定时态信息以及基于不确定时态信息的索引技术,并详细讨论了能处理时间变元的改进的4R-tree索引技术的建立过程。     

基于不确定信息的时态索引技术

扩展了时态数据库中时态信息的表示方法，使它能处理不确定时态信息。并实现相应索引机制，从而为支持不确定时态信息的时态数据库打下基础。性能测试的结果说明，该索引不但在功能上支持不确定时态数据，而且在性能上也优于已有的时态索引技术。     

时态数据库的可视化方法与技术

1 引言时态数据库是将时间的概念结合到传统的数据库中。近十年来,人们对此所作的许多研究主要集中在把关系数据模型扩展为时态模型以及(或)把关系查询语言SQL和Quel扩展为时态的模式。除了强调关系模型之外,也有一些研究是将ER和其他的语义数据模型扩展到时态方面。时态数据模型的形式化和时态数据库的物理数据组织结构也受到了人们的关注。     

基于时态的次协调数据库性质与查询优化

为了处理以时间为推移的数据库中不完全信息和矛盾信息,学术界提出了基于时态的次协调数据库的数据模型。本文基于此模型,初步讨论并给出关系运算符的相关性质,根据建立的关系运算符的若干性质对查询语句优化,从而提升查询速度。     

Extending temporal query languages to handle imprecise time intervals

Previous work on temporal and historical databases has been mainly based on the assumption that the time intervals of temporal attributes and the start/finish points of modeled events are precisely known. In many real life situations, however, the time boundary of events and the duration of entity relationships may not be exactly known. Modeling these situations and providing a way to write queries dealing with time impreciseness represents a useful extension currently lacking in existing/proposed temporal database systems. In this paper, we discuss the problem of handling time impreciseness in temporal databases and present three models for the representation of imprecise time intervals. We illustrate the basic idea and motivation of each model, its underlying logic, tradeoffs, and important properties. We also propose query language extensions that can enrich the user interface with capabilities to formulate queries dealing with time impreciseness. Extensions to existing query constructs at both the transaction level and the operator level are presented. New operators related to time impreciseness are also presented. The models and extensions discussed in this paper enrich the flexibility of temporal databases and can be used to help users obtain more meaningful replies for their temporal queries.     

A 1NF temporal relational model and algebra coping with valid-time temporal indeterminacy

In the real world, many phenomena are time related and in the last three decades the database community has devoted much work in dealing with “time of facts” in databases. While many approaches incorporating time in the relational model have been already devised, most of them assume that the exact time of facts is known. However, this assumption does not hold in many practical domains, in which temporal indeterminacy of facts occurs. The treatment of valid-time indeterminacy requires in-depth extensions to the current relational approaches. In this paper, we propose a theoretically grounded approach to cope with this issue, overcoming the limitations of related approaches in the literature. In particular, we present a 1NF temporal relational model and propose a new temporal relational algebra to query it. We also formally study the properties of the new data model and algebra, thus granting that our approach is interoperable with pre-existent temporal and non-temporal relational approaches, and is implementable on top of them. Finally, we consider computational complexity, showing that only a limited overhead is added when moving from determinate to indeterminate time.     

Temporal Granularity and Indeterminacy in Reasoning About Actions and Change: An Approach Based on the Event Calculus

In many real-world applications, temporal information is often imprecise about the temporal location of events (indeterminacy) and comes at different granularities. Formalisms for reasoning about events and change, such as the Event Calculus (EC) and the Situation Calculus, do not usually provide mechanisms for handling such information, and very little research has been devoted to the goal of extending them with these capabilities. In this paper, we propose TGIC (Temporal Granularity and Indeterminacy event Calculus), an approach based on the EC ontology to represent events with imprecise location and to deal with them on different timelines.     

ON TEMPORAL DEDUCTIVE DATABASES

This article introduces a temporal deductive database system featuring a logic programming language and an algebraic front-end. The language, called Temporal DATALOG, is an extension of DATALOG based on a linear-time temporal logic in which the flow of time is modeled by the set of natural numbers. Programs of Temporal DATALOG are considered as temporal deductive databases, specifying temporal relationships among data and providing base relations to the algebraic front-end. The minimum model of a given Temporal DATALOG program is regarded as the temporal database the program models intensionally. The algebraic front-end, called TRA, is a point-wise extension of the relational algebra upon the set of natural numbers. When needed during the evaluation of TRA expressions, slices of temporal relations over intervals can be retrieved from a given temporal deductive database by bottom-up evaluation strategies.
A modular extension of Temporal DATALOG is also proposed, through which temporal relations created during the evaluation of TRA expressions may be fed back to the deductive part for further manipulation. Modules therefore enable the algebra to have full access to the deductive capabilities of Temporal DATALOG and to extend it with nonstandard algebraic operators. This article also shows that the temporal operators of TRA can be simulated in Temporal DATALOG by program clauses.     

First-Order Temporal Verification in Practice

First-order temporal logic, the extension of first-order logic with operators dealing with time, is a powerful and expressive formalism with many potential applications. This expressive logic can be viewed as a framework in which to investigate problems specified in other logics. The monodic fragment of first-order temporal logic is a useful fragment that possesses good computational properties such as completeness and sometimes even decidability. Temporal logics of knowledge are useful for dealing with situations where the knowledge of agents in a system is involved. In this paper we present a translation from temporal logics of knowledge into the monodic fragment of first-order temporal logic. We can then use a theorem prover for monodic first-order temporal logic to prove properties of the translated formulas. This allows problems specified in temporal logics of knowledge to be verified automatically without needing a specialized theorem prover for temporal logics of knowledge. We present the translation, its correctness, and examples of its use. Partially supported by EPSRC project: Analysis and Mechanisation of Decidable First-Order Temporal Logics (GR/R45376/01).     

Temporal inferencing on administrative databases

Temporal inferencing is basic to business data processing, e.g. in such concepts as income, wealth, exchange, budgeting and contractual commitment. Time appears in numerous forms within the information system, however, in data items of varying syntax; or implicitly, in the current state of the database. Here, a uniform concept of time based on temporal intervals over a continuous calendar scale is suggested for administrative applications. Temporal operators and related axioms are proposed for logical databases.