Effective timestamping in databases

Many existing database applications place various timestamps on their data, rendering temporal values such as dates and times prevalent in database tables. During the past two decades, several dozen temporal data models have appeared, all with timestamps being integral components. The models have used timestamps for encoding two specific temporal aspects of database facts, namely transaction time, when the facts are current in the database, and valid time, when the facts are true in the modeled reality. However, with few exceptions, the assignment of timestamp values has been considered only in the context of individual modification statements. This paper takes the next logical step: It considers the use of timestamping for capturing transaction and valid time in the context of transactions. The paper initially identifies and analyzes several problems with straightforward timestamping, then proceeds to propose a variety of techniques aimed at solving these problems. Timestamping the results of a transaction with the commit time of the transaction is a promising approach. The paper studies how this timestamping may be done using a spectrum of techniques. While many database facts are valid until now, the current time, this value is absent from the existing temporal types. Techniques that address this problem using different substitute values are presented. Using a stratum architecture, the performance of the different proposed techniques are studied. Although querying and modifying time-varying data is accompanied by a number of subtle problems, we present a comprehensive approach that provides application programmers with simple, consistent, and efficient support for modifying bitemporal databases in the context of user transactions. Received: March 11, 1998 / Accepted July 27, 1999     

An algebraic framework for temporal attribute characteristics

Most real-world database applications manage temporal data, i.e., data with associated time references that capture a temporal aspect of the data, typically either when the data is valid or when the data is known. Such applications abound in, e.g., the financial, medical, and scientific domains. In contrast to this, current database management systems offer preciously little built-in query language support for temporal data management. This situation persists although an active temporal database research community has demonstrated that application development can be simplified substantially by built-in temporal support. This paper's contribution is motivated by the observation that existing temporal data models and query languages generally make the same rigid assumption about the semantics of the association of data and time, namely that if a subset of the time domain is associated with some data then this implies the association of any further subset with the data. This paper offers a comprehensive, general framework where alternative semantics may co-exist. It supports so-called malleable and atomic temporal associations, in addition to the conventional ones mentioned above, which are termed constant. To demonstrate the utility of the framework, the paper defines a characteristics-enabled temporal algebra, termed CETA, which defines the traditional relational operators in the new framework. This contribution demonstrates that it is possible to provide built-in temporal support while making less rigid assumptions about the data and without jeopardizing the degree of the support. This moves temporal support closer to practical applications.     

一种有效的双时态索引技术

时态数据库中时间特征主要由有效时间和事务时间描述,论文扩充了4R索引的双时态数据,使其能处理有效时间初始值大于事务时间初始值的所有双时态数据,同时扩充了4R的查询功能使其不仅可以查询当前和历史数据,还可以查询将来的情况,并改进了相应的索引方法。     

Temporal knowledge extraction from large-scale text corpus

Knowledge, in practice, is time-variant and many relations are only valid for a certain period of time. This phenomenon highlights the importance of harvesting temporal-aware knowledge, i.e., the relational facts coupled with their valid temporal interval. Inspired by pattern-based information extraction systems, we resort to temporal patterns to extract time-aware knowledge from free text. However, pattern design is extremely laborious and time consuming even for a single relation, and free text is usually ambiguous which makes temporal instance extraction extremely difficult. Therefore, in this work, we study the problem of temporal knowledge extraction with two steps: (1) temporal pattern extraction by automatically analysing a large-scale text corpus with a small number of seed temporal facts, (2) temporal instance extraction by applying the identified temporal patterns. For pattern extraction, we introduce various techniques, including corpus annotation, pattern generation, scoring and clustering, to improve both accuracy and coverage of the extracted patterns. For instance extraction, we propose a double-check strategy to improve the accuracy and a set of node-extension rules to improve the coverage. We conduct extensive experiments on real world datasets and compared with state-of-the-art systems. Experimental results verify the effectiveness of our proposed methods for temporal knowledge harvesting.

     

模糊栅格区域的层次拓扑关系模型

空间区域拓扑关系建模是空间推理和地理信息系统(GIS)等领域的一个主要研究内容,近年来模糊空间区域建模及其拓扑关系分析显现出越来越重要的作用．根据栅格数据模型下模糊区域的特征和实际应用领域中拓扑关系分析的特点,提出了一种模糊栅格区域的层次拓扑关系模型,该模型利用3个谓词的真值来分析栅格区域间的拓扑关系,将分明栅格区域作为特例统一处理,能够根据谓词的多种真值实现模糊栅格区域在多个层次上的拓扑关系分析．该模型表达能力强、易于实现,并且在实际应用中得到了令人满意的结果．     

Temporal databases: From theory to applications

Problems concerning the date and time representation in databases are well studied in scientific literature. Temporal databases are required in many applications including the conventional applications of DBMSs such as financial systems. However, commercial systems and standards for the query language do not support temporal data features. A simple and efficient approach for implementing temporal capabilities over conventional commercial DBMSs is proposed and various aspects of its practical applications are described.     

Processing skyline queries in incomplete distributed databases

Due to its great benefits over many database applications, skyline queries have received formidable concern in the last decades. Skyline queries attempt to assist users by identifying the set of data items which represents the best results that meet the conditions of a given query. Most of the existing skyline techniques concentrate on identifying skylines over a single relation. However, in distributed databases, the process of skyline queries required accessing multiple relations which might be located at different sites. Consequently, data items from these multiple relations need to be joined and thus transferring these data items from one site to another is unavoidable. Moreover, the previous techniques also assume that the values of dimensions for every data item are presented (complete) which is not always true as some values may be missing. In this paper, we proposed an approach for processing skyline queries in incomplete distributed databases. The approach derives skylines from multiple relations where dominated data items are removed before joining the relations to reduce the processing time and the network cost. The experimental results illustrate that our proposed approach outperforms the previous approaches in terms of processing time and network cost.     

A foundation for capturing and querying complex multidimensional data

On-line analytical processing (OLAP) systems considerably improve data analysis and are finding wide-spread use. OLAP systems typically employ multidimensional data models to structure their data. This paper identifies 11 modeling requirements for multidimensional data models. These requirements are derived from an assessment of complex data found in real-world applications. A survey of 14 multidimensional data models reveals shortcomings in meeting some of the requirements. Existing models do not support many-to-many relationships between facts and dimensions, lack built-in mechanisms for handling change and time, lack support for imprecision, and are generally unable to insert data with varying granularities. This paper defines an extended multidimensional data model and algebraic query language that address all 11 requirements. The model reuses the common multidimensional concepts of dimension hierarchies and granularities to capture imprecise data. For queries that cannot be answered precisely due to the imprecise data, techniques are proposed that take into account the imprecision in the grouping of the data, in the subsequent aggregate computation, and in the presentation of the imprecise result to the user. In addition, alternative queries unaffected by imprecision are offered. The data model and query evaluation techniques discussed in this paper can be implemented using relational database technology. The approach is also capable of exploiting multidimensional query processing techniques like pre-aggregation. This yields a practical solution with low computational overhead.     

Interval-based temporal functional dependencies: specification and verification

In the temporal database literature, every fact stored in a database may be equipped with two temporal dimensions: the valid time, which describes the time when the fact is true in the modeled reality, and the transaction time, which describes the time when the fact is current in the database and can be retrieved. Temporal functional dependencies (TFDs) add valid time to classical functional dependencies (FDs) in order to express database integrity constraints over the flow of time. Currently, proposals dealing with TFDs adopt a point-based approach, where tuples hold at specific time points, to express integrity constraints such as “for each month, the salary of an employee depends only on his role”. To the best of our knowledge, there are no proposals dealing with interval-based temporal functional dependencies (ITFDs), where the associated valid time is represented by an interval and there is the need of representing both point-based and interval-based data dependencies. In this paper, we propose ITFDs based on Allen’s interval relations and discuss their expressive power with respect to other TFDs proposed in the literature: ITFDs allow us to express interval-based data dependencies, which cannot be expressed through the existing point-based TFDs. ITFDs allow one to express constraints such as “employees starting to work the same day with the same role get the same salary” or “employees with a given role working on a project cannot start to work with the same role on another project that will end before the first one”. Furthermore, we propose new algorithms based on B-trees to efficiently verify the satisfaction of ITFDs in a temporal database. These algorithms guarantee that, starting from a relation satisfying a set of ITFDs, the updated relation still satisfies the given ITFDs.     

Modeling temporal dimensions of semistructured data

In this paper we propose a graph-based generic model able to uniformly represent semistructured data and their temporal aspects. In particular, we start from a generic and expressive model proposed in the database literature and consider in a formal and systematic way both valid time and transaction time, together with the set of temporal constraints needed to correctly manage the semantics of the represented time dimension. We then propose operations, which allow the incremental management of the proposed model satisfying the introduced temporal constraints. Moreover, we also deal with the possibility of managing together the two classical time dimensions of valid and transaction times, and formalize the set of constraints needed to correctly handle these temporal aspects together. Some examples taken from a medical scenario will be used to describe the introduced concepts.     

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     

Towards an integration of time and causation in a hybrid knowledge representation formalism

The article focuses on the relations between time and causation and proposes a general framework in which a strict integration is achieved both in the representation formalism and in the reasoning process. By taking advantage of the basic capabilities of a Hybrid Knowledge Representation formalism (in particular, we use BACK), we provide an explicit and integrated representation of the basic entities in the temporal and causal ontology, and propose a taxonomy in which different types of causal relations found in the literature are classified, depending on the temporal relations they impose between causes and effects. Moreover, we develop a specialized causal-temporal reasoner which operates on causal nets built upon the basic causal-temporal representation. Such a reasoner is flexible (in the sense that different definitions of causation may be considered in the same reasoning process) and integrated (since the mutual effects of time and causation are coped with), and takes advantage of a specialized temporal reasoner. © 1994 John Wiley & Sons, Inc.     

一种基于时态中间件的高效双时态索引模型

当前的时态数据库中间件不支持包含事务时间和有效时间的双时态数据索引,通过使用适当的数据变换和查询变换,可将双时态数据转化为R树可索引的数据.基于4R技术,提出了作为时态中间件TimeDB组件的双时态索引模型B4Rindex.实验证明,利用该模型对双时态数据进行索引是高效的.     

Accurate and efficient cross-domain visual matching leveraging multiple feature representations

Cross-domain visual matching aims at finding visually similar images across a wide range of visual domains, and has shown a practical impact on a number of applications. Unfortunately, the state-of-the-art approach, which estimates the relative importance of the single feature dimensions still suffers from low matching accuracy and high time cost. To this end, this paper proposes a novel cross-domain visual matching framework leveraging multiple feature representations. To integrate the discriminative power of multiple features, we develop a data-driven, query specific feature fusion model, which estimates the relative importance of the individual feature dimensions as well as the weight vector among multiple features simultaneously. Moreover, to alleviate the computational burden of an exhaustive subimage search, we design a speedup scheme, which employs hyperplane hashing for rapidly collecting the hard-negatives. Extensive experiments carried out on various matching tasks demonstrate that the proposed approach outperforms the state-of-the-art in both accuracy and efficiency.     

Designing access methods for bitemporal databases

By supporting the valid and transaction time dimensions, bitemporal databases represent reality more accurately than conventional databases. The authors examine the issues involved in designing efficient access methods for bitemporal databases, and propose the partial-persistence and the double-tree methodologies. The partial-persistence methodology reduces bitemporal queries to partial persistence problems for which an efficient access method is then designed. The double-tree methodology “sees” each bitemporal data object as consisting of two intervals (a valid-time and a transaction-time interval) and divides objects into two categories according to whether the right endpoint of the transaction time interval is already known. A common characteristic of both methodologies is that they take into account the properties of each time dimension. Their performance is compared with a straightforward approach that “sees” the intervals associated with a bitemporal object as composing one rectangle, which is stored in a single multidimensional access method. Given that some limited additional space is available, the experimental results show that the partial-persistence methodology provides the best overall performance, especially for transaction timeslice queries. For those applications that require ready, off-the-shelf, access methods, the double-tree methodology is a good alternative     

Rule-based composite event queries: the language XChangeEQ and its semantics

Web systems, Web services, and Web-based publish/subscribe systems communicate events as XML messages and in many cases, require composite event detection: it is not sufficient to react to single event messages, but events have to be considered in relation to other events that are received over time. This entails a need for expressive, high-level languages for querying composite events. Emphasizing language design and formal semantics, we describe the rule-based composite event query language XChange^EQ. XChange^EQ is designed to completely cover and integrate the four complementary querying dimensions: event data, event composition, temporal relationships, and event accumulation. Semantics are provided as a model theory with accompanying fixpoint theory, an approach that is established for rule languages but has not been applied to event queries so far. Because they are highly declarative, thus easy to understand and well suited for query optimization, such semantics are desirable for event queries.     

Bounding the effects of compensation under relaxed multi-level serializability

The multi-level transaction concept provides a powerful tool for structuring activities in multidatabase systems. However, even multi-level serializability is sometimes too restrictive as a correctness criterion, either because of very high concurrency requirements, or because of the practical difficulties of implementing a scheduler in actual production environments. The extended multi-level transaction model presented in this paper supports higher concurrency in cases where higher level operations commute in one direction, but not in the other-i.e., when it is valid to interchange them when they occur in one order in a history, but not when they occur in the other order. We introduce a relaxed correctness criterion based on allowing a bounded number of out of order conflicts at each level in the multi-level framework, where the bound can be different for different levels. Finally we discuss the properties of compensation in this framework, developing a theory of compensation which depends only on the semantics of the operations and not on the particular state of the database. We illustrate the use of these concepts in the context of a particular class of practical applications. Recommended by: Tamer ÖzsuThis work was supported in part by MCC, Bellcore, and by the Texas Advanced Research Program under Grant No. 3652008. Majority of Sheth's work was performed at Bellcore.     

一种面向不完整数据流上的k-支配skyline查询算法

skyline查询是数据挖掘一个重要的研究方向,在基于数据的决策支持等应用中有着重要的作用.由于现实应用中存在着大量的不完整数据流,但大多数现有的skyline查询算法都依赖于如下的假设:1)任意数据点的所有维度值都是已知的;2)数据集是稳定、有界的并且可以随意访问.此外,随着数据维度的增加,skyline数据点的个数会变得过多,因此引入了k-支配skyline的概念,但是不完整数据的k-支配关系并不具有传递性,现有的skyline查询算法都无法适用.基于这些问题,考虑到数据流高维、无界、顺序性的特点,并且在某些维度上可能具有缺失值的特性,提出了一种新的基于滑动窗口的不完整数据流的k-支配skyline查询算法,实验结果表明,算法不仅可以支持不完整数据流上的k-支配skyline计算,并能够保证效率和性能.     

时态XML索引技术

首先通过讨论时态XML查询数据模型TXQDM,提出了基于结点有效时间的前缀编码方案.以此为基础,引入TXQDM结点间的基于时态连通的等价关系和基于时态包含的拟序关系,建立了时态XML索引数据模型TXIDM,该模型的基本特征是具有二重嵌套的索引框架,适合于TXQDM这种不规则的具有较大随意性的树形结构情形.其次,在TXIDM框架内,讨论了相应时态查询算法,其中包括基于时态的路径查询和值查询,同时,还讨论了时态索引更新算法,其中包括插入和修改算法.最后,对于文中提出的模型TXIDM和时态索引操作算法进行了性能分析且设计了相应模拟实验.实验结果表明,基于TXIDM的时态查询与更新算法是可行的和有效的.