137 research outputs found
Temporal Support in Relational Databases
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission. © 2012 Higher Education AcademyThis paper examines the current state of temporal support in relational databases and the type of situations where we need that support. There has been much research in this area and there were attempts in the American National Standards Institute (ANSI) and the International Organisation for Standardisation (ISO) standards committees in the late 1990s to add an extension called TSQL2 to the existing SQL standard. However no agreement could be reached as it was felt that some of the suggested extensions did not fit well with the relational model, as well as being difficult to implement. TSQL2 was abandoned and since then vendors have added their own data types, and if we are lucky, operators too in an attempt to provide support. However, to novice students and database designers it is often not apparent why some temporal concepts are difficult to deal with in a relational database. In teaching these concepts to students we use a Case Study (based on a real example) which illustrates the problems of providing temporal support by using examples of the data types which could be useful to solve temporal problems and the operators which are necessary to provide this
Expressiveness of Temporal Query Languages: On the Modelling of Intervals, Interval Relationships and States
Storing and retrieving time-related information are important, or even critical, tasks on many areas of Computer Science (CS) and in particular for Artificial Intelligence (AI). The expressive power of temporal databases/query languages has been studied from different perspectives, but the kind of temporal information they are able to store and retrieve is not always conveniently addressed. Here we assess a number of temporal query languages with respect to the modelling of time intervals, interval relationships and states, which can be thought of as the building blocks to represent and reason about a large and important class of historic information. To survey the facilities and issues which are particular to certain temporal query languages not only gives an idea about how useful they can be in particular contexts, but also gives an interesting insight in how these issues are, in many cases, ultimately inherent to the database paradigm. While in the area of AI declarative languages are usually the preferred choice, other areas of CS heavily rely on the extended relational paradigm. This paper, then, will be concerned with the representation of historic information in two well known temporal query languages: it Templog in the context of temporal deductive databases, and it TSQL2 in the context of temporal relational databases. We hope the results highlighted here will increase cross-fertilisation between different communities. This article can be related to recent publications drawing the attention towards the different approaches followed by the Databases and AI communities when using time-related concepts
Schema Vacuuming in Temporal Databases
Temporal databases facilitate the support of historical information by providing functions for indicating the intervals during which a tuple was applicable (along one or more temporal dimensions). Because data are never deleted, only superceded, temporal databases are inherently append-only resulting, over time, in a large historical sequence of database states. Data vacuuming in temporal databases allows for this sequence to be shortened by strategically, and irrevocably, deleting obsolete data. Schema versioning allows users to maintain a history of database schemata without compromising the semantics of the data or the ability to view data through historical schemata. While the techniques required for data vacuuming in temporal databases have been relatively well covered, the associated area of vacuuming schemata has received less attention. This paper discusses this issue and proposes a mechanism that fits well with existing methods for data vacuuming and schema versioning
On Data Representation and Use In A Temporal Relational DBMS
Numerous proposals for extending the relational data model to incorporate the temporal
dimension of data have appeared over the past decade. It has long been known that these
proposals have adopted one of two basic approaches to the incorporation of time into the
extended relational model. Recent work formally contrasted the expressive power of these two
approaches, termed temporally ungrouped and temporally grouped, and demonstrated that the
temporally grouped models are more expressive. IN the temporally ungrouped models, the
temporal dimension is added through the addition of some number of distinguished attributes to
the schema of each relation, and each tuple is "stamped" with temporal values for these attributes.
By contrast, in temporally grouped models the temporal dimension is added to the types of values
that serve as the domain of each ordinary attribute, and the application's schema is left intact.
The recent appearance of TSQL2, a temporal extension to the SQL-92 standard based upon the
temporally ungrouped paradigm, means that it is likely that commercial DBMS's will be extended
to support time in this weaker way. Thus the distinction between these two approaches - and its
impact on the day-to-day user of a DBMS - is of increasing relevance to the database practitioner
and the database user community. In this paper we address this issue from the practical
perspective of such a user. Through a series of example queries and updates, we illustrate the
differences between these two approaches and demonstrate that the temporally grouped approach
more adequately captures the semantics of historical data.Information Systems Working Papers Serie
ON PERIODICITY IN TEMPORAL DATABASES
The issue of periodicity is generally understood to be a desirable property of temporal
data that should be supported by temporal database models and their query
languages. Nevertheless, there has so far not been any systematic examination of how
to incorporate this concept into a temporal DBMS. In this paper we describe two concepts
of periodicity, which we call strong periodicity and near periodicity, and discuss
how they capture formally two of the intuitive meanings of this term. We formally
compare the expressive power of these two concepts, relate them to existing temporal
query languages, and show how they can be incorporated into temporal relational
database query languages, such as the proposed temporal extension to SQL, in a clean
and straightforward manner.Information Systems Working Papers Serie
Snapshot Semantics for Temporal Multiset Relations (Extended Version)
Snapshot semantics is widely used for evaluating queries over temporal data:
temporal relations are seen as sequences of snapshot relations, and queries are
evaluated at each snapshot. In this work, we demonstrate that current
approaches for snapshot semantics over interval-timestamped multiset relations
are subject to two bugs regarding snapshot aggregation and bag difference. We
introduce a novel temporal data model based on K-relations that overcomes these
bugs and prove it to correctly encode snapshot semantics. Furthermore, we
present an efficient implementation of our model as a database middleware and
demonstrate experimentally that our approach is competitive with native
implementations and significantly outperforms such implementations on queries
that involve aggregation.Comment: extended version of PVLDB pape
Supporting service discovery, querying and interaction in ubiquitous computing environments.
In this paper, we contend that ubiquitous computing environments will be highly heterogeneous, service rich domains. Moreover, future applications will consequently be required to interact with multiple, specialised service location and interaction protocols simultaneously. We argue that existing service discovery techniques do not provide sufficient support to address the challenges of building applications targeted to these emerging environments. This paper makes a number of contributions. Firstly, using a set of short ubiquitous computing scenarios we identify several key limitations of existing service discovery approaches that reduce their ability to support ubiquitous computing applications. Secondly, we present a detailed analysis of requirements for providing effective support in this domain. Thirdly, we provide the design of a simple extensible meta-service discovery architecture that uses database techniques to unify service discovery protocols and addresses several of our key requirements. Lastly, we examine the lessons learnt through the development of a prototype implementation of our architecture
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