Expressive power of temporal relational query languages and temporal completeness

Tansel, Abdullah Uz; Tin, Erkan

doi:10.1007/bfb0053701

Cited by 7 publications

(13 citation statements)

References 15 publications

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“…For instance, (Bohlen et al 1996) discusses a relationship between TSQL2 and Temporal Logic, (Toman and Niwinski 1996) describes the class of first order queries that cannot be expressed in Temporal Logic, (Toman 1996) compares point-based vs. interval-based query languages, and (Baudinet et al 1993) surveys some languages regarding infinite temporal extensions. Other works addressing expressiveness issues include (McKenzie and Snodgrass 1989;Tansel and Tin 1998;Cobo and Augusto 1999). However, we believe that some important issues remain overlooked.…”

Section: Introductionmentioning

confidence: 91%

“…It turns out that because intervals have to be "programmed" ad-hoc in Templog by using the language primitives, maximal intervals cannot be feasibly enforced (usually known as coalescing (Tansel et al 1993)). We have seen that, since explicit time-references are not supported, intervals could be represented by predicates asserting the occurrence of bounding events.…”

Section: Coalescing In Templogmentioning

confidence: 99%

“…TSQL2 is a temporal relational query language, based on the SQL-92 standard (Tansel et al 1993). It supports user-defined, valid and transaction times.…”

Section: Language Overviewmentioning

confidence: 99%

“…TQuel (Snodgrass 1987;Tansel et al 1993) is a minimal extension to Quel, the relational query language for the system Ingres (Stonebraker et al 1976). It supports user-defined, valid and transaction times.…”

Section: Tsql2 and Tquelmentioning

confidence: 99%

“…Temporal databases has recently become an active research area in computer science (Tansel et al 1993;Etzioni et al 1998;Morris and Khatib 1999;Goodwin and Trudel 2000;Bettini and Montanari 2001;Artale et al 2002;Reynolds and Sattar 2003)). This kind of databases offers the possibility to associate time to data and to deal with it in a way that non-temporal systems cannot handle, or in a much more convenient way.…”

Section: Introductionmentioning

confidence: 99%

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Expressiveness of temporal query languages: on the modelling of intervals, interval relationships and states

Gómez

Augusto

2006

Artif Intell Rev

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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: Templog in the context of temporal deductive databases, and 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.

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Section: Introductionmentioning

confidence: 91%

Section: Coalescing In Templogmentioning

confidence: 99%

“…TSQL2 is a temporal relational query language, based on the SQL-92 standard (Tansel et al 1993). It supports user-defined, valid and transaction times.…”

Section: Language Overviewmentioning

confidence: 99%

Section: Tsql2 and Tquelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Expressiveness of temporal query languages: on the modelling of intervals, interval relationships and states

Gómez

Augusto

2006

Artif Intell Rev

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Temporal ER Modeling with Description Logics

Artale¹,

Franconi

1999

Lecture Notes in Computer Science

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Reasoning with enhanced temporal entity-relationship models

Artale

Franconi

1999

Proceedings. Tenth International Workshop on Database and Expert Systems Applications. DEXA 99

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Recent efforts in the Conceptual Modelling community have been devoted to properly capturing time-varying information, and several proposals of temporally enhanced Entity-Relationship (ER) exist. This work gives a logical formalisation of the various properties that characterise and extend different temporal ER models which are found in literature. The formalisation we propose is based on Description Logics (DL), which have been proved useful for a logical reconstruction of the most popular conceptual data modelling formalisms. The proposed DL has the ability to express both enhanced temporal ER schemas and integrity constraints in the form of complex inclusion dependencies. Reasoning in the devised logic is decidable, thus allowing for automated deductions over the whole conceptual representation, which includes both the ER schema and the integrity constraints over it.

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Expressive power of temporal relational query languages and temporal completeness

Cited by 7 publications

References 15 publications

Expressiveness of temporal query languages: on the modelling of intervals, interval relationships and states

Expressiveness of temporal query languages: on the modelling of intervals, interval relationships and states

Temporal ER Modeling with Description Logics

Reasoning with enhanced temporal entity-relationship models

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