3,721 research outputs found
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 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
TEMPOS: A Platform for Developing Temporal Applications on Top of Object DBMS
This paper presents TEMPOS: a set of models and languages supporting the manipulation of temporal data on top of object DBMS. The proposed models exploit object-oriented technology to meet some important, yet traditionally neglected design criteria related to legacy code migration and representation independence. Two complementary ways for accessing temporal data are offered: a query language and a visual browser. The query language, namely TempOQL, is an extension of OQL supporting the manipulation of histories regardless of their representations, through fully composable functional operators. The visual browser offers operators that facilitate several time-related interactive navigation tasks, such as studying a snapshot of a collection of objects at a given instant, or detecting and examining changes within temporal attributes and relationships. TEMPOS models and languages have been formalized both at the syntactical and the semantical level and have been implemented on top of an object DBMS. The suitability of the proposals with regard to applications' requirements has been validated through concrete case studies
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
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