Reconciling point-based and interval-based semantics in temporal relational databases: a treatment of the telic/atelic distinction

Terenziani, Paolo; Snodgrass, Richard T.

doi:10.1109/tkde.2004.1277816

Cited by 57 publications

(51 citation statements)

References 32 publications

(112 reference statements)

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“…We use the terminology from this work, propose an implementation, and extend the work to scale attribute values in aggregate functions, grouping, set operations, and join conditions. Terenziani and Snodgrass [2004] distinguish between atelic facts that are valid for each point in time and telic facts that are only valid for one specific interval. That work focuses on the semantics of facts recorded in a database and proposes a three-sorted relational model (atelic, telic, nontemporal).…”

Section: Xxxx:41mentioning

confidence: 99%

Extending the Kernel of a Relational DBMS with Comprehensive Support for Sequenced Temporal Queries

Dignös

Böhlen

Gamper

et al. 2016

ACM Trans. Database Syst.

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Many databases contain temporal, or time-referenced, data and use intervals to capture the temporal aspect. While SQL-based database management systems (DBMSs) are capable of supporting the management of interval data, the support they offer can be improved considerably. A range of proposed temporal data models and query languages offer ample evidence to this effect. Natural queries that are very difficult to formulate in SQL are easy to formulate in these temporal query languages. The increased focus on analytics over historical data where queries are generally more complex exacerbates the difficulties and thus the potential benefits of a temporal query language. Commercial DBMSs have recently started to offer limited temporal functionality in a step-by-step manner, focusing on the representation of intervals and neglecting the implementation of the query evaluation engine. This article demonstrates how it is possible to extend the relational database engine to achieve a full-fledged, industrial-strength implementation of sequenced temporal queries, which intuitively are queries that are evaluated at each time point. Our approach reduces temporal queries to nontemporal queries over data with adjusted intervals, and it leaves the processing of nontemporal queries unaffected. Specifically, the approach hinges on three concepts: interval adjustment, timestamp propagation, and attribute scaling. Interval adjustment is enabled by introducing two new relational operators, a temporal normalizer and a temporal aligner, and the latter two concepts are enabled by the replication of timestamp attributes and the use of so-called scaling functions. By providing a set of reduction rules, we can transform any temporal query, expressed in terms of temporal relational operators, to a query expressed in terms of relational operators and the two new operators. We prove that the size of a transformed query is linear in the number of temporal operators in the original query. An integration of the new operators and the transformation rules, along with query optimization rules, into the kernel of PostgreSQL is reported. Empirical studies with the resulting temporal DBMS are covered that offer insights into pertinent design properties of the article's proposal. Many databases contain temporal, or time-referenced, data and use intervals to capture the temporal aspect. While SQL-based database management systems (DBMSs) are capable of supporting the management of interval data, the support they offer can be improved considerably. A range of proposed temporal data models and query languages offer ample evidence to this effect. Natural queries that are very difficult to formulate in SQL are easy to formulate in these temporal query languages. The increased focus on analytics over historical data where queries are generally more complex exacerbates the difficulties and thus the potential benefits of a temporal query language. Commercial DBMSs have recently started to offer limited temporal functionality in a step-by-step manner, ...

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Section: Xxxx:41mentioning

confidence: 99%

Extending the Kernel of a Relational DBMS with Comprehensive Support for Sequenced Temporal Queries

Dignös

Böhlen

Gamper

et al. 2016

ACM Trans. Database Syst.

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“…Think for example to temporal databases [15,16], where the temporal attributes (e.g., valid time, event time, etc.) are inherently nonpunctual, and, in some cases, it makes no sense to reduce the intervals to the set of its points, as shown, for example, in [17].…”

Section: Motivationmentioning

confidence: 99%

Reasoning with Time Intervals: A Logical and Computational Perspective

Sciavicco

2012

ISRN Artificial Intelligence

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The role of time in artificial intelligence is extremely important. Interval-based temporal reasoning can be seen as a generalization of the classical point-based one, and the first results in this field date back to Hamblin (1972) and Benhtem (1991) from the philosophical point of view, to Allen (1983) from the algebraic and first-order one, and to Halpern and Shoham (1991) from the modal logic one. Without purporting to provide a comprehensive survey of the field, we take the reader to a journey through the main developments in modal and first-order interval temporal reasoning over the past ten years and outline some landmark results on expressiveness and (un)decidability of the satisfiability problem for the family of modal interval logics.

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“…In this perspective, user-defined constraints for VT PSTDM graphs could allow one to represent different kinds of temporal information, as those proposed, for example, in Terenziani and Snodgrass (2004), where the distinction of point-based vs interval-based semantics when associating a fact to a temporal dimension is discussed within the context of temporal databases. A more refined classification of temporal propositions according to their temporal features has been proposed in Shoham (1987) and widely considered in the AI area.…”

Section: Constraints For Valid Timementioning

confidence: 99%

Modeling temporal dimensions of semistructured data

2011

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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.

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Reconciling point-based and interval-based semantics in temporal relational databases: a treatment of the telic/atelic distinction

Cited by 57 publications

References 32 publications

Extending the Kernel of a Relational DBMS with Comprehensive Support for Sequenced Temporal Queries

Extending the Kernel of a Relational DBMS with Comprehensive Support for Sequenced Temporal Queries

Reasoning with Time Intervals: A Logical and Computational Perspective

Modeling temporal dimensions of semistructured data

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