Valid-time indeterminacy

Dyreson, Curtis E.; Snodgrass, Richard T.

doi:10.1109/icde.1993.344048

Cited by 34 publications

(11 citation statements)

References 30 publications

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“…For instance, we may know that an event happened "sometime in June 1993," which is an imprecise period of 30 days. An indeterminate instant is the time of an event, which is known to have occurred, but exactly when is unknown [Dyreson and Snodgrass 1993b;.…”

Section: Indeterminate Timestamp Valuesmentioning

confidence: 99%

On the semantics of “now” in databases

Clifford

Dyreson

Isakowitz

et al. 1997

ACM Trans. Database Syst.

129

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Although “ now ” is expressed in SQL and CURRENT_TIMESTAMP within queries, this value cannot be stored in the database. How ever, this notion of an ever-increasing current-time value has been reflected in some temporal data models by inclusion of database-resident variables, such as “ now ” “ until-changed, ” “**,” “@,” and “-”. Time variables are very desirable, but their used also leads to a new type of database, consisting of tuples with variables, termed a variable database.

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Section: Indeterminate Timestamp Valuesmentioning

confidence: 99%

On the semantics of “now” in databases

Clifford

Dyreson

Isakowitz

et al. 1997

ACM Trans. Database Syst.

129

View full text Add to dashboard Cite

show abstract

“…In many of such approaches, the application-builder is required to introduce customized functions with a detailed specification of how to obtain the desired conversion between (user-defined) granularities (Dyreson 1994). Other approaches adopt semantic assumptions or coercion functions in order to achieve such a goal.…”

Section: Temporal Granularitiesmentioning

confidence: 99%

“…For instance, in TSQL2 "consensus" approach (Snodgrass 1995), temporal granularities can be defined by users, and the query language includes a CAST function to convert between them. However, in most cases, no support is provided to the application builder, who is required to introduce customized functions with a detailed specification of how to obtain the desired conversions (Dyreson 1994). Other approaches adopt semantic assumptions or coercion functions in order to achieve such a goal (Bettini et al 2000).…”

Section: Introductionmentioning

confidence: 97%

Temporal aggregation on user-defined granularities

Terenziani

2011

J Intell Inf Syst

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Time-varying data play a major role in many applications, and, starting from the 80's, they have been widely studied in temporal databases. In the last two decades, several researchers have shown that, to deal with many application domains, user-def ined temporal granularities must be coped with. When data are stored at multiple user-defined temporal granularities, the task of defining proper conversion functions to aggregate data from an origin granularity (e.g., business days) to a task granularity (e.g., months) is of primary importance. However, current temporal database approaches mostly demand such a task to system administrators, or to specific applications, providing no methodology or general guideline to accomplish it. In this paper, we propose a general and application-independent methodology which, on the basis of the temporal relationship between two user-defined granularities, provides users with a set of conversion/aggregation functions between them, consistent with the telic vs. atelic character of the data to be aggregated. The correctness of the approach is also proved.

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“…Valid time is the real world time at which information recorded in the database becomes true (or no longer true) [10,11]. For example, J. Smith might have started work at X Corp. on May 1, 1994 and left it on Oct. 7,2001.…”

Section: Temporal Databasesmentioning

confidence: 99%