An Efficient SQL Rewrite Approach for Temporal Coalescing in the Teradata RDBMS

Al-Kateb, Mohammed; Ghazal, Ahmad; Crolotte, Alain

doi:10.1007/978-3-642-32597-7_32

Cited by 7 publications

(10 citation statements)

References 4 publications

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“…First, two rows, which are consecutive are inserted and after that, in the SELECT statement the P NORMALIZE function coalesces the corresponding rows, before the result is displayed. The implementation of temporal coalescing in Teradata is under way and is based upon the paper of Al-Kateb et al [1]. The approach uses higher analytic functions together with runtime partitioning.…”

Section: Teradata: Coalescingcontrasting

confidence: 40%

Modern Temporal Data Models: Strengths and Weaknesses

Petković

2015

Beyond Databases, Architectures and Structures

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Abstract. Time is generally a challenging task. All issues in relation to time can be better supported using a temporal data model than implementing them in user applications. More than two-dozen temporal data models have been introduced in time period between 1982 and 1998. After several years of stagnancy, the last couple of years brought the new revival of the topic and the emergence of new data models. Two temporal data models have been specified recently. The one is the SQL:2011 standard, published in 2011. The second one is from Teradata. In this article we present the temporal data model of the ANSI SQL standard on one side and the data model of an existing relational DBMS on the other. After that, we compare their support of several temporal concepts. Finally, we discuss strengths and weaknesses of both models and give suggestions for future extensions.

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Section: Teradata: Coalescingcontrasting

confidence: 40%

Modern Temporal Data Models: Strengths and Weaknesses

Petković

2015

Beyond Databases, Architectures and Structures

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“…Thus, as shown in lines 2-12 of Figure 12 we compute two aggregation functions grouping on mach, tstart, and t end . The example instance of table active contains two tuples belonging to the same group which also have the same period: (M1,10,1,5) and (M1, 20,1,5). Based on these two tuples we compute the pre-aggregated result (M1, 30,1,5).…”

Section: E3 Combining Split With Temporal Aggregationmentioning

confidence: 99%

“…The example instance of table active contains two tuples belonging to the same group which also have the same period: (M1,10,1,5) and (M1, 20,1,5). Based on these two tuples we compute the pre-aggregated result (M1, 30,1,5). Note that no matter what aggregation function we are computing, we always will also compute count since it is needed later in the implementation to determine intervals without results for aggregation with group-by.…”

Section: E3 Combining Split With Temporal Aggregationmentioning

confidence: 99%

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Snapshot semantics for temporal multiset relations

et al. 2019

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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.

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“…Note that coalescing in Teradata will be implemented in one of future versions, using analytical functions. The description of this feature can be found in [1].…”

Section: Teradata: Coalescingmentioning

confidence: 99%

Support of Temporal Data in Database Systems

Petković¹

2016

IJCA

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The time is generally a challenging task. All issues in relation to time can be better supported using temporal data models. Almost all enterprise database systems have implemented temporal data, partly according to the model specified in the SQL:2011 standard and partly according to other, older temporal models. In this article five temporal concepts will be used to investigate their implementations in enterprise database systems. Also, strengths and weaknesses of these implementations will be discussed.

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An Efficient SQL Rewrite Approach for Temporal Coalescing in the Teradata RDBMS

Cited by 7 publications

References 4 publications

Modern Temporal Data Models: Strengths and Weaknesses

Modern Temporal Data Models: Strengths and Weaknesses

Snapshot semantics for temporal multiset relations

Support of Temporal Data in Database Systems

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