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Optimization of Nonsequenced Queries Using Log-Segmented Timestamps

Author Curtis E. Dyreson

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Curtis E. Dyreson
  • Department of Computer Science, Utah State University, Logan, UT, USA

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Curtis E. Dyreson. Optimization of Nonsequenced Queries Using Log-Segmented Timestamps. In 30th International Symposium on Temporal Representation and Reasoning (TIME 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 278, pp. 13:1-13:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.TIME.2023.13

Abstract

In a period-timestamped, relational temporal database, each tuple is timestamped with a period. The timestamp records when the tuple is "alive" in some temporal dimension. Nonsequenced semantics is a query evaluation semantics that involves adding temporal predicates and constructors to a query. We show how to use log-segmented timestamps to improve the efficiency of temporal, nonsequenced queries evaluated using a non-temporal DBMS, i.e., a DBMS that has no special temporal indexes or query evaluation operators. A log-segmented timestamp divides the time-line into segments of known length. Any temporal period can be represented by a small number of such segments. The segments can be appended to a relation as additional columns. The advantage of log-segmented timestamps is that each segment can be indexed using standard database indexes, e.g., a B^+-tree. A query optimizer can use the indexes to generate a lower cost query evaluation plan. This paper shows how to rewrite a query to use the additional columns and evaluates the time cost benefits and space cost disadvantages.

Subject Classification

ACM Subject Classification
  • Information systems → Temporal data
  • Information systems → Relational database query languages
Keywords
  • Temporal databases
  • nonsequenced semantics
  • query evaluation
  • query performance

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References

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