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Temporal Data Management – An Overview

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Business Intelligence and Big Data (eBISS 2017)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 324))

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Abstract

Despite the ubiquity of temporal data and considerable research on the effective and efficient processing of such data, database systems largely remain designed for processing the current state of some modeled reality. More recently, we have seen an increasing interest in the processing of temporal data that captures multiple states of reality. The SQL:2011 standard incorporates some temporal support, and commercial DBMSs have started to offer temporal functionality in a step-by-step manner, such as the representation of temporal intervals, temporal primary and foreign keys, and the support for so-called time-travel queries that enable access to past states.

This tutorial gives an overview of state-of-the-art research results and technologies for storing, managing, and processing temporal data in relational database management systems. Following an introduction that offers a historical perspective, we provide an overview of basic temporal database concepts. Then we survey the state-of-the-art in temporal database research, followed by a coverage of the support for temporal data in the current SQL standard and the extent to which the temporal aspects of the standard are supported by existing systems. The tutorial ends by covering a recently proposed framework that provides comprehensive support for processing temporal data and that has been implemented in PostgreSQL.

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Notes

  1. 1.

    To comply with the SQL:2011 standard, in this section we use closed-open intervals, whereas in the other sections we use closed-closed intervals.

  2. 2.

    To keep the examples simple, we use only the year, not complete dates or timestamps.

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Authors and Affiliations

  1. University of Zurich, Zurich, Switzerland

    Michael H. Böhlen

  2. Free University of Bozen-Bolzano, Bolzano, Italy

    Anton Dignös & Johann Gamper

  3. Aalborg University, Aalborg, Denmark

    Christian S. Jensen

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  1. Michael H. Böhlen
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  2. Anton Dignös
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  3. Johann Gamper
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  4. Christian S. Jensen
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Correspondence to Johann Gamper .

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    Esteban Zimányi

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Böhlen, M.H., Dignös, A., Gamper, J., Jensen, C.S. (2018). Temporal Data Management – An Overview. In: Zimányi, E. (eds) Business Intelligence and Big Data. eBISS 2017. Lecture Notes in Business Information Processing, vol 324. Springer, Cham. https://doi.org/10.1007/978-3-319-96655-7_3

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