Temporal Data Management – An Overview

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.

Graph data temporal evolutions: From conceptual modelling to implementation

“…Temporal data management involves all methods and techniques to model, query and store time-varying data (or temporal data) [5]. There is a vast literature on this topic since the 80's focusing on relational database management systems (RDBMS) [6].…”

Leveraging range joins for the computation of overlap joins

“…This is straightforward for discrete domains since all boundaries can be given explicitly so that all periods can be transformed into a uniform representation. In PostgreSQL, for instance, range types for integers (int4range) are internally converted to the default representation [B, E) by using predecessors and/or successor functions, e.g., [3,4] is converted to [3,5). Establishing a total order among period start and end points is more challenging for continuous domains, where predecessors and successors cannot be represented explicitly.…”

“…For the experiments with clustered indices, we cluster the index tables according to one of the indices. 5 https://www.postgresql.org/docs/10/static/sql-createtype.html. 6 https://www.postgresql.org/docs/10/static/sql-createopclass.html.…”

“…Temporal support in relational database systems has gained significant interest during the last years, witnessed by temporal features in the SQL:2011 standard [5,28,32] as well as by the numerous database products that offer selected temporal features, such as IBM DB2 [36], Oracle [31], Teradata [1], Microsoft SQL Server [30], and PostgreSQL [35].…”

Modeling and querying facts with period timestamps in data warehouses

“…Several decades of intensive research activities regarding temporal databases studied various aspects of representing and querying temporal data in database management systems. The research work concentrated on various data models and query languages (Jensen et al, 1994;Jensen and Snodgrass, 2009;Böhlen et al, 2009;Dignös et al, 2012; as well as evaluation algorithms for selected operators, such as temporal aggregation (e.g., Kline and Snodgrass, 1995;Zhang et al, 2001;Moon et al, 2003;Yang and Widom, 2003;Böhlen et al, 2006b;Piatov and Helmer, 2017) and temporal joins (e.g., Zhang et al, 2002;Gao et al, 2005;Piatov et al, 2016;Bouros and Mamoulis, 2017;Cafagna and Böhlen, 2017); for an overview, see the work of Böhlen et al (2018). Fundamental concepts that emerged in this research are the distinction between different time dimensions, e.g., valid time, when a fact is true in the modeled reality, and transaction time, when a fact has been stored in the database.…”