Language-Integrated Query for Temporal Data (Extended version)

TL;DR

This paper extends language-integrated query to support temporal data operations, enabling developers to write temporal queries in a uniform language that automatically rewrites them for standard relational databases.

Contribution

It introduces two core languages for temporal data manipulation, formalizes their semantics, and demonstrates implementation within the Links language with a practical case study.

Findings

Supports automatic rewriting of temporal queries into standard SQL

Provides formal semantics for temporal query languages

Successfully implements and tests in a real-world case study

Abstract

Modern applications often manage time-varying data. Despite decades of research on temporal databases, which culminated in the addition of temporal data operations into the SQL:2011 standard, temporal data query and manipulation operations are unavailable in most mainstream database management systems, leaving developers with the unenviable task of implementing such functionality from scratch. In this paper, we extend \emph{language-integrated query} to support writing temporal queries and updates in a uniform host language, with the language performing the required rewriting to emulate temporal capabilities automatically on any standard relational database. We introduce two core languages, $\lambda_{\mathsf{TLINQ}}$ and $\lambda_{\mathsf{VLINQ}}$, for manipulating transaction time and valid time data respectively, and formalise existing implementation strategies by giving provably correct semantics-preserving translations into a non-temporal core language, $\lambda_{\mathsf{LINQ}}$. We show how existing work on query normalisation supports a surprisingly simple implementation strategy for \emph{sequenced joins}. We implement our approach in the Links programming language, and describe a non-trivial case study based on curating COVID-19 statistics.