Typing Regular Path Query Languages for Data Graphs

TL;DR

This paper introduces a schema language for edge-labelled data graphs and a query type inference method, enhancing optimization and simplifying application development for regular path query languages.

Contribution

It proposes a simple, expressive schema language and a type inference approach that improves query optimization and development in data graph query languages.

Findings

Schema language enhances query optimization

Type inference improves query precision

Facilitates application development

Abstract

Regular path query languages for data graphs are essentially \emph{untyped}. The lack of type information greatly limits the optimization opportunities for query engines and makes application development more complex. In this paper we discuss a simple, yet expressive, schema language for edge-labelled data graphs. This schema language is, then, used to define a query type inference approach with good precision properties.