DIG: The Data Interface Grammar

TL;DR

This paper introduces the Data Interface Grammar (DIG), a novel intermediate representation for analysis tasks that facilitates automatic data interface generation and optimization, bridging the gap between data processing needs and interface design.

Contribution

The paper proposes DIG as a new formalism for representing analysis tasks, enabling automatic interface creation and optimization in data engineering workflows.

Findings

DIG is compatible with existing data practices.

DIG can be translated into interface designs easily.

DIG enables automatic interface and workload generation.

Abstract

Building interactive data interfaces is hard because the design of an interface depends on the data processing needs for the underlying analysis task, yet we do not have a good representation for analysis tasks. To fill this gap, this paper advocates for a Data Interface Grammar (DIG) as an intermediate representation of analysis tasks. We show that DIG is compatible with existing data engineering practices, compact to represent any analysis, simple to translate into an interface design, and amenable to offline analysis. We further illustrate the potential benefits of this abstraction, such as automatic interface generation, automatic interface backend optimization, tutorial generation, and workload generation.