# Precision Interfaces

**Authors:** Haoci Zhang, Thibault Sellam, Eugene Wu

arXiv: 1704.03022 · 2017-07-03

## TL;DR

Precision Interfaces is a semi-automatic system that generates task-specific data analytics interfaces from program logs by identifying variations and mapping them to UI components, streamlining the creation of interactive data tools.

## Contribution

The paper introduces a novel system that automatically constructs data analysis interfaces from logs, using a domain-specific language and transformation graph to simplify interface design.

## Key findings

- Successfully generates interfaces from SQL logs
- Maximizes coverage of query transformations with minimal UI components
- Prototype demonstrates practical applicability on real logs

## Abstract

Building interactive tools to support data analysis is hard because it is not always clear what to build and how to build it. To address this problem, we present Precision Interfaces, a semi-automatic system to generate task-specific data analytics interfaces. Precision Interface can turn a log of executed programs into an interface, by identifying micro-variations between the programs and mapping them to interface components. This paper focuses on SQL query logs, but we can generalize the approach to other languages. Our system operates in two steps: it first build an interaction graph, which describes how the queries can be transformed into each other. Then, it finds a set of UI components that covers a maximal number of transformations. To restrict the domain of changes to be detected, our system uses a domain-specific language, PILang. We give a full description of Precision Interface's components, showcase an early prototype on real program logs and discuss future research opportunities.

## Full text

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## Figures

16 figures with captions in the complete paper: https://tomesphere.com/paper/1704.03022/full.md

## References

26 references — full list in the complete paper: https://tomesphere.com/paper/1704.03022/full.md

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Source: https://tomesphere.com/paper/1704.03022