OSCAR: A Semantic-based Data Binning Approach

TL;DR

OSCAR introduces a semantic-aware binning method that automatically selects data bins based on inferred semantic types, improving user preference over traditional statistical binning in visualizations.

Contribution

The paper presents OSCAR, a novel semantic-based binning approach leveraging survey and visualization data to enhance data categorization and visualization effectiveness.

Findings

Users prefer OSCAR-generated bins over traditional statistical bins.

OSCAR effectively infers semantic categories for data binning.

Crowdsourced study validates improved user satisfaction with OSCAR.

Abstract

Binning is applied to categorize data values or to see distributions of data. Existing binning algorithms often rely on statistical properties of data. However, there are semantic considerations for selecting appropriate binning schemes. Surveys, for instance, gather respondent data for demographic-related questions such as age, salary, number of employees, etc., that are bucketed into defined semantic categories. In this paper, we leverage common semantic categories from survey data and Tableau Public visualizations to identify a set of semantic binning categories. We employ these semantic binning categories in OSCAR: a method for automatically selecting bins based on the inferred semantic type of the field. We conducted a crowdsourced study with 120 participants to better understand user preferences for bins generated by OSCAR vs. binning provided in Tableau. We find that maps and histograms using binned values generated by OSCAR are preferred by users as compared to binning schemes based purely on the statistical properties of the data.