Comparing Apples and Oranges: Taxonomy and Design of Pairwise Comparisons within Tabular Data

摘要

Asking pairwise comparison questions is common. Yet, we often find ourselves comparing apples and oranges -- the two entities of interest are not readily comparable. To understand how technologies can extend our capabilities to conduct pairwise comparisons during data analysis, we analyzed pairwise comparison questions collected from crowd workers and propose a taxonomy of pairwise comparisons. We demonstrate how the taxonomy can be adopted by incorporating pairwise comparison capabilities into Duo, a spreadsheet application that supports comparing two groups of records in a data table. Duo decomposes a pairwise comparison question into rules and showcases sloppy rules, a query technique for specifying pairwise comparisons. We conducted a user study comparing sloppy rules and natural language. The findings suggest that for easier pairwise comparison tasks, the two techniques are comparable in efficiency and preference and that for more difficult pairwise comparison tasks, sloppy rules allow faster specification and are more preferable.