Erica: Query Refinement for Diversity Constraint Satisfaction

Jinyang Li, Alon Silberstein, Yuval Moskovitch, Julia Stoyanovich, H. V. Jagadish

Research output: Contribution to journalConference articlepeer-review

Abstract

Relational queries are commonly used to support decision making in critical domains like hiring and college admissions. For example, a college admissions officer may need to select a subset of the applicants for in-person interviews, who individually meet the qualification requirements (e.g., have a sufficiently high GPA) and are collectively demographically diverse (e.g., include a sufficient number of candidates of each gender and of each race). However, traditional relational queries only support selection conditions checked against each input tuple, and they do not support diversity conditions checked against multiple, possibly overlapping, groups of output tuples. To address this shortcoming, we present Erica, an interactive system that proposes minimal modifications for selection queries to have them satisfy constraints on the cardinalities of multiple groups in the result. We demonstrate the effectiveness of Erica using several real-life datasets and diversity requirements.

Original languageEnglish
Pages (from-to)4070-4073
Number of pages4
JournalProceedings of the VLDB Endowment
Volume16
Issue number12
DOIs
StatePublished - 1 Jan 2023
Event49th International Conference on Very Large Data Bases, VLDB 2023 - Vancouver, Canada
Duration: 28 Aug 20231 Sep 2023

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • General Computer Science

Fingerprint

Dive into the research topics of 'Erica: Query Refinement for Diversity Constraint Satisfaction'. Together they form a unique fingerprint.

Cite this