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


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

    ASJC Scopus subject areas

    • Computer Science (miscellaneous)
    • General Computer Science


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