Erica: Query Refinement for Diversity Constraint Satisfaction

IF 2.6 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI:10.14778/3611540.3611623
Jinyang Li, Alon Silberstein, Yuval Moskovitch, Julia Stoyanovich, H. V. Jagadish
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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.
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Erica:多样性约束满足的查询细化
关系查询通常用于支持招聘和大学录取等关键领域的决策制定。例如,大学招生官可能需要选择一部分申请人进行面对面面试,这些申请人个人符合资格要求(例如,有足够高的GPA),并且在人口统计学上具有多样性(例如,包括足够数量的每种性别和每种种族的候选人)。然而,传统的关系查询只支持针对每个输入元组检查的选择条件,而不支持针对多个(可能重叠的)输出元组检查的多样性条件。为了解决这个缺点,我们提出了Erica,这是一个交互式系统,它对选择查询提出了最小的修改,以使它们满足对结果中多个组的基数的约束。我们使用几个现实生活中的数据集和多样性要求来证明Erica的有效性。
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来源期刊
Proceedings of the Vldb Endowment
Proceedings of the Vldb Endowment Computer Science-General Computer Science
CiteScore
7.70
自引率
0.00%
发文量
95
期刊介绍: The Proceedings of the VLDB (PVLDB) welcomes original research papers on a broad range of research topics related to all aspects of data management, where systems issues play a significant role, such as data management system technology and information management infrastructures, including their very large scale of experimentation, novel architectures, and demanding applications as well as their underpinning theory. The scope of a submission for PVLDB is also described by the subject areas given below. Moreover, the scope of PVLDB is restricted to scientific areas that are covered by the combined expertise on the submission’s topic of the journal’s editorial board. Finally, the submission’s contributions should build on work already published in data management outlets, e.g., PVLDB, VLDBJ, ACM SIGMOD, IEEE ICDE, EDBT, ACM TODS, IEEE TKDE, and go beyond a syntactic citation.
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