Database queries are often used to select and rank items as decision support for many applications. As automated decision-making tools become more prevalent, there is a growing recognition of the need to diversify their outcomes. In this paper, we define and study the problem of modifying the selection conditions of an ORDER BY query so that the result of the modified query closely fits some user-defined notion of diversity while simultaneously maintaining the intent of the original query. We show the hardness of this problem and propose a mixed-integer linear programming (MILP) based solution. We further present optimizations designed to enhance the scalability and applicability of the solution in real-life scenarios. We investigate the performance characteristics of our algorithm and show its efficiency and the usefulness of our optimizations.

Database queries are commonly used to select and rank items. With the increasing awareness of diversity, ensuring a diverse output (i.e., the representation of different groups in the top- k positions) becomes essential. To address this challenge, we present Rodeo, a system that generates minimal modifications to queries to enhance the diversity of the ranking they produce based on constraints over groups' representation in the top- k for various k values.

We introduce historical what-if queries, a novel type of what-if analysis that determines the effect of a hypothetical change to the transactional history of a database. For example, "how would revenue be affected if we would have charged an additional $6 for shipping?" We develop efficient techniques for answering historical what-if queries, i.e., determining how a modified history affects the current database state. Our techniques are based on reenactment, a replay technique for transactional histories. We optimize this process using program and data slicing techniques that determine which updates and what data can be excluded from reenactment without affecting the result. Using an implementation of our techniques in Mahif (a Middleware for Answering Historical what-IF queries) we demonstrate their effectiveness experimentally.