Accepted to SIGMOD 2018: “A Query Engine for Probabilistic Preferences” by Uzi Cohen, Batya Kenig, Haoyue Ping, Benny Kimelfeld, and Julia Stoyanovich
The paper was accepted to the 2018 SIGMOD conference.
Lab members
Papers
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A Query Engine for Probabilistic Preferences
SIGMOD Conference 2018: 1509-1524
Abstract
Models of uncertain preferences, such as Mallows, have been extensively studied due to their plethora of application domains. In a recent work, a conceptual and theoretical framework has been proposed for supporting uncertain preferences as first-class citizens in a relational database. The resulting database is probabilistic, and, consequently, query evaluation entails inference of marginal probabilities of query answers. In this paper, we embark on the challenge of a practical realization of this framework. We first describe an implementation of a query engine that supports querying probabilistic preferences alongside relational data. Our system accommodates preference distributions in the general form of the Repeated Insertion Model (RIM), which generalizes Mallows and other models. We then devise a novel inference algorithm for conjunctive queries over RIM, and show that it significantly outperforms the state of the art in terms of both asymptotic and empirical execution cost. We also develop performance optimizations that are based on sharing computation among different inference tasks in the workload. Finally, we conduct an extensive experimental evaluation and demonstrate that clear performance benefits can be realized by a query engine with built-in probabilistic inference, as compared to a stand-alone implementation with a black-box inference solver.