Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2021

Abstract

The two most common paradigms to identify records of preference in a multi-objective setting rely either on dominance (e.g., the skyline operator) or on a utility function defined over the records’ attributes (typically, using a top-�� query). Despite their proliferation, each of them has its own palpable drawbacks. Motivated by these drawbacks, we identify three hard requirements for practical decision support, namely, personalization, controllable output size, and flexibility in preference specification. With these requirements as a guide, we combine elements from both paradigms and propose two new operators, ORD and ORU. We perform a qualitative study to demonstrate how they work, and evaluate their performance against adaptations of previous work that mimic their output.

Keywords

Top-�� query, Skyline, Multi-dimensional datasets

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 2021 International Conference on Management of Data, SIGMOD, China, June 20-25

First Page

1317

Last Page

1330

ISBN

9781450383431

Identifier

10.1145/3448016.3457299

Publisher

ACM

City or Country

Virtual Conference

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