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
Citation
1
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.