Marrying Top-k with Skyline Queries: Operators with Relaxed Preference Input and Controllable Output Size

• Main Authors: MOURATIDIS, Kyriakos, LI, Keming, TANG, Bo
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Top-k query; skyline; multi-dimensional datasets; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/9706; https://ink.library.smu.edu.sg/context/sis_research/article/10706/viewcontent/TODS25.pdf
• Institution: Singapore Management University

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-k 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.