Trying to bridge the gap between skyline and top-k queries

TL;DR

This paper introduces three novel approaches—Flexible Skyline, ORD-ORU, and UTK—that aim to combine the advantages of skyline and top-k queries, addressing personalization, output size control, and flexible preference specification.

Contribution

It proposes three new methods that bridge the gap between skyline and top-k queries, enhancing flexibility and user preference accommodation.

Findings

Flexible Skyline improves personalization and control.

ORD-ORU offers flexible preference modeling.

UTK balances ranking accuracy with user preferences.

Abstract

There are two most common paradigms that are used in order to identify records of preference in a multi-objective settings, one relies on dominance, like the skyline operator, the other instead, on a utility function defined over the records' attributes, typically using top-k queries. Although they are very popular, we have to take in account their main disadvantages, which bring us to describe three hard requirements: personalization, controllable output size, and flexibility in preference specification. In fact Skyline queries are simple to specify but they are not equipped with any means to accommodate user preferences or to control the cardinality of the result set. Ranking queries adopt, instead, a specific scoring function to rank tuples, and can easily control the output size, but it is difficult to specify correctly the weights of this scoring function in order to give different importance to the attributes. In this paper we describe three different approaches which try to satisfy the three hard requirements mentioned above embracing the advantages either of the Skyline queries or of the ranking queries. These approaches are namely: Flexible Skyline, ORD-ORU and UTK.