Efficient Skyline Querying with Variable User Preferences on Nominal Attributes

TL;DR

This paper introduces two novel methods for efficient skyline querying that accommodate dynamic user preferences on nominal attributes, enhancing responsiveness and flexibility in practical applications.

Contribution

It proposes semi-materialization and adaptive SFS methods to handle variable user preferences in skyline queries, addressing limitations of fixed attribute orderings.

Findings

Both methods improve query response times.

Experimental results demonstrate higher efficiency over existing techniques.

The adaptive SFS method adapts to changing preferences effectively.

Abstract

Current skyline evaluation techniques assume a fixed ordering on the attributes. However, dynamic preferences on nominal attributes are more realistic in known applications. In order to generate online response for any such preference issued by a user, we propose two methods of different characteristics. The first one is a semi-materialization method and the second is an adaptive SFS method. Finally, we conduct experiments to show the efficiency of our proposed algorithms.