Classement d'objets Skylines dans les bases de donn{\'e}es

TL;DR

This paper introduces CoSky and DeepSky methods for ranking Skyline points in databases, improving comparison relevance and integrating multi-criteria analysis with dominance hierarchies.

Contribution

It proposes CoSky, a novel ranking method based on TOPSIS and Gini index, and DeepSky, combining multilevel Skyline with CoSky for better result set analysis.

Findings

CoSky effectively ranks Skyline points using normalized attributes and cosine similarity.

DeepSky enhances Skyline analysis by integrating multilevel ranking.

Experimental results show improved comparison and ranking quality.

Abstract

Multi-criteria decision analysis in databases has been actively studied, especially through the Skyline operator. Yet, few approaches offer a relevant comparison of Pareto optimal, or Skyline, points for high cardinality result sets. We propose to improve the dp-idp method, inspired by tf-idf, a recent approach computing a score for each Skyline point, by introducing the concept of dominance hierarchy. As dp-idp does not ensure a distinctive rank, we introduce the TOPSIS based CoSky method, derived from both information research and multi-criteria analysis. CoSky, directly embeddable in DBMS, automatically ponderates normalized attributes using the Gini index, then computes a score using Salton's cosine toward an ideal point. By coupling multilevel Skyline to CoSky, we introduce DeepSky. CoSky and dp-idp implementations are evaluated experimentally.