Ranking Methods for Skyline Queries

TL;DR

This paper introduces new ranking methods for Skyline queries, including RankSky, CoSky, and DeepSky, improving scoring, efficiency, and integration in database systems for multi-criteria decision analysis.

Contribution

It proposes three novel ranking methods—RankSky, CoSky, and DeepSky—for Skyline queries, enhancing scoring, efficiency, and embedding capabilities in database management systems.

Findings

RankSky uses PageRank-inspired algorithm for Skyline ranking.

CoSky employs TOPSIS with Gini index for attribute weighting.

DeepSky combines multiple methods for improved Skyline analysis.

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 lacks efficiency and does not ensure a distinctive rank, we introduce the RankSky method, the adaptation of Google's well-known PageRank solution, using a square stochastic matrix, a teleportation matrix, a damping factor, and then a row score eigenvector and the IPL algorithm. For the same reasons as RankSky, and also to offer directly embeddable in DBMS solution, we establish the TOPSIS based CoSky method, derived from both information research and multi-criteria analysis. CoSky 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 dp-idp, RankSky or CoSky, we introduce DeepSky. Implementations of dp-idp, RankSky and CoSky are evaluated experimentally.