ParetoPrep: Fast computation of Path Skylines Queries

TL;DR

ParetoPrep is a novel algorithm that efficiently computes path skyline queries in networks with multiple cost criteria, reducing processing time and memory without precomputation, suitable for dynamic edge costs.

Contribution

It introduces a new method for fast, memory-efficient path skyline computation that does not require precomputation or indexing, adaptable to changing network costs.

Findings

Outperforms existing methods in speed and memory usage

Effective for dynamic network scenarios without precomputation

Enables real-time multi-criteria path analysis

Abstract

Computing cost optimal paths in network data is a very important task in many application areas like transportation networks, computer networks or social graphs. In many cases, the cost of an edge can be described by various cost criteria. For example, in a road network possible cost criteria are distance, time, ascent, energy consumption or toll fees. In such a multicriteria network, a route or path skyline query computes the set of all paths having pareto optimal costs, i.e. each result path is optimal for different user preferences. In this paper, we propose a new method for computing route skylines which significantly decreases processing time and memory consumption. Furthermore, our method does not rely on any precomputation or indexing method and thus, it is suitable for dynamically changing edge costs. Our experiments demonstrate that our method outperforms state of the art approaches and allows highly efficient path skyline computation without any preprocessing.