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.Comment: 12 pages, 9 figures, technical repor