CONTINUOUS MULTIQUERIES K-DOMINANT SKYLINE ON ROAD NETWORK

The increasing use of mobile devices makes spatial data worthy of consideration. To get maximum results, users often look for the best from a collection of objects. Among the algorithms that can be used is the skyline query. The algorithm looks for all objects that are not dominated by other objects in all of its attributes. However, data that has many attributes makes the query output a lot of objects so it is less useful for the user. k-dominant skyline queries can be a solution to reduce the output. Among the challenges is the use of skyline queries with spatial data and the many user preferences in finding the best object. This study proposes IKSR: the k-dominant skyline query algorithm that works in a road network environment and can process many queries that have the same subspace in one processing. This algorithm combines queries that operate on the same subspace and set of objects with different k values by computing from the smallest to the largest k. Optimization occurs when some data for larger k are precomputed when calculating the result for the smallest k so the Voronoi cell computing is not repeated. Testing is done by comparing with the naïve algorithm without precomputation. IKSR algorithm can speed up computing time two to three times compared to naïve algorithm

ParetoPrep: Fast computation of Path Skylines Queries

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