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

Visit Places on YourWay: A Skyline Approach in Time-Dependent Networks

Many people take the same path every day, such as taking a specific autobahn to get home from work. However, one needs to frequently divert from this path, e.g., to visit a Point of Interest (POI) from a category like the category of restaurants or ATMs. Usually, people want to minimize not only their overall travel cost but also their detour cost, i.e., one wants to return to the known path as fast as possible. Finding such a POI minimizing both costs efficiently is highly challenging in case one considers time-dependent road networks which are the case in real-world scenarios. For such road networks time decency means the time a user needs to traverse a road, heavily depends on the user’s arrival time on that road. Prior works have several limitations, such as assuming that travel costs are coming from a metric space and do not change over time. Both assumptions hardly match real-world requirements: Just think of traffic jams at the rush hour. To overcome these limitations, we study how to solve this problem considering time-dependent road networks relying on linear skylines. Our main contribution is an efficient algorithm called STACY to find all non-dominated paths. A large-scale empirical evaluation on real-world data reveals that STACY is accurate, efficient and effective in real-world settings

In-Route Skyline Querying for Location-Based Services

Abstract. With the emergence of an infrastructure for location-aware mobile services, the processing of advanced, location-based queries that are expected to underlie such services is gaining in relevance. While much work has assumed that users move in Euclidean space, this paper assumes that movement is constrained to a road network and that points of interest can be reached via the network. More specifically, the paper assumes that the queries are issued by users moving along routes towards destinations. The paper defines in-route nearestneighbor skyline queries in this setting and considers their efficient computation. The queries take into account several spatial preferences, and they intuitively return a set of most interesting results for each result returned by the corresponding non-skyline queries. The paper also covers a performance study of the proposed techniques based on real point-of-interest and road network data.