Abstract-Research on spatial network databases has so far considered that there is a single cost value associated with each road segment of the network. In most real-world situations, however, there may exist multiple cost types involved in transportation decision making. For example, the different costs of a road segment could be its Euclidean length, the driving time, the walking time, possible toll fee, etc. The relative significance of these cost types may vary from user to user. In this paper we consider such multi-cost transportation networks (MCN), where each edge (road segment) is associated with multiple cost values. We formulate skyline and top-k queries in MCNs and design algorithms for their efficient processing. Our solutions have two important properties in preference-based querying; the skyline methods are progressive and the top-k ones are incremental. The performance of our techniques is evaluated with experiments on a real road network.
In REF , the authors introduce preference queries in such multi-cost networks.
Preference queries in large multi-cost transportation networks
2010 IEEE 26th International Conference on Data Engineering (ICDE 2010)
2010 IEEE 26th International Conference on Data Engineering (ICDE 2010)
Computer Science