Abstract
An uncertain geo-spatial dataset is a collection of geo-spatial objects that do not represent accurately real-world entities. Each object has a confidence value indicating how likely it is for the object to be correct. Uncertain data can be the result of operations such as imprecise integration, incorrect update or inexact querying. A k-route, over an uncertain geo-spatial dataset, is a path that travels through the geo-spatial objects, starting at a given location and stopping after visiting k correct objects. A k-route is considered shortest if the expected length of the route is less than or equal to the expected length of any other k-route that starts at the given location. This paper introduces the problem of finding a shortest k-route over an uncertain dataset. Since the problem is a generalization of the traveling salesman problem, it is unlikely to have an efficient solution, i.e., there is no polynomial-time algorithm that solves the problem (unless P=NP). Hence, in this work we consider heuristics for the problem. Three methods for computing a short k-route are presented. The three methods are compared analytically and experimentally. For these three methods, experiments on both synthetic and real-world data show the tradeoff between the quality of the result (i.e., the expected length of the returned route) and the efficiency of the computation.
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Safra, E., Kanza, Y., Dolev, N., Sagiv, Y., Doytsher, Y. (2007). Computing a k-Route over Uncertain Geographical Data. In: Papadias, D., Zhang, D., Kollios, G. (eds) Advances in Spatial and Temporal Databases. SSTD 2007. Lecture Notes in Computer Science, vol 4605. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73540-3_16
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DOI: https://doi.org/10.1007/978-3-540-73540-3_16
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