Abstract
Geometric structures such as the convex hull, Delaunay triangulation, or minimum spanning tree (MST) are fundamental tools for reasoning about multidimensional data. What happens to these structures when the underlying data points are known with only partial certainty? For instance, what is the expected cost of the MST of a set of points, each known to be alive with some probability? Or, in a set of uncertain points, how likely is it that the closest pair is within distance L? This talk explores the effects of data uncertainty on the complexity of basic geometric problems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Suri, S. (2013). Geometric Computing over Uncertain Data. In: Bar-Noy, A., Halldórsson, M.M. (eds) Algorithms for Sensor Systems. ALGOSENSORS 2012. Lecture Notes in Computer Science, vol 7718. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36092-3_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-36092-3_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36091-6
Online ISBN: 978-3-642-36092-3
eBook Packages: Computer ScienceComputer Science (R0)