STOC '91 Proceedings of the twenty-third annual ACM symposium on Theory of computing
Online computation and competitive analysis
Online computation and competitive analysis
Data structures for mobile data
SODA '97 Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms
Computing the median with uncertainty
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
On computing functions with uncertainty
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Offering a Precision-Performance Tradeoff for Aggregation Queries over Replicated Data
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Computing Shortest Paths with Uncertainty
STACS '03 Proceedings of the 20th Annual Symposium on Theoretical Aspects of Computer Science
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We consider the problems of computing maximal points and the convex hull of a set of points in 2D, when the points are "in motion." We assume that the point locations (or trajectories) are not known precisely and determining these values exactly is feasible, but expensive. In our model, the algorithm only knows areas within which each of the input points lie, and is required to identify the maximal points or points on the convex hull correctly by updating some points (i.e. determining exactly their location). We compare the number of points updated by the algorithm on a given instance to the minimum number of points that must be updated by an omniscient adversary in order to provably compute the answer correctly. We give algorithms for both of the above problems that always update at most 3 times as many points as the adversary, and show that this is the best possible. Our model is similar to that of [5,2].