The power of geometric duality
BIT - Ellis Horwood series in artificial intelligence
Applications of random sampling in computational geometry, II
Discrete & Computational Geometry - Selected papers from the fourth ACM symposium on computational geometry, Univ. of Illinois, Urbana-Champaign, June 6 8, 1988
Simplex range reporting on a pointer machine
Computational Geometry: Theory and Applications
Reasoning about Uncertainty
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
A dynamic data structure for 3-d convex hulls and 2-d nearest neighbor queries
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Range search on multidimensional uncertain data
ACM Transactions on Database Systems (TODS)
Efficient query evaluation on probabilistic databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Efficient indexing methods for probabilistic threshold queries over uncertain data
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Probabilistic ranked queries in uncertain databases
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Improved bounds and new techniques for Davenport--Schinzel sequences and their generalizations
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Optimal halfspace range reporting in three dimensions
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Probabilistic databases: diamonds in the dirt
Communications of the ACM - Barbara Liskov: ACM's A.M. Turing Award Winner
Processing probabilistic range queries over gaussian-based uncertain data
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
| Hi-index | 0.00 |
Querying uncertain data has emerged as an important problem in data management due to the imprecise nature of many measurement data. In this article, we study answering range queries over uncertain data. Specifically, we are given a collection P of n uncertain points in ℝ, each represented by its one-dimensional probability density function (pdf). The goal is to build a data structure on P such that, given a query interval I and a probability threshold τ, we can quickly report all points of P that lie in I with probability at least τ. We present various structures with linear or near-linear space and (poly)logarithmic query time. Our structures support pdf's that are either histograms or more complex ones such as Gaussian or piecewise algebraic.