Range searching on uncertain data
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 01, 2012
- Source ID
- 10.1145/2344422.2344433
Entities
People
- Ke Yi
- Pankaj Agarwal
- Siu-wing Cheng
Organizations
- Army Research Office
- Division of Computer and Network Systems
- Division of Computing and Communication Foundations
- Division of Information and Intelligent Systems
- Duke University
- Hong Kong University of Science and Technology
- National Institutes of Health
- Research Grants Council, University Grants Committee
- United States Department of Energy