Probabilistic Spatial Queries on Existentially Uncertain Data

• Published in: Advances in Spatial and Temporal Databases pp. 400 - 417
• Main Authors: Dai, Xiangyuan, Yiu, Man Lung, Mamoulis, Nikos, Tao, Yufei, Vaitis, Michail
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Computer science; control theory; systems; Exact sciences and technology; Information systems. Data bases; Memory organisation. Data processing; Near Neighbor; Range Query; Skyline Query; Software; Spatial Query; Uncertain Data; Computational geometry; Nearest neighbour; Uncertainty; Probabilistic approach; Database query; Hierarchical classification; Threshold detection; Information retrieval; Modeling; Search algorithm; Object-oriented databases; Spatial database
• ISBN: 3540281274; 9783540281276
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11535331_23

We study the problem of answering spatial queries in databases where objects exist with some uncertainty and they are associated with an existential probability. The goal of a thresholding probabilistic spatial query is to retrieve the objects that qualify the spatial predicates with probability that exceeds a threshold. Accordingly, a ranking probabilistic spatial query selects the objects with the highest probabilities to qualify the spatial predicates. We propose adaptations of spatial access methods and search algorithms for probabilistic versions of range queries and nearest neighbors and conduct an extensive experimental study, which evaluates the effectiveness of proposed solutions.