Indexing Multi-Dimensional Uncertain Data with Arbitrary Probability Density Functions

In an “uncertain database”, an object o is associated with a multi-dimensional probability density function (pdf), which describes the likelihood that o appears at each position in the data space. A fundamental operation is the “probabilistic range search” which, given a value pq and a rectangular area rq, retrieves the objects that appear in rq with probabilities at least pq. In this paper, we propose the U-tree, an access method designed to optimize both the I/O and CPU time of range retrieval on multi-dimensional imprecise data. The new structure is fully dynamic (i.e., objects can be incrementally inserted/deleted in any order), and does not place any constraints on the data pdfs. We verify the query and update efficiency of U-trees with extensive experiments.

• Type: Conference
• Year: 2005
• Where: VLDB
• Authors: Yufei Tao, Reynold Cheng, Xiaokui Xiao, Wang Kay Ngai, Ben Kao, Sunil Prabhakar
• Keywords: Database, Multi-dimensional Probability Density, Probabilistic Range Search, Rectangular Area Rq, VLDB 2005