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Range Search on Multidimensional Uncertain Data

By Yufei Tao, Xiaokui Xiao and Reynold Cheng

Abstract

In an uncertain database, every object o is associated with a probability density function, which describes the likelihood that o appears at each position in a multidimensional workspace. This article studies two types of range retrieval fundamental to many analytical tasks. Specifically, a nonfuzzy query returns all the objects that appear in a search region rq with at least a certain probability tq. On the other hand, given an uncertain object q, fuzzy search retrieves the set of objects that are within distance εq from q with no less than probability tq. The core of our methodology is a novel concept of “probabilistically constrained rectangle”, which permits effective pruning/validation of nonqualifying/qualifying data. We develop a new index structure called the U-tree for minimizing the query overhead. Our algorithmic findings are accompanied with a thorough theoretical analysis, which reveals valuable insight into the problem characteristics, and mathematically confirms the efficiency of our solutions. We verify the effectiveness of the proposed techniques with extensiv

Topics: Categories and Subject Descriptors, H.2.2 [Database Management, Physical Design—Access Methods, H.3.3 [Information Storage and Retrieval, Information Search and Retrieval General Terms, Algorithms, Experimentation Additional Key Words and Phrases, Uncertain databases, range search ACM Reference Format
Year: 2008
OAI identifier: oai:CiteSeerX.psu:10.1.1.134.2878
Provided by: CiteSeerX
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