Range search on multidimensional uncertain data

• Autores: Yufei Tao, Xiaokui Xiao, Reynold Cheng
• Localización: ACM transactions on database systems, ISSN 0362-5915, Vol. 32, Nº 3, 2007
• Idioma: inglés
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• Resumen

  • 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 extensive experiments