Threshold-based Probabilistic Top-k Dominating Querie 摘要粗译
Abstract Recently, due to intrinsic characteristics in many underlying data sets, a number of probabilistic queries on uncertain data have been investigated. Top-k dominating queries are very important in many applications including decision making in a multidimen-
sional space. In this paper, we study the problem of efficiently computing top-k dominating queries on uncertain data. We frst formally deˉne the problem. Then, we develop an e±cient, threshold-based algorithm to compute the exact solution. To overcome some inherent computational deˉciency in an exact computation, we develop an e±cient randomized algorithm with an accuracy guar-antee. Our extensive experiments demonstrate that both algorithms are quite e±cient, while the randomized algorithm is quite scalable against data set sizes, object areas, k values, etc. The randomized algorithm is also highly accurate in practice.
Keywords Uncertain Objects Top k Dominating Relation
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基于阈值的概率Top-k控制查询
摘要：近期，因为一些代表性数据集固有特性，我们进行了许多在不确定数据下的概率查询方面的研究。Top-k控制查询在很多应用中是非常重要的，包括在多维空间中的判断。本文中，我们研究了在不确定数据下的Top-k控制查询有效地计算问题。首先形成问题定义，然后，我们研究了一个有效的、基于阈值的算法来计算精确解。为了解决一些在精确计算方面的固有问题，我们用开发了一个有效的精确的随机算法。我�**惴旱氖笛橹っ鳎庑┧惴ㄊ欠浅Ｓ行У模彼婊惴ㄔ谑菁笮。韵笄颍琄值等方面具有高扩展性。这个随机算法在实践中也是高精确的。
关键词：非确定性目标，Top-k 控制关系
