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Efficient and Progressive Algorithms for Distributed Skyline Queries over Uncertain Data

机译:不确定数据的分布式天际查询的高效渐进算法

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The skyline operator has received considerable attention from the database community, due to its importance in many applications including multi-criteria decision making, preference answering, and so forth. In many applications where uncertain data are inherently exist, i.e., data collected from different sources in distributed locations are usually with imprecise measurements, and thus exhibit kind of uncertainty. Taking into account the network delay and economic cost associated with sharing and communicating large amounts of distributed data over an internet, an important problem in this scenario is to retrieve the global skyline tuples from all the distributed local sites with minimum communication cost. Based on the well known notation of the probabilistic skyline query over centralized uncertain data, in this paper, for the first time, we propose the notation of distributed skyline queries over uncertain data. Furthermore, two communication-and computation-efficient algorithms are proposed to retrieve the qualified skylines from distributed local sites. Extensive experiments have been conducted to verify the efficiency and the effectiveness of our algorithms with both the synthetic and real data sets.
机译:由于其在许多应用程序中的重要性,包括多标准决策,偏好应答等,因此天际线操作员已从数据库社区接受了相当大的关注。在许多应用中,不确定数据本质上存在,即,从分布式位置中的不同源收集的数据通常具有不精确的测量,因此表现出种类的不确定性。考虑到与Internet共享和传播大量分布式数据相关的网络延迟和经济成本,这一场景中的一个重要问题是从所有分布式本地站点检索全球地平线元组,最小通信成本。基于众所周知的概率天际线查询集中式不确定数据,本文首次提出了通过不确定数据的分布式天际线查询的符号。此外,建议两个通信和计算有效的算法检索来自分布式本地站点的合格的天际线。已经进行了广泛的实验,以验证我们算法与合成和实数据集的效率和有效性。

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