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Efficient and Progressive Algorithms for Distributed Skyline Queries over Uncertain Data

机译:不确定数据分布式天际查询的高效渐进算法

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摘要

The skyline operator has received considerable attention from the database community, due to its importance in many applications including multicriteria 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, 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, the effectiveness and the progressiveness of our algorithms with both the synthetic and real data sets.
机译:由于Skyline运算符在许多应用程序中的重要性(包括多准则决策,偏好应答等)中,因此引起了数据库社区的极大关注。在许多固有存在不确定性数据的应用中,即,从分布位置的不同来源收集的数据通常具有不精确的测量值,因此表现出某种不确定性。考虑到与通过Internet共享和通信大量分布式数据相关的网络延迟和经济成本,此方案中的一个重要问题是以最小的通信成本从所有分布式本地站点检索全局天际线元组。基于对集中式不确定数据的概率天际线查询的著名表示法,本文提出了对不确定数据的分布式天际线查询的表示法。此外,提出了两种通信和计算效率高的算法来从分布式本地站点检索合格的天际线。已经进行了广泛的实验,以通过综合和真实数据集验证我们算法的效率,有效性和渐进性。

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