Top k Favorite Probabilistic Products Queries
Top k Favorite Probabilistic Products Queries
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最喜欢的 k 个概率产品查询
DOI：
10.1109/tkde.2016.2584606
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发表时间：
2016
影响因子：
8.9
通讯作者：
Li Keqin
中科院分区：
文献类型：
--
作者：
Zhou Xu;Li Kenli;Xiao Guoqing;Zhou Yantao;Li Keqin
随着经济的发展，产品的内容显著丰富，不确定性已成为其内在的品质。概率动态天际线查询是客户根据自己的偏好选择产品的一个有力工具。然而，这种查询有几个局限性：它需要指定的概率阈值，报告不理想的结果和忽视重要的结果;它只关注具有大的动态天际线概率的对象;此外，结果不稳定。为了解决这个问题，在本文中，我们制定了一个不确定的动态天际线（UDS）查询的概率产品集。此外，我们提出了有效的修剪策略的UDS查询，并将它们集成到有效的算法。此外，一种新的查询类型，即最喜爱的概率产品（TFPP）查询，提出。TFPP查询用于选择能够最大程度满足客户集需求的产品。为了解决TFPP查询，我们提出了一个TFPP算法及其有效的并行化。大量的实验与各种实验设置说明了我们提出的算法的效率和有效性。
With the development of the economy, products are significantly enriched, and uncertainty has been their inherent quality. The probabilistic dynamic skyline (PDS) query is a powerful tool for customers to use in selecting products according to their preferences. However, this query suffers several limitations: it requires the specification of a probabilistic threshold, which reports undesirable results and disregards important results; it only focuses on the objects that have large dynamic skyline probabilities; and, additionally, the results are not stable. To address this concern, in this paper, we formulate an uncertain dynamic skyline (UDS) query over a probabilistic product set. Furthermore, we propose effective pruning strategies for the UDS query, and integrate them into effective algorithms. In addition, a novel query type, namely the topfavorite probabilistic products (TFPP) query, is presented. The TFPP query is utilized to selectproducts which can meet the needs of a customer set at the maximum level. To tackle the TFPP query, we propose a TFPP algorithm and its efficient parallelization. Extensive experiments with a variety of experimental settings illustrate the efficiency and effectiveness of our proposed algorithms.