Discovering the Most Influential Sites over Uncertain Data: A Rank-Based Approach
Discovering the Most Influential Sites over Uncertain Data: A Rank-Based Approach
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DOI：
10.1109/tkde.2011.121
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发表时间：
2012-12
影响因子：
8.9
通讯作者：
Kai Zheng;Zi Huang;Aoying Zhou;Xiaofang Zhou
中科院分区：
文献类型：
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作者：
Kai Zheng;Zi Huang;Aoying Zhou;Xiaofang Zhou
随着不确定数据在位置服务、传感器监控、生物信息管理等重要应用中的应用日益增多，不确定性感知查询处理近年来受到数据库界的广泛关注。本文研究了不确定数据库环境下的一种新的查询类型，即不确定top-k影响站点查询（UTkIS query），它可以应用于市场分析和移动的服务等广泛的应用领域.由于它不是那么简单，以精确定义的语义top-k查询与不确定的数据，在本文中，我们介绍了一种新的和更直观的配方的查询的基础上，预期的排名语义。为了解决效率问题所造成的可能世界探索，我们提出了有效的修剪规则和分治的范例，使候选人的数量以及可能的世界被认为是可以显着减少。最后，我们在真实的数据集上进行了大量的实验，以验证本文提出的新方法的有效性和效率。
With the rapidly increasing availability of uncertain data in many important applications such as location-based services, sensor monitoring, and biological information management systems, uncertainty-aware query processing has received a significant amount of research effort from the database community in recent years. In this paper, we investigate a new type of query in the context of uncertain databases, namely uncertain top-k influential sites query (UTkIS query for short), which can be applied in a wide range of application areas such as marketing analysis and mobile services. Since it is not so straightforward to precisely define the semantics of top-k query with uncertain data, in this paper we introduce a novel and more intuitive formulation of the query on the basis of expected rank semantics. To address the efficiency issue caused by possible worlds exploration, we propose effective pruning rules and a divide-and-conquer paradigm such that the number of candidates as well as the number of possible worlds to be considered can be significantly reduced. Finally, we conduct extensive experiments on real data sets to verify the effectiveness and efficiency of the new methods proposed in this paper.