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Probabilistic threshold range aggregate query processing over uncertain data

机译:不确定数据的概率阈值范围集合查询处理

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摘要

Uncertainty is inherent in many novel and important applications such as market surveillance, information extraction sensor data analysis, etc. In the recent a few decades, uncertain data has attracted considerable research attention. There are various factors that cause the uncertainty, for instance randomness or incompleteness of data, limitations of equipment and delay or loss in data transfer.A probabilistic threshold range aggregate (PRTA) query retrieves summarized information about the uncertain objects in the database satisfying a range query, with respect to a given probability threshold. This thesis is trying to address and handle this important type of query which there is no previous work studying on.We formulate the problem in both discrete and continuous uncertain data model and develop a novel index structure, asU-tree (aggregate-based sampling-auxiliary U-tree) which not only supports exact query answering but also provides approximate results with accuracy guarantee if efficiency is more concerned. The new asU-tree structure is totally dynamic. Query processing algorithms for both exact answer and approximate answer based on this new index structure are also proposed. An extensive experimental study shows that asU-tree is very efficient and effective over real and synthetic datasets.
机译:不确定性是许多新颖而重要的应用程序所固有的,例如市场监视,信息提取传感器数据分析等。在最近的几十年中,不确定的数据吸引了相当多的研究关注。导致不确定性的因素有很多,例如数据的随机性或不完整性,设备的限制以及数据传输的延迟或丢失。概率阈值范围聚合(PRTA)查询检索有关满足范围的数据库中不确定对象的汇总信息关于给定的概率阈值的查询。本论文试图解决和处理这一重要的查询类型,而这之前没有进行过研究。我们在离散和连续的不确定数据模型中提出问题,并开发了一种新颖的索引结构asU-tree(基于集合的抽样-辅助U树),它不仅支持精确的查询回答,而且还提供近似结果,并在保证效率的情况下保证准确性。新的asU-tree结构是完全动态的。还提出了基于这种新索引结构的精确答案和近似答案的查询处理算法。广泛的实验研究表明,asU-tree在真实和合成数据集上非常有效。

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