Nowadays, efficient and effective processing over massive stream data has attracted much attention from the database community, which are useful in many real applications such as sensor data monitoring, network intrusion detection, and so on. In practice, due to the malfunction of sensing devices or imperfect data collection techniques, real-world stream data may often contain missing or incomplete data attributes. In this paper, we will formalize and tackle a novel and important problem, named skyline query over incomplete data stream (Sky-iDS), which retrieves skyline objects (in the presence of missing attributes) with high confidences from incomplete data stream. In order to tackle the Sky-iDS problem, we will design efficient approaches to impute missing attributes of objects from incomplete data stream via differential dependency (DD) rules. We will propose effective pruning strategies to reduce the search space of the Sky-iDS problem, devise cost-model-based index structures to facilitate the data imputation and skyline computation at the same time, and integrate our proposed techniques into an efficient Sky-iDS query answering algorithm. Extensive experiments have been conducted to confirm the efficiency and effectiveness of our Sky-iDS processing approach over both real and synthetic data sets.

中文翻译：

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对不完整数据流的天际线查询

如今，对大量流数据的高效处理已经引起了数据库界的广泛关注，这在许多实际应用中非常有用，例如传感器数据监视，网络入侵检测等。实际上，由于感测设备的故障或数据收集技术的不完善，现实世界中的流数据可能经常包含丢失或不完整的数据属性。在本文中，我们将形式化并解决一个新的重要问题，即针对不完整数据流的名为天际线查询（Sky-iDS），它可以从不完整的数据流中以较高的置信度来检索天际线对象（在缺少属性的情况下）。为了解决Sky-iDS问题，我们将设计有效的方法，通过差分依赖（DD）规则从不完整的数据流中估算对象的缺失属性。我们将提出有效的修剪策略，以减少Sky-iDS问题的搜索空间，设计基于成本模型的索引结构，以同时促进数据估算和天际线计算，并将我们提出的技术集成到有效的Sky-iDS中查询应答算法。已经进行了广泛的实验，以确认我们的Sky-iDS处理方法在真实和合成数据集上的效率和有效性。