Quality-Aware Probing of Uncertain Data with Resource Constraints
Quality-Aware Probing of Uncertain Data with Resource Constraints
复制标题
对资源约束下的不确定数据进行质量感知探测
DOI：
10.1007/978-3-540-69497-7_31
复制
发表时间：
2008
期刊：
影响因子：
--
通讯作者：
Reynold Cheng
中科院分区：
文献类型：
--
作者：
Jinchuan Chen;Reynold Cheng
在传感器网络监控和基于位置的服务等应用中，由于网络带宽和电池电量有限，系统无法始终从外部环境获取准确和新鲜的数据。为了在这些环境中捕获数据错误，最近的研究提出了将不确定性建模为概率分布函数（pdf），以及概率查询的概念，它提供了对答案正确性的统计保证。在本文中，我们提出了一个基于熵的度量来量化由于数据的不确定性的概率查询答案的歧义程度。在此基础上，提出了一种提高查询答案质量的方法.该方法的主要思想是从一组选定的传感设备中获取（或探测）数据，以减少数据的不确定性并提高查询答案的质量。给定一个查询被分配了有限数量的探测资源，我们调查如何查询答案的质量可以达到最佳的改善。为了提高我们的解决方案的效率，我们进一步提出了实现接近最佳质量改进的方法。我们推广我们的解决方案来处理多个查询。在一个现实的数据集进行实验模拟，以验证我们的方法。
In applications like sensor network monitoring and location-based services, due to limited network bandwidth and battery power, a system cannot always acquire accurate and fresh data from the external environment. To capture data errors in these environments, recent researches have proposed to model uncertainty as a probability distribution function (pdf), as well as the notion of probabilistic queries, which provide statistical guarantees on answer correctness. In this paper, we present an entropy-based metric to quantify the degree of ambiguity of probabilistic query answers due to data uncertainty. Based on this metric, we develop a new method to improve the query answer quality. The main idea of this method is to acquire (or probe) data from a selected set of sensing devices, in order to reduce data uncertainty and improve the quality of a query answer. Given that a query is assigned a limited number of probing resources, we investigate how the quality of a query answer can attain an optimal improvement. To improve the efficiency of our solution, we further present heuristics which achieve near-to-optimal quality improvement. We generalize our solution to handle multiple queries. An experimental simulation over a realistic dataset is performed to validate our approaches.