An Effective Probabilistic Skyline Query Process on Uncertain Data Streams
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- 作者：Chuan-Ming Liu ; cmliu@ntut.edu.tw" class="auth_mail" title="E-mail the corresponding author ; Syuan-Wei Tang
- 关键词：Probabilistic Skyline ; Uncertain Data ; Data Streams ; Query Processing
- 刊名：Procedia Computer Science
- 出版年：2015
- 出版时间：2015
- 年：2015
- 卷：63
- 期：Complete
- 页码：40-47
- 全文大小：527 K
文摘
With the evolution of technology, the ways to acquire data and the applications of data are more diverse. As data volume con- tinuously grows, the data quality may not be high as usual. The data can be defected, imprecise or inaccurate due to the process of data acquiring. Recently, the skyline query is widely used in data analysis to derive the results that meets more than one spe- cific condition simultaneously. For example, the forest monitoring system, which collects the temperature and humidity of the surrounding environment with sensors, to monitor the disasters. Using the skyline query, the zones of potential fire hazards can be found in time, where the temperature is high and the humidity is low. In the mentioned application, the derived data change with time. We refer to such data as data streams. The constant change and uncertainty of data make the query process difficult and need more computations. Thus, how to have an effective skyline query process in terms of time and space over uncertain data streams becomes crucial. In this paper, we discuss this problem and propose an effective approach, Efficient Probabilistic Skyline Update (EPSU), using a new data structure by augmenting the R-tree structure. The relevant algorithms are analyzed and discussed. Last, we perform the simulated experiments extensively with synthetic data to validate the EPSU approach. The results show that EPSU can effectively compute the probabilistic skyline query in terms of the time and space and outperforms the existing ones.