边缘计算环境下不确定数据流上的概率Skyline查询处理
Probabilistic Skyline Query Processing over Uncertain Data Streams in Edge Computing Environments
摘要：
随着技术的进步，我们生活中产生的数据越来越快，各种应用需要处理的数据量变得极其巨大。因此，我们需要投入更多的精力来分析数据并提取有价值的信息。云计算曾经是解决大量数据分析问题的好技术。然而，在物联网（IoT）普及的时代，将传感数据传回云端进行集中数据分析将消耗大量的无线通信和网络传输成本。为了解决上述问题，边缘计算已经成为一种很有前途的解决方案。在本文中，我们提出了一个新的算法，在边缘计算环境中处理不确定数据流上的概率skyline查询。我们使用第二个天际线集的概念来过滤不太可能是天际线结果的数据。此外，边缘服务器只发送更新云服务器上的全局分析结果所需的信息，这将大大减少通过网络传输的数据量。实验结果表明，该方法在二维数据上的响应时间比暴力方法减少了50%以上，在高维数据上的处理速度也保持了领先水平.
英文摘要：
With the advancement of technology, the data generated in our lives is getting faster and faster, and the amount of data that various applications need to process becomes extremely huge. Therefore, we need to put more effort into analyzing data and extracting valuable information. Cloud computing used to be a good technology to solve a large number of data analysis problems. However, in the era of the popularity of the Internet of Things (IoT), transmitting sensing data back to the cloud for centralized data analysis will consume a lot of wireless communication and network transmission costs. To solve the above problems, edge computing has become a promising solution. In this paper, we propose a new algorithm for processing probabilistic skyline queries over uncertain data streams in an edge computing environment. We use the concept of a second skyline set to filter data that is unlikely to be the result of the skyline. Besides, the edge server only sends the information needed to update the global analysis results on the cloud server, which will greatly reduce the amount of data transmitted over the network. The results show that our proposed method not only reduces the response time by more than 50% compared with the brute force method on two-dimensional data but also maintains the leading processing speed on high-dimensional data.
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