Probabilistic Skyline Query Processing over Uncertain Data Streams in Edge Computing Environments

TL;DR

This paper introduces a novel algorithm for probabilistic skyline query processing over uncertain data streams in edge computing, significantly reducing response time and network transmission costs compared to traditional methods.

Contribution

It proposes a second skyline set concept and an efficient filtering algorithm tailored for edge environments, enhancing data processing speed and reducing communication overhead.

Findings

Reduces response time by over 50% on 2D data

Maintains high processing speed on high-dimensional data

Effectively minimizes network data transmission

Abstract

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.