Parallel k-Dominant Skyline Queries over Uncertain Data Streams with Capability Index
Xiaoyong Li, Jun Liu, Kaijun Ren, Xiaoling Li, Xiaoli Ren, Kefeng Deng · 2019
The skyline query over uncertain data streams, as an important aspect of big data analysis, plays a significant role in various domains like financial data analysis, environmental monitoring, and wireless sensor network. However, with the diversity of user query requirements, the traditional skyline query is not practical enough and even cannot meet users' requirements. To address the problem that the number of uncertain skyline queries results is so numerous that cannot offer any practical insights effectively, we propose a dominance-capability based parallel uncertain k-dominant skyline queries method named PKDS in this paper. Firstly, the method defines the k-dominant skyline query problem over uncertain data streams. Secondly, PKDS maps the new arriving items to multiple compute nodes according to the streaming items mapping strategy based on sliding-window partitioning, in order to support the parallel processing for the k-dominant skyline queries over uncertain data streams efficiently. Specifically, an index structure based on the k-dominant capability of streaming items is developed to efficiently manage streaming items, which could greatly improve the k-dominance tests and further the efficiency of parallel k-dominant skyline queries over uncertain data streams. Extensive experimental results demonstrate that, PKDS method not only can reduce the results of skyline queries over high-dimensional streaming items to the scope that could give a better decision support, but also can greatly improve the query efficiency.