A skyline query method over Gaussian model uncertain data streams

Skyline queries over uncertain streams has become a challenge because of the uncertainty and dynamics of data. Uncertain data is usually represented by a multivariate probability density function (PDF). The current skyline queries over uncertain data streams are modeled by discrete PDF. However, the discrete PDF may not suit for many streams, because these streams may be continuously changing. The research of the skyline query on continuous PDF model proposes an efficient skyline query method over Gaussian model uncertain stream (SGMU). Firstly, a dynamic Gaussian modeling (DGM) algorithm is proposed to build the Gaussian model by sampling sliding window in streams. Secondly, a Gauss-tree based skyline query algorithm (GTS) is raised to build a spatial indexing structure for uncertain data stream. Experimental results demonstrate that SGMU not only can model the uncertain objects efficiently to support skyline queries, but also can greatly improve the skyline queries by pruning objects efficiently.