An Efficient Skyline Model for Processing Multiple Continuous Skyline Queries Over Uncertain Data Stream

Skyline queries based on pareto dominance have gained significant attention for their ability to effectively identify interesting objects from large multi-dimensional datasets. They are particularly useful in applications that involve multi-criteria decision support. Recently, several techniques have been proposed for processing continuous skyline queries over uncertain data streams, however, they mainly focus on uncertainty that results from objects having many instances. Uncertainty owing to objects having range values wherein the exact values of the objects are not known at the point of processing has not been extensively explored. Moreover, identical objects may recur in the stream at different times while continuous skyline queries submitted by different users can overlap within the same time frame. Thus, it would be inefficient to repeatedly process the same segment of the data stream and the same objects in processing these multiple queries. We seek to overcome the aforementioned problems by introducing an efficient skyline model named MCSQ-UDS, for computing multiple continuous skyline queries over uncertain data stream. Several extensive experiments were conducted on synthetic and real datasets with different parameter settings. The results show that the proposed model outperforms the baseline techniques with regard to number of pairwise comparisons and execution time.