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

Pareto dominance-based skyline queries have drawn a lot of attention for their ability to extract a selection of interesting objects from potentially large multidimensional datasets. These queries are particularly useful for multi-criteria decision support applications. A few techniques have recently been put forth for handling continuous skyline queries over uncertain data streams; however, these techniques primarily address uncertainty resulting 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. Furthermore, it is evident that while identical objects may reappear in the stream at a different time, multiple continuous skyline queries submitted by different users may fall within a similar time frame. Thus, it would be imprudent to process the same portion of the data stream and the same objects repeatedly in order to satisfy these queries. This study attempts to resolve the aforementioned problems by proposing an efficient skyline model in computing multiple continuous skyline queries over uncertain data stream. This research is significant as it contributes in cost reduction and offers faster and better decision making.