Efficient continuous skyline computation

In a number of emerging streaming applications, the data values that are produced have an associated time interval for which they are valid. A useful computation over such streaming data is to produce a continuous and valid skyline summary. Previous work on skyline algorithms have only focused on evaluating skylines over static data sets, and there are no known algorithms for skyline computation in the continuous setting. In this paper, we introduce the continuous time-interval skyline operator, which continuously computes the current skyline over a data stream. We present a new algorithm called LookOut for evaluating such queries efficiently, and empirically demonstrate the scalability of this algorithm. In addition, we also examine the effect of the underlying spatial index structure when evaluating skylines. Whereas previous work on skyline computations have only considered using the R^∗-tree index structure, we show that for skyline computations using an underlying quadtree has significant performance benefits over an R^∗-tree index.