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Skyline has been proposed as an important operator for multi-criteria decision making , data mining and visualization, and user-preference queries. In this paper, we consider the problem of efficiently computing a Skycube, which consists of skylines of all possible non-empty subsets of a given set of dimensions. While existing skyline computation algorithms(More)
— Existing prediction methods in moving objects databases cannot forecast locations accurately if the query time is far away from the current time. Even for near future prediction, most techniques assume the trajectory of an object's movements can be represented by some mathematical formulas of motion functions based on its recent movements. However, an(More)
The skyline operator is important for multicriteria decision-making applications. Although many recent studies developed efficient methods to compute skyline objects in a given space, none of them considers skylines in multiple subspaces simultaneously. More importantly, the fundamental problem on the <i>semantics</i> of skylines remains open: Why and in(More)
—Peta-scale scientific applications running on High End Computing (HEC) platforms can generate large volumes of data. For high performance storage and in order to be useful to science end users, such data must be organized in its layout, indexed, sorted, and otherwise manipulated for subsequent data presentation, visualization, and detailed analysis. In(More)
Graphs are widely used to model complex data in many applications, such as bioinformatics, chemistry, social networks , pattern recognition, etc. A fundamental and critical query primitive is to efficiently search similar structures in a large collection of graphs. This paper studies the graph similarity queries with edit distance constraints. Existing(More)
In this paper, we study the problem of projected outlier detection in high dimensional data streams and propose a new technique, called Stream Projected Ouliter deTector (SPOT), to identify outliers embedded in subspaces. Sparse Subspace Template (SST), a set of subspaces obtained by unsupervised and/or supervised learning processes, is constructed in SPOT(More)
Significant challenges exist for achieving peak or even consistent levels of performance when using IO systems at scale. They stem from sharing IO system resources across the processes of single largescale applications and/or multiple simultaneous programs causing internal and external interference, which in turn, causes substantial reductions in IO(More)