The skyline of a d-dimensional dataset contains the points that are not dominated by any other point on all dimensions. Skyline computation has recently received considerable attention in the… (More)
Despite the importance of spatial networks in real-life applications, most of the spatial database literature focuses on Euclidean spaces. In this paper we propose an architecture that integrates… (More)
The skyline of a set of d-dimensional points contains the points that are not dominated by any other point on all dimensions. Skyline computation has recently received considerable attention in the… (More)
The increasing trend of embedding positioning capabilities (for example, GPS) in mobile devices facilitates the widespread use of location-based services. For such applications to succeed, privacy… (More)
Given a point q, a reverse k nearest neighbor (RkNN) query retrieves all the data points that have q as one of their k nearest neighbors. Existing methods for processing such queries have at least… (More)
A continuous nearest neighbor query retrieves the nearest neighbor (NN) of every point on a line segment (e.g., “find all my nearest gas stations during my route from point s to point e”). The result… (More)
A predictive spatio-temporal query retrieves the set of moving objects that will intersect a query window during a future time interval. Currently, the only access method for processing such queries… (More)
Given a set of objects P and a query point q, a k nearest neighbor (k-NN) query retrieves the k objects in P that lie closest to q. Even though the problem is well-studied for static datasets, the… (More)
Among the various types of spatio-temporal queries, the most common ones involve window queries in time. In particular, timestamp (or timeslice) queries retrieve all objects that intersect a window… (More)
Given two spatial datasets <i>P</i> (e.g., facilities) and <i>Q</i> (queries), an <i>aggregate nearest neighbor</i> (ANN) query retrieves the point(s) of <i>P</i> with the smallest aggregate… (More)