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The skyline of a <i>d</i>-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 database community, especially for progressive methods that can quickly return the initial results without reading the entire database. All the existing(More)
Given a set of objects <i>P</i> and a query point <i>q,</i> a <i>k</i> nearest neighbor (<i>k</i>-NN) query retrieves the <i>k</i> objects in <i>P</i> that lie closest to <i>q.</i> Even though the problem is well-studied for static datasets, the traditional methods do not extend to highly dynamic environments where multiple continuous queries require(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 in practice is the TPR-tree. In this paper we first perform an analysis to determine the factors that affect the performance of predictive queries and show that(More)
— The increasing trend of embedding positioning capabilities (e.g., GPS) in mobile devices facilitates the widespread use of Location Based Services. For such applications to succeed , privacy and confidentiality are essential. Existing privacy-enhancing techniques rely on encryption to safeguard communication channels, and on pseudonyms to protect user(More)
In this paper we propose an approach that enables mobile clients to determine the validity of previous queries based on their current locations. In order to make this possible, the server returns in addition to the query result, a <i>validity region</i> around the client's location within which the result remains the same. We focus on two of the most common(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 at a specific timestamp. Interval queries include multiple (usually consecutive) timestamps. Although several indexes have been developed for either type,(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 network and Euclidean information, capturing pragmatic constraints. Based on this architecture, we develop a Euclidean restriction and a network expansion(More)
Given two sets of points P and Q, a group nearest neighbor (GNN) query retrieves the point(s) of P with the smallest sum of distances to all points in Q. Consider, for instance, three users at locations q 1 , q 2 and q 3 that want to find a meeting point (e.g., a restaurant); the corresponding query returns the data point p that minimizes the sum of(More)